text
stringlengths 128
100k
| source
stringlengths 31
152
|
|---|---|
Computer programming or coding is the composition of sequences of instructions, called programs, that computers can follow to perform tasks. It involves designing and implementing algorithms, step-by-step specifications of procedures, by writing code in one or more programming languages. Programmers typically use high-level programming languages that are more easily intelligible to humans than machine code, which is directly executed by the central processing unit. Proficient programming usually requires expertise in several different subjects, including knowledge of the application domain, details of programming languages and generic code libraries, specialized algorithms, and formal logic.
Auxiliary tasks accompanying and related to programming include analyzing requirements, testing, debugging (investigating and fixing problems), implementation of build systems, and management of derived artifacts, such as programs' machine code. While these are sometimes considered programming, often the term software development is used for this larger overall process – with the terms programming, implementation, and coding reserved for the writing and editing of code per se. Sometimes software development is known as software engineering, especially when it employs formal methods or follows an engineering design process.
== History ==
Programmable devices have existed for centuries. As early as the 9th century, a programmable music sequencer was invented by the Persian Banu Musa brothers, who described an automated mechanical flute player in the Book of Ingenious Devices. In 1206, the Arab engineer Al-Jazari invented a programmable drum machine where a musical mechanical automaton could be made to play different rhythms and drum patterns, via pegs and cams. In 1801, the Jacquard loom could produce entirely different weaves by changing the "program" – a series of pasteboard cards with holes punched in them.
Code-breaking algorithms have also existed for centuries. In the 9th century, the Arab mathematician Al-Kindi described a cryptographic algorithm for deciphering encrypted code, in A Manuscript on Deciphering Cryptographic Messages. He gave the first description of cryptanalysis by frequency analysis, the earliest code-breaking algorithm.
The first computer program is generally dated to 1843 when mathematician Ada Lovelace published an algorithm to calculate a sequence of Bernoulli numbers, intended to be carried out by Charles Babbage's Analytical Engine. The algorithm, which was conveyed through notes on a translation of Luigi Federico Menabrea's paper on the analytical engine was mainly conceived by Lovelace as can be discerned through her correspondence with Babbage. However, Charles Babbage himself had written a program for the AE in 1837. Lovelace was also the first to see a broader application for the analytical engine beyond mathematical calculations.
In the 1880s, Herman Hollerith invented the concept of storing data in machine-readable form. Later a control panel (plug board) added to his 1906 Type I Tabulator allowed it to be programmed for different jobs, and by the late 1940s, unit record equipment such as the IBM 602 and IBM 604, were programmed by control panels in a similar way, as were the first electronic computers. However, with the concept of the stored-program computer introduced in 1949, both programs and data were stored and manipulated in the same way in computer memory.
=== Machine language ===
Machine code was the language of early programs, written in the instruction set of the particular machine, often in binary notation. Assembly languages were soon developed that let the programmer specify instructions in a text format (e.g., ADD X, TOTAL), with abbreviations for each operation code and meaningful names for specifying addresses. However, because an assembly language is little more than a different notation for a machine language, two machines with different instruction sets also have different assembly languages.
=== Compiler languages ===
High-level languages made the process of developing a program simpler and more understandable, and less bound to the underlying hardware.
The first compiler related tool, the A-0 System, was developed in 1952 by Grace Hopper, who also coined the term 'compiler'. FORTRAN, the first widely used high-level language to have a functional implementation, came out in 1957, and many other languages were soon developed—in particular, COBOL aimed at commercial data processing, and Lisp for computer research.
These compiled languages allow the programmer to write programs in terms that are syntactically richer, and more capable of abstracting the code, making it easy to target varying machine instruction sets via compilation declarations and heuristics. Compilers harnessed the power of computers to make programming easier by allowing programmers to specify calculations by entering a formula using infix notation.
=== Source code entry ===
Programs were mostly entered using punched cards or paper tape. By the late 1960s, data storage devices and computer terminals became inexpensive enough that programs could be created by typing directly into the computers. Text editors were also developed that allowed changes and corrections to be made much more easily than with punched cards.
== Modern programming ==
=== Quality requirements ===
Whatever the approach to development may be, the final program must satisfy some fundamental properties. The following properties are among the most important:
Reliability: how often the results of a program are correct. This depends on conceptual correctness of algorithms and minimization of programming mistakes, such as mistakes in resource management (e.g., buffer overflows and race conditions) and logic errors (such as division by zero or off-by-one errors).
Robustness: how well a program anticipates problems due to errors (not bugs). This includes situations such as incorrect, inappropriate or corrupt data, unavailability of needed resources such as memory, operating system services, and network connections, user error, and unexpected power outages.
Usability: the ergonomics of a program: the ease with which a person can use the program for its intended purpose or in some cases even unanticipated purposes. Such issues can make or break its success even regardless of other issues. This involves a wide range of textual, graphical, and sometimes hardware elements that improve the clarity, intuitiveness, cohesiveness, and completeness of a program's user interface.
Portability: the range of computer hardware and operating system platforms on which the source code of a program can be compiled/interpreted and run. This depends on differences in the programming facilities provided by the different platforms, including hardware and operating system resources, expected behavior of the hardware and operating system, and availability of platform-specific compilers (and sometimes libraries) for the language of the source code.
Maintainability: the ease with which a program can be modified by its present or future developers in order to make improvements or to customize, fix bugs and security holes, or adapt it to new environments. Good practices during initial development make the difference in this regard. This quality may not be directly apparent to the end user but it can significantly affect the fate of a program over the long term.
Efficiency/performance: Measure of system resources a program consumes (processor time, memory space, slow devices such as disks, network bandwidth and to some extent even user interaction): the less, the better. This also includes careful management of resources, for example cleaning up temporary files and eliminating memory leaks. This is often discussed under the shadow of a chosen programming language. Although the language certainly affects performance, even slower languages, such as Python, can execute programs instantly from a human perspective. Speed, resource usage, and performance are important for programs that bottleneck the system, but efficient use of programmer time is also important and is related to cost: more hardware may be cheaper.
Using automated tests and fitness functions can help to maintain some of the aforementioned attributes.
=== Readability of source code ===
In computer programming, readability refers to the ease with which a human reader can comprehend the purpose, control flow, and operation of source code. It affects the aspects of quality above, including portability, usability and most importantly maintainability.
Readability is important because programmers spend the majority of their time reading, trying to understand, reusing, and modifying existing source code, rather than writing new source code. Unreadable code often leads to bugs, inefficiencies, and duplicated code. A study found that a few simple readability transformations made code shorter and drastically reduced the time to understand it.
Following a consistent programming style often helps readability. However, readability is more than just programming style. Many factors, having little or nothing to do with the ability of the computer to efficiently compile and execute the code, contribute to readability. Some of these factors include:
Different indent styles (whitespace)
Comments
Decomposition
Naming conventions for objects (such as variables, classes, functions, procedures, etc.)
The presentation aspects of this (such as indents, line breaks, color highlighting, and so on) are often handled by the source code editor, but the content aspects reflect the programmer's talent and skills.
Various visual programming languages have also been developed with the intent to resolve readability concerns by adopting non-traditional approaches to code structure and display. Integrated development environments (IDEs) aim to integrate all such help. Techniques like Code refactoring can enhance readability.
=== Algorithmic complexity ===
The academic field and the engineering practice of computer programming are concerned with discovering and implementing the most efficient algorithms for a given class of problems. For this purpose, algorithms are classified into orders using Big O notation, which expresses resource use—such as execution time or memory consumption—in terms of the size of an input. Expert programmers are familiar with a variety of well-established algorithms and their respective complexities and use this knowledge to choose algorithms that are best suited to the circumstances.
=== Methodologies ===
The first step in most formal software development processes is requirements analysis, followed by testing to determine value modeling, implementation, and failure elimination (debugging). There exist a lot of different approaches for each of those tasks. One approach popular for requirements analysis is Use Case analysis. Many programmers use forms of Agile software development where the various stages of formal software development are more integrated together into short cycles that take a few weeks rather than years. There are many approaches to the Software development process.
Popular modeling techniques include Object-Oriented Analysis and Design (OOAD) and Model-Driven Architecture (MDA). The Unified Modeling Language (UML) is a notation used for both the OOAD and MDA.
A similar technique used for database design is Entity-Relationship Modeling (ER Modeling).
Implementation techniques include imperative languages (object-oriented or procedural), functional languages, and logic programming languages.
=== Measuring language usage ===
It is very difficult to determine what are the most popular modern programming languages. Methods of measuring programming language popularity include: counting the number of job advertisements that mention the language, the number of books sold and courses teaching the language (this overestimates the importance of newer languages), and estimates of the number of existing lines of code written in the language (this underestimates the number of users of business languages such as COBOL).
Some languages are very popular for particular kinds of applications, while some languages are regularly used to write many different kinds of applications. For example, COBOL is still strong in corporate data centers often on large mainframe computers, Fortran in engineering applications, scripting languages in Web development, and C in embedded software. Many applications use a mix of several languages in their construction and use. New languages are generally designed around the syntax of a prior language with new functionality added, (for example C++ adds object-orientation to C, and Java adds memory management and bytecode to C++, but as a result, loses efficiency and the ability for low-level manipulation).
=== Debugging ===
Debugging is a very important task in the software development process since having defects in a program can have significant consequences for its users. Some languages are more prone to some kinds of faults because their specification does not require compilers to perform as much checking as other languages. Use of a static code analysis tool can help detect some possible problems. Normally the first step in debugging is to attempt to reproduce the problem. This can be a non-trivial task, for example as with parallel processes or some unusual software bugs. Also, specific user environment and usage history can make it difficult to reproduce the problem.
After the bug is reproduced, the input of the program may need to be simplified to make it easier to debug. For example, when a bug in a compiler can make it crash when parsing some large source file, a simplification of the test case that results in only few lines from the original source file can be sufficient to reproduce the same crash. Trial-and-error/divide-and-conquer is needed: the programmer will try to remove some parts of the original test case and check if the problem still exists. When debugging the problem in a GUI, the programmer can try to skip some user interaction from the original problem description and check if the remaining actions are sufficient for bugs to appear. Scripting and breakpointing are also part of this process.
Debugging is often done with IDEs. Standalone debuggers like GDB are also used, and these often provide less of a visual environment, usually using a command line. Some text editors such as Emacs allow GDB to be invoked through them, to provide a visual environment.
== Programming languages ==
Different programming languages support different styles of programming (called programming paradigms). The choice of language used is subject to many considerations, such as company policy, suitability to task, availability of third-party packages, or individual preference. Ideally, the programming language best suited for the task at hand will be selected. Trade-offs from this ideal involve finding enough programmers who know the language to build a team, the availability of compilers for that language, and the efficiency with which programs written in a given language execute. Languages form an approximate spectrum from "low-level" to "high-level"; "low-level" languages are typically more machine-oriented and faster to execute, whereas "high-level" languages are more abstract and easier to use but execute less quickly. It is usually easier to code in "high-level" languages than in "low-level" ones.
Programming languages are essential for software development. They are the building blocks for all software, from the simplest applications to the most sophisticated ones.
Allen Downey, in his book How To Think Like A Computer Scientist, writes:
The details look different in different languages, but a few basic instructions appear in just about every language:
Input: Gather data from the keyboard, a file, or some other device.
Output: Display data on the screen or send data to a file or other device.
Arithmetic: Perform basic arithmetical operations like addition and multiplication.
Conditional Execution: Check for certain conditions and execute the appropriate sequence of statements.
Repetition: Perform some action repeatedly, usually with some variation.
Many computer languages provide a mechanism to call functions provided by shared libraries. Provided the functions in a library follow the appropriate run-time conventions (e.g., method of passing arguments), then these functions may be written in any other language.
== Learning to program ==
Learning to program has a long history related to professional standards and practices, academic initiatives and curriculum, and commercial books and materials for students, self-taught learners, hobbyists, and others who desire to create or customize software for personal use. Since the 1960s, learning to program has taken on the characteristics of a popular movement, with the rise of academic disciplines, inspirational leaders, collective identities, and strategies to grow the movement and make institutionalize change. Through these social ideals and educational agendas, learning to code has become important not just for scientists and engineers, but for millions of citizens who have come to believe that creating software is beneficial to society and its members.
=== Context ===
In 1957, there were approximately 15,000 computer programmers employed in the U.S., a figure that accounts for 80% of the world's active developers. In 2014, there were approximately 18.5 million professional programmers in the world, of which 11 million can be considered professional and 7.5 million student or hobbyists. Before the rise of the commercial Internet in the mid-1990s, most programmers learned about software construction through books, magazines, user groups, and informal instruction methods, with academic coursework and corporate training playing important roles for professional workers.
The first book containing specific instructions about how to program a computer may have been Maurice Wilkes, David Wheeler, and Stanley Gill's Preparation of Programs for an Electronic Digital Computer (1951). The book offered a selection of common subroutines for handling basic operations on the EDSAC, one of the world's first stored-program computers.
When high-level languages arrived, they were introduced by numerous books and materials that explained language keywords, managing program flow, working with data, and other concepts. These languages included FLOW-MATIC, COBOL, FORTRAN, ALGOL, Pascal, BASIC, and C. An example of an early programming primer from these years is Marshal H. Wrubel's A Primer of Programming for Digital Computers (1959), which included step-by-step instructions for filling out coding sheets, creating punched cards, and using the keywords in IBM's early FORTRAN system. Daniel McCracken's A Guide to FORTRAN Programming (1961) presented FORTRAN to a larger audience, including students and office workers.
In 1961, Alan Perlis suggested that all university freshmen at Carnegie Technical Institute take a course in computer programming. His advice was published in the popular technical journal Computers and Automation, which became a regular source of information for professional programmers.
Programmers soon had a range of learning texts at their disposal. Programmer's references listed keywords and functions related to a language, often in alphabetical order, as well as technical information about compilers and related systems. An early example was IBM's Programmers' Reference Manual: the FORTRAN Automatic Coding System for the IBM 704 EDPM (1956).
Over time, the genre of programmer's guides emerged, which presented the features of a language in tutorial or step by step format. Many early primers started with a program known as “Hello, World”, which presented the shortest program a developer could create in a given system. Programmer's guides then went on to discuss core topics like declaring variables, data types, formulas, flow control, user-defined functions, manipulating data, and other topics.
Early and influential programmer's guides included John G. Kemeny and Thomas E. Kurtz's BASIC Programming (1967), Kathleen Jensen and Niklaus Wirth's The Pascal User Manual and Report (1971), and Brian W. Kernighan and Dennis Ritchie's The C Programming Language (1978). Similar books for popular audiences (but with a much lighter tone) included Bob Albrecht's My Computer Loves Me When I Speak BASIC (1972), Al Kelley and Ira Pohl's A Book on C (1984), and Dan Gookin's C for Dummies (1994).
Beyond language-specific primers, there were numerous books and academic journals that introduced professional programming practices. Many were designed for university courses in computer science, software engineering, or related disciplines. Donald Knuth's The Art of Computer Programming (1968 and later), presented hundreds of computational algorithms and their analysis. The Elements of Programming Style (1974), by Brian W. Kernighan and P. J. Plauger, concerned itself with programming style, the idea that programs should be written not only to satisfy the compiler but human readers. Jon Bentley's Programming Pearls (1986) offered practical advice about the art and craft of programming in professional and academic contexts. Texts specifically designed for students included Doug Cooper and Michael Clancy's Oh Pascal! (1982), Alfred Aho's Data Structures and Algorithms (1983), and Daniel Watt's Learning with Logo (1983).
=== Technical publishers ===
As personal computers became mass-market products, thousands of trade books and magazines sought to teach professional, hobbyist, and casual users to write computer programs. A sample of these learning resources includes BASIC Computer Games, Microcomputer Edition (1978), by David Ahl; Programming the Z80 (1979), by Rodnay Zaks; Programmer's CP/M Handbook (1983), by Andy Johnson-Laird; C Primer Plus (1984), by Mitchell Waite and The Waite Group; The Peter Norton Programmer's Guide to the IBM PC (1985), by Peter Norton; Advanced MS-DOS (1986), by Ray Duncan; Learn BASIC Now (1989), by Michael Halvorson and David Rygymr; Programming Windows (1992 and later), by Charles Petzold; Code Complete: A Practical Handbook for Software Construction (1993), by Steve McConnell; and Tricks of the Game-Programming Gurus (1994), by André LaMothe.
The PC software industry spurred the creation of numerous book publishers that offered programming primers and tutorials, as well as books for advanced software developers. These publishers included Addison-Wesley, IDG, Macmillan Inc., McGraw-Hill, Microsoft Press, O'Reilly Media, Prentice Hall, Sybex, Ventana Press, Waite Group Press, Wiley, Wrox Press, and Ziff-Davis.
Computer magazines and journals also provided learning content for professional and hobbyist programmers. A partial list of these resources includes Amiga World, Byte (magazine), Communications of the ACM, Computer (magazine), Compute!, Computer Language (magazine), Computers and Electronics, Dr. Dobb's Journal, IEEE Software, Macworld, PC Magazine, PC/Computing, and UnixWorld.
=== Digital learning / online resources ===
Between 2000 and 2010, computer book and magazine publishers declined significantly as providers of programming instruction, as programmers moved to Internet resources to expand their access to information. This shift brought forward new digital products and mechanisms to learn programming skills. During the transition, digital books from publishers transferred information that had traditionally been delivered in print to new and expanding audiences.
Important Internet resources for learning to code included blogs, wikis, videos, online databases, subscription sites, and custom websites focused on coding skills. New commercial resources included YouTube videos, Lynda.com tutorials (later LinkedIn Learning), Khan Academy, Codecademy, GitHub, W3Schools, and numerous coding bootcamps.
Most software development systems and game engines included rich online help resources, including integrated development environments (IDEs), context-sensitive help, APIs, and other digital resources. Commercial software development kits (SDKs) also provided a collection of software development tools and documentation in one installable package.
Commercial and non-profit organizations published learning websites for developers, created blogs, and established newsfeeds and social media resources about programming. Corporations like Apple, Microsoft, Oracle, Google, and Amazon built corporate websites providing support for programmers, including resources like the Microsoft Developer Network (MSDN). Contemporary movements like Hour of Code (Code.org) show how learning to program has become associated with digital learning strategies, education agendas, and corporate philanthropy.
== Programmers ==
Computer programmers are those who write computer software. Their jobs usually involve:
Although programming has been presented in the media as a somewhat mathematical subject, some research shows that good programmers have strong skills in natural human languages, and that learning to code is similar to learning a foreign language.
== See also ==
Code smell
Computer networking
Competitive programming
Programming best practices
Systems programming
== References ==
=== Sources ===
Ceruzzi, Paul E. (1998). History of Computing. Cambridge, Massachusetts: MIT Press. ISBN 9780262032551 – via EBSCOhost.
Evans, Claire L. (2018). Broad Band: The Untold Story of the Women Who Made the Internet. New York: Portfolio/Penguin. ISBN 9780735211759.
Gürer, Denise (1995). "Pioneering Women in Computer Science" (PDF). Communications of the ACM. 38 (1): 45–54. doi:10.1145/204865.204875. S2CID 6626310. Archived (PDF) from the original on October 9, 2022.
Smith, Erika E. (2013). "Recognizing a Collective Inheritance through the History of Women in Computing". CLCWeb: Comparative Literature & Culture. 15 (1): 1–9. doi:10.7771/1481-4374.1972.
Essinger, J., & EBSCO Publishing (Firm). (2014). Ada's algorithm: How lord byron's daughter ada lovelace launched the digital age. Melville House.
== Further reading ==
A.K. Hartmann, Practical Guide to Computer Simulations, Singapore: World Scientific (2009)
A. Hunt, D. Thomas, and W. Cunningham, The Pragmatic Programmer. From Journeyman to Master, Amsterdam: Addison-Wesley Longman (1999)
Brian W. Kernighan, The Practice of Programming, Pearson (1999)
Weinberg, Gerald M., The Psychology of Computer Programming, New York: Van Nostrand Reinhold (1971)
Edsger W. Dijkstra, A Discipline of Programming, Prentice-Hall (1976)
O.-J. Dahl, E.W.Dijkstra, C.A.R. Hoare, Structured Programming, Academic Press (1972)
David Gries, The Science of Programming, Springer-Verlag (1981)
== External links ==
Media related to Computer programming at Wikimedia Commons
Quotations related to Programming at Wikiquote
|
https://en.wikipedia.org/wiki/Computer_programming
|
A programming language is a system of notation for writing computer programs.
Programming languages are described in terms of their syntax (form) and semantics (meaning), usually defined by a formal language. Languages usually provide features such as a type system, variables, and mechanisms for error handling. An implementation of a programming language is required in order to execute programs, namely an interpreter or a compiler. An interpreter directly executes the source code, while a compiler produces an executable program.
Computer architecture has strongly influenced the design of programming languages, with the most common type (imperative languages—which implement operations in a specified order) developed to perform well on the popular von Neumann architecture. While early programming languages were closely tied to the hardware, over time they have developed more abstraction to hide implementation details for greater simplicity.
Thousands of programming languages—often classified as imperative, functional, logic, or object-oriented—have been developed for a wide variety of uses. Many aspects of programming language design involve tradeoffs—for example, exception handling simplifies error handling, but at a performance cost. Programming language theory is the subfield of computer science that studies the design, implementation, analysis, characterization, and classification of programming languages.
== Definitions ==
Programming languages differ from natural languages in that natural languages are used for interaction between people, while programming languages are designed to allow humans to communicate instructions to machines.
The term computer language is sometimes used interchangeably with "programming language". However, usage of these terms varies among authors.
In one usage, programming languages are described as a subset of computer languages. Similarly, the term "computer language" may be used in contrast to the term "programming language" to describe languages used in computing but not considered programming languages. Most practical programming languages are Turing complete, and as such are equivalent in what programs they can compute.
Another usage regards programming languages as theoretical constructs for programming abstract machines and computer languages as the subset thereof that runs on physical computers, which have finite hardware resources. John C. Reynolds emphasizes that formal specification languages are just as much programming languages as are the languages intended for execution. He also argues that textual and even graphical input formats that affect the behavior of a computer are programming languages, despite the fact they are commonly not Turing-complete, and remarks that ignorance of programming language concepts is the reason for many flaws in input formats.
== History ==
=== Early developments ===
The first programmable computers were invented at the end of the 1940s, and with them, the first programming languages. The earliest computers were programmed in first-generation programming languages (1GLs), machine language (simple instructions that could be directly executed by the processor). This code was very difficult to debug and was not portable between different computer systems. In order to improve the ease of programming, assembly languages (or second-generation programming languages—2GLs) were invented, diverging from the machine language to make programs easier to understand for humans, although they did not increase portability.
Initially, hardware resources were scarce and expensive, while human resources were cheaper. Therefore, cumbersome languages that were time-consuming to use, but were closer to the hardware for higher efficiency were favored. The introduction of high-level programming languages (third-generation programming languages—3GLs)—revolutionized programming. These languages abstracted away the details of the hardware, instead being designed to express algorithms that could be understood more easily by humans. For example, arithmetic expressions could now be written in symbolic notation and later translated into machine code that the hardware could execute. In 1957, Fortran (FORmula TRANslation) was invented. Often considered the first compiled high-level programming language, Fortran has remained in use into the twenty-first century.
=== 1960s and 1970s ===
Around 1960, the first mainframes—general purpose computers—were developed, although they could only be operated by professionals and the cost was extreme. The data and instructions were input by punch cards, meaning that no input could be added while the program was running. The languages developed at this time therefore are designed for minimal interaction. After the invention of the microprocessor, computers in the 1970s became dramatically cheaper. New computers also allowed more user interaction, which was supported by newer programming languages.
Lisp, implemented in 1958, was the first functional programming language. Unlike Fortran, it supported recursion and conditional expressions, and it also introduced dynamic memory management on a heap and automatic garbage collection. For the next decades, Lisp dominated artificial intelligence applications. In 1978, another functional language, ML, introduced inferred types and polymorphic parameters.
After ALGOL (ALGOrithmic Language) was released in 1958 and 1960, it became the standard in computing literature for describing algorithms. Although its commercial success was limited, most popular imperative languages—including C, Pascal, Ada, C++, Java, and C#—are directly or indirectly descended from ALGOL 60. Among its innovations adopted by later programming languages included greater portability and the first use of context-free, BNF grammar. Simula, the first language to support object-oriented programming (including subtypes, dynamic dispatch, and inheritance), also descends from ALGOL and achieved commercial success. C, another ALGOL descendant, has sustained popularity into the twenty-first century. C allows access to lower-level machine operations more than other contemporary languages. Its power and efficiency, generated in part with flexible pointer operations, comes at the cost of making it more difficult to write correct code.
Prolog, designed in 1972, was the first logic programming language, communicating with a computer using formal logic notation. With logic programming, the programmer specifies a desired result and allows the interpreter to decide how to achieve it.
=== 1980s to 2000s ===
During the 1980s, the invention of the personal computer transformed the roles for which programming languages were used. New languages introduced in the 1980s included C++, a superset of C that can compile C programs but also supports classes and inheritance. Ada and other new languages introduced support for concurrency. The Japanese government invested heavily into the so-called fifth-generation languages that added support for concurrency to logic programming constructs, but these languages were outperformed by other concurrency-supporting languages.
Due to the rapid growth of the Internet and the World Wide Web in the 1990s, new programming languages were introduced to support Web pages and networking. Java, based on C++ and designed for increased portability across systems and security, enjoyed large-scale success because these features are essential for many Internet applications. Another development was that of dynamically typed scripting languages—Python, JavaScript, PHP, and Ruby—designed to quickly produce small programs that coordinate existing applications. Due to their integration with HTML, they have also been used for building web pages hosted on servers.
=== 2000s to present ===
During the 2000s, there was a slowdown in the development of new programming languages that achieved widespread popularity. One innovation was service-oriented programming, designed to exploit distributed systems whose components are connected by a network. Services are similar to objects in object-oriented programming, but run on a separate process. C# and F# cross-pollinated ideas between imperative and functional programming. After 2010, several new languages—Rust, Go, Swift, Zig and Carbon —competed for the performance-critical software for which C had historically been used. Most of the new programming languages use static typing while a few numbers of new languages use dynamic typing like Ring and Julia.
Some of the new programming languages are classified as visual programming languages like Scratch, LabVIEW and PWCT. Also, some of these languages mix between textual and visual programming usage like Ballerina. Also, this trend lead to developing projects that help in developing new VPLs like Blockly by Google. Many game engines like Unreal and Unity added support for visual scripting too.
== Elements ==
Every programming language includes fundamental elements for describing data and the operations or transformations applied to them, such as adding two numbers or selecting an item from a collection. These elements are governed by syntactic and semantic rules that define their structure and meaning, respectively.
=== Syntax ===
A programming language's surface form is known as its syntax. Most programming languages are purely textual; they use sequences of text including words, numbers, and punctuation, much like written natural languages. On the other hand, some programming languages are graphical, using visual relationships between symbols to specify a program.
The syntax of a language describes the possible combinations of symbols that form a syntactically correct program. The meaning given to a combination of symbols is handled by semantics (either formal or hard-coded in a reference implementation). Since most languages are textual, this article discusses textual syntax.
The programming language syntax is usually defined using a combination of regular expressions (for lexical structure) and Backus–Naur form (for grammatical structure). Below is a simple grammar, based on Lisp:
This grammar specifies the following:
an expression is either an atom or a list;
an atom is either a number or a symbol;
a number is an unbroken sequence of one or more decimal digits, optionally preceded by a plus or minus sign;
a symbol is a letter followed by zero or more of any alphabetical characters (excluding whitespace); and
a list is a matched pair of parentheses, with zero or more expressions inside it.
The following are examples of well-formed token sequences in this grammar: 12345, () and (a b c232 (1)).
Not all syntactically correct programs are semantically correct. Many syntactically correct programs are nonetheless ill-formed, per the language's rules; and may (depending on the language specification and the soundness of the implementation) result in an error on translation or execution. In some cases, such programs may exhibit undefined behavior. Even when a program is well-defined within a language, it may still have a meaning that is not intended by the person who wrote it.
Using natural language as an example, it may not be possible to assign a meaning to a grammatically correct sentence or the sentence may be false:
"Colorless green ideas sleep furiously." is grammatically well-formed but has no generally accepted meaning.
"John is a married bachelor." is grammatically well-formed but expresses a meaning that cannot be true.
The following C language fragment is syntactically correct, but performs operations that are not semantically defined (the operation *p >> 4 has no meaning for a value having a complex type and p->im is not defined because the value of p is the null pointer):
If the type declaration on the first line were omitted, the program would trigger an error on the undefined variable p during compilation. However, the program would still be syntactically correct since type declarations provide only semantic information.
The grammar needed to specify a programming language can be classified by its position in the Chomsky hierarchy. The syntax of most programming languages can be specified using a Type-2 grammar, i.e., they are context-free grammars. Some languages, including Perl and Lisp, contain constructs that allow execution during the parsing phase. Languages that have constructs that allow the programmer to alter the behavior of the parser make syntax analysis an undecidable problem, and generally blur the distinction between parsing and execution. In contrast to Lisp's macro system and Perl's BEGIN blocks, which may contain general computations, C macros are merely string replacements and do not require code execution.
=== Semantics ===
The term semantics refers to the meaning of languages, as opposed to their form (syntax).
==== Static semantics ====
Static semantics defines restrictions on the structure of valid texts that are hard or impossible to express in standard syntactic formalisms. For compiled languages, static semantics essentially include those semantic rules that can be checked at compile time. Examples include checking that every identifier is declared before it is used (in languages that require such declarations) or that the labels on the arms of a case statement are distinct. Many important restrictions of this type, like checking that identifiers are used in the appropriate context (e.g. not adding an integer to a function name), or that subroutine calls have the appropriate number and type of arguments, can be enforced by defining them as rules in a logic called a type system. Other forms of static analyses like data flow analysis may also be part of static semantics. Programming languages such as Java and C# have definite assignment analysis, a form of data flow analysis, as part of their respective static semantics.
==== Dynamic semantics ====
Once data has been specified, the machine must be instructed to perform operations on the data. For example, the semantics may define the strategy by which expressions are evaluated to values, or the manner in which control structures conditionally execute statements. The dynamic semantics (also known as execution semantics) of a language defines how and when the various constructs of a language should produce a program behavior. There are many ways of defining execution semantics. Natural language is often used to specify the execution semantics of languages commonly used in practice. A significant amount of academic research goes into formal semantics of programming languages, which allows execution semantics to be specified in a formal manner. Results from this field of research have seen limited application to programming language design and implementation outside academia.
=== Type system ===
A data type is a set of allowable values and operations that can be performed on these values. Each programming language's type system defines which data types exist, the type of an expression, and how type equivalence and type compatibility function in the language.
According to type theory, a language is fully typed if the specification of every operation defines types of data to which the operation is applicable. In contrast, an untyped language, such as most assembly languages, allows any operation to be performed on any data, generally sequences of bits of various lengths. In practice, while few languages are fully typed, most offer a degree of typing.
Because different types (such as integers and floats) represent values differently, unexpected results will occur if one type is used when another is expected. Type checking will flag this error, usually at compile time (runtime type checking is more costly). With strong typing, type errors can always be detected unless variables are explicitly cast to a different type. Weak typing occurs when languages allow implicit casting—for example, to enable operations between variables of different types without the programmer making an explicit type conversion. The more cases in which this type coercion is allowed, the fewer type errors can be detected.
==== Commonly supported types ====
Early programming languages often supported only built-in, numeric types such as the integer (signed and unsigned) and floating point (to support operations on real numbers that are not integers). Most programming languages support multiple sizes of floats (often called float and double) and integers depending on the size and precision required by the programmer. Storing an integer in a type that is too small to represent it leads to integer overflow. The most common way of representing negative numbers with signed types is twos complement, although ones complement is also used. Other common types include Boolean—which is either true or false—and character—traditionally one byte, sufficient to represent all ASCII characters.
Arrays are a data type whose elements, in many languages, must consist of a single type of fixed length. Other languages define arrays as references to data stored elsewhere and support elements of varying types. Depending on the programming language, sequences of multiple characters, called strings, may be supported as arrays of characters or their own primitive type. Strings may be of fixed or variable length, which enables greater flexibility at the cost of increased storage space and more complexity. Other data types that may be supported include lists, associative (unordered) arrays accessed via keys, records in which data is mapped to names in an ordered structure, and tuples—similar to records but without names for data fields. Pointers store memory addresses, typically referencing locations on the heap where other data is stored.
The simplest user-defined type is an ordinal type, often called an enumeration, whose values can be mapped onto the set of positive integers. Since the mid-1980s, most programming languages also support abstract data types, in which the representation of the data and operations are hidden from the user, who can only access an interface. The benefits of data abstraction can include increased reliability, reduced complexity, less potential for name collision, and allowing the underlying data structure to be changed without the client needing to alter its code.
==== Static and dynamic typing ====
In static typing, all expressions have their types determined before a program executes, typically at compile-time. Most widely used, statically typed programming languages require the types of variables to be specified explicitly. In some languages, types are implicit; one form of this is when the compiler can infer types based on context. The downside of implicit typing is the potential for errors to go undetected. Complete type inference has traditionally been associated with functional languages such as Haskell and ML.
With dynamic typing, the type is not attached to the variable but only the value encoded in it. A single variable can be reused for a value of a different type. Although this provides more flexibility to the programmer, it is at the cost of lower reliability and less ability for the programming language to check for errors. Some languages allow variables of a union type to which any type of value can be assigned, in an exception to their usual static typing rules.
=== Concurrency ===
In computing, multiple instructions can be executed simultaneously. Many programming languages support instruction-level and subprogram-level concurrency. By the twenty-first century, additional processing power on computers was increasingly coming from the use of additional processors, which requires programmers to design software that makes use of multiple processors simultaneously to achieve improved performance. Interpreted languages such as Python and Ruby do not support the concurrent use of multiple processors. Other programming languages do support managing data shared between different threads by controlling the order of execution of key instructions via the use of semaphores, controlling access to shared data via monitor, or enabling message passing between threads.
=== Exception handling ===
Many programming languages include exception handlers, a section of code triggered by runtime errors that can deal with them in two main ways:
Termination: shutting down and handing over control to the operating system. This option is considered the simplest.
Resumption: resuming the program near where the exception occurred. This can trigger a repeat of the exception, unless the exception handler is able to modify values to prevent the exception from reoccurring.
Some programming languages support dedicating a block of code to run regardless of whether an exception occurs before the code is reached; this is called finalization.
There is a tradeoff between increased ability to handle exceptions and reduced performance. For example, even though array index errors are common C does not check them for performance reasons. Although programmers can write code to catch user-defined exceptions, this can clutter a program. Standard libraries in some languages, such as C, use their return values to indicate an exception. Some languages and their compilers have the option of turning on and off error handling capability, either temporarily or permanently.
== Design and implementation ==
One of the most important influences on programming language design has been computer architecture. Imperative languages, the most commonly used type, were designed to perform well on von Neumann architecture, the most common computer architecture. In von Neumann architecture, the memory stores both data and instructions, while the CPU that performs instructions on data is separate, and data must be piped back and forth to the CPU. The central elements in these languages are variables, assignment, and iteration, which is more efficient than recursion on these machines.
Many programming languages have been designed from scratch, altered to meet new needs, and combined with other languages. Many have eventually fallen into disuse. The birth of programming languages in the 1950s was stimulated by the desire to make a universal programming language suitable for all machines and uses, avoiding the need to write code for different computers. By the early 1960s, the idea of a universal language was rejected due to the differing requirements of the variety of purposes for which code was written.
=== Tradeoffs ===
Desirable qualities of programming languages include readability, writability, and reliability. These features can reduce the cost of training programmers in a language, the amount of time needed to write and maintain programs in the language, the cost of compiling the code, and increase runtime performance.
Although early programming languages often prioritized efficiency over readability, the latter has grown in importance since the 1970s. Having multiple operations to achieve the same result can be detrimental to readability, as is overloading operators, so that the same operator can have multiple meanings. Another feature important to readability is orthogonality, limiting the number of constructs that a programmer has to learn. A syntax structure that is easily understood and special words that are immediately obvious also supports readability.
Writability is the ease of use for writing code to solve the desired problem. Along with the same features essential for readability, abstraction—interfaces that enable hiding details from the client—and expressivity—enabling more concise programs—additionally help the programmer write code. The earliest programming languages were tied very closely to the underlying hardware of the computer, but over time support for abstraction has increased, allowing programmers express ideas that are more remote from simple translation into underlying hardware instructions. Because programmers are less tied to the complexity of the computer, their programs can do more computing with less effort from the programmer. Most programming languages come with a standard library of commonly used functions.
Reliability means that a program performs as specified in a wide range of circumstances. Type checking, exception handling, and restricted aliasing (multiple variable names accessing the same region of memory) all can improve a program's reliability.
Programming language design often involves tradeoffs. For example, features to improve reliability typically come at the cost of performance. Increased expressivity due to a large number of operators makes writing code easier but comes at the cost of readability.
Natural-language programming has been proposed as a way to eliminate the need for a specialized language for programming. However, this goal remains distant and its benefits are open to debate. Edsger W. Dijkstra took the position that the use of a formal language is essential to prevent the introduction of meaningless constructs. Alan Perlis was similarly dismissive of the idea.
=== Specification ===
The specification of a programming language is an artifact that the language users and the implementors can use to agree upon whether a piece of source code is a valid program in that language, and if so what its behavior shall be.
A programming language specification can take several forms, including the following:
An explicit definition of the syntax, static semantics, and execution semantics of the language. While syntax is commonly specified using a formal grammar, semantic definitions may be written in natural language (e.g., as in the C language), or a formal semantics (e.g., as in Standard ML and Scheme specifications).
A description of the behavior of a translator for the language (e.g., the C++ and Fortran specifications). The syntax and semantics of the language have to be inferred from this description, which may be written in natural or formal language.
A reference or model implementation, sometimes written in the language being specified (e.g., Prolog or ANSI REXX). The syntax and semantics of the language are explicit in the behavior of the reference implementation.
=== Implementation ===
An implementation of a programming language is the conversion of a program into machine code that can be executed by the hardware. The machine code then can be executed with the help of the operating system. The most common form of interpretation in production code is by a compiler, which translates the source code via an intermediate-level language into machine code, known as an executable. Once the program is compiled, it will run more quickly than with other implementation methods. Some compilers are able to provide further optimization to reduce memory or computation usage when the executable runs, but increasing compilation time.
Another implementation method is to run the program with an interpreter, which translates each line of software into machine code just before it executes. Although it can make debugging easier, the downside of interpretation is that it runs 10 to 100 times slower than a compiled executable. Hybrid interpretation methods provide some of the benefits of compilation and some of the benefits of interpretation via partial compilation. One form this takes is just-in-time compilation, in which the software is compiled ahead of time into an intermediate language, and then into machine code immediately before execution.
== Proprietary languages ==
Although most of the most commonly used programming languages have fully open specifications and implementations, many programming languages exist only as proprietary programming languages with the implementation available only from a single vendor, which may claim that such a proprietary language is their intellectual property. Proprietary programming languages are commonly domain-specific languages or internal scripting languages for a single product; some proprietary languages are used only internally within a vendor, while others are available to external users.
Some programming languages exist on the border between proprietary and open; for example, Oracle Corporation asserts proprietary rights to some aspects of the Java programming language, and Microsoft's C# programming language, which has open implementations of most parts of the system, also has Common Language Runtime (CLR) as a closed environment.
Many proprietary languages are widely used, in spite of their proprietary nature; examples include MATLAB, VBScript, and Wolfram Language. Some languages may make the transition from closed to open; for example, Erlang was originally Ericsson's internal programming language.
Open source programming languages are particularly helpful for open science applications, enhancing the capacity for replication and code sharing.
== Use ==
Thousands of different programming languages have been created, mainly in the computing field.
Individual software projects commonly use five programming languages or more.
Programming languages differ from most other forms of human expression in that they require a greater degree of precision and completeness. When using a natural language to communicate with other people, human authors and speakers can be ambiguous and make small errors, and still expect their intent to be understood. However, figuratively speaking, computers "do exactly what they are told to do", and cannot "understand" what code the programmer intended to write. The combination of the language definition, a program, and the program's inputs must fully specify the external behavior that occurs when the program is executed, within the domain of control of that program. On the other hand, ideas about an algorithm can be communicated to humans without the precision required for execution by using pseudocode, which interleaves natural language with code written in a programming language.
A programming language provides a structured mechanism for defining pieces of data, and the operations or transformations that may be carried out automatically on that data. A programmer uses the abstractions present in the language to represent the concepts involved in a computation. These concepts are represented as a collection of the simplest elements available (called primitives). Programming is the process by which programmers combine these primitives to compose new programs, or adapt existing ones to new uses or a changing environment.
Programs for a computer might be executed in a batch process without any human interaction, or a user might type commands in an interactive session of an interpreter. In this case the "commands" are simply programs, whose execution is chained together. When a language can run its commands through an interpreter (such as a Unix shell or other command-line interface), without compiling, it is called a scripting language.
=== Measuring language usage ===
Determining which is the most widely used programming language is difficult since the definition of usage varies by context. One language may occupy the greater number of programmer hours, a different one has more lines of code, and a third may consume the most CPU time. Some languages are very popular for particular kinds of applications. For example, COBOL is still strong in the corporate data center, often on large mainframes; Fortran in scientific and engineering applications; Ada in aerospace, transportation, military, real-time, and embedded applications; and C in embedded applications and operating systems. Other languages are regularly used to write many different kinds of applications.
Various methods of measuring language popularity, each subject to a different bias over what is measured, have been proposed:
counting the number of job advertisements that mention the language
the number of books sold that teach or describe the language
estimates of the number of existing lines of code written in the language – which may underestimate languages not often found in public searches
counts of language references (i.e., to the name of the language) found using a web search engine.
Combining and averaging information from various internet sites, stackify.com reported the ten most popular programming languages (in descending order by overall popularity): Java, C, C++, Python, C#, JavaScript, VB .NET, R, PHP, and MATLAB.
As of June 2024, the top five programming languages as measured by TIOBE index are Python, C++, C, Java and C#. TIOBE provides a list of top 100 programming languages according to popularity and update this list every month.
== Dialects, flavors and implementations ==
A dialect of a programming language or a data exchange language is a (relatively small) variation or extension of the language that does not change its intrinsic nature. With languages such as Scheme and Forth, standards may be considered insufficient, inadequate, or illegitimate by implementors, so often they will deviate from the standard, making a new dialect. In other cases, a dialect is created for use in a domain-specific language, often a subset. In the Lisp world, most languages that use basic S-expression syntax and Lisp-like semantics are considered Lisp dialects, although they vary wildly as do, say, Racket and Clojure. As it is common for one language to have several dialects, it can become quite difficult for an inexperienced programmer to find the right documentation. The BASIC language has many dialects.
== Classifications ==
Programming languages are often placed into four main categories: imperative, functional, logic, and object oriented.
Imperative languages are designed to implement an algorithm in a specified order; they include visual programming languages such as .NET for generating graphical user interfaces. Scripting languages, which are partly or fully interpreted rather than compiled, are sometimes considered a separate category but meet the definition of imperative languages.
Functional programming languages work by successively applying functions to the given parameters. Although appreciated by many researchers for their simplicity and elegance, problems with efficiency have prevented them from being widely adopted.
Logic languages are designed so that the software, rather than the programmer, decides what order in which the instructions are executed.
Object-oriented programming—whose characteristic features are data abstraction, inheritance, and dynamic dispatch—is supported by most popular imperative languages and some functional languages.
Although markup languages are not programming languages, some have extensions that support limited programming. Additionally, there are special-purpose languages that are not easily compared to other programming languages.
== See also ==
== References ==
== Further reading ==
|
https://en.wikipedia.org/wiki/Programming_language
|
C (pronounced – like the letter c) is a general-purpose programming language. It was created in the 1970s by Dennis Ritchie and remains very widely used and influential. By design, C's features cleanly reflect the capabilities of the targeted CPUs. It has found lasting use in operating systems code (especially in kernels), device drivers, and protocol stacks, but its use in application software has been decreasing. C is commonly used on computer architectures that range from the largest supercomputers to the smallest microcontrollers and embedded systems.
A successor to the programming language B, C was originally developed at Bell Labs by Ritchie between 1972 and 1973 to construct utilities running on Unix. It was applied to re-implementing the kernel of the Unix operating system. During the 1980s, C gradually gained popularity. It has become one of the most widely used programming languages, with C compilers available for practically all modern computer architectures and operating systems. The book The C Programming Language, co-authored by the original language designer, served for many years as the de facto standard for the language. C has been standardized since 1989 by the American National Standards Institute (ANSI) and, subsequently, jointly by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC).
C is an imperative procedural language, supporting structured programming, lexical variable scope, and recursion, with a static type system. It was designed to be compiled to provide low-level access to memory and language constructs that map efficiently to machine instructions, all with minimal runtime support. Despite its low-level capabilities, the language was designed to encourage cross-platform programming. A standards-compliant C program written with portability in mind can be compiled for a wide variety of computer platforms and operating systems with few changes to its source code.
Since 2000, C has consistently ranked among the top four languages in the TIOBE index, a measure of the popularity of programming languages.
== Overview ==
C is an imperative, procedural language in the ALGOL tradition. It has a static type system. In C, all executable code is contained within subroutines (also called "functions", though not in the sense of functional programming). Function parameters are passed by value, although arrays are passed as pointers, i.e. the address of the first item in the array. Pass-by-reference is simulated in C by explicitly passing pointers to the thing being referenced.
C program source text is free-form code. Semicolons terminate statements, while curly braces are used to group statements into blocks.
The C language also exhibits the following characteristics:
The language has a small, fixed number of keywords, including a full set of control flow primitives: if/else, for, do/while, while, and switch. User-defined names are not distinguished from keywords by any kind of sigil.
It has a large number of arithmetic, bitwise, and logic operators: +,+=,++,&,||, etc.
More than one assignment may be performed in a single statement.
Functions:
Function return values can be ignored, when not needed.
Function and data pointers permit ad hoc run-time polymorphism.
Functions may not be defined within the lexical scope of other functions.
Variables may be defined within a function, with scope.
A function may call itself, so recursion is supported.
Data typing is static, but weakly enforced; all data has a type, but implicit conversions are possible.
User-defined (typedef) and compound types are possible.
Heterogeneous aggregate data types (struct) allow related data elements to be accessed and assigned as a unit. The contents of whole structs cannot be compared using a single built-in operator (the elements must be compared individually).
Union is a structure with overlapping members; it allows multiple data types to share the same memory location.
Array indexing is a secondary notation, defined in terms of pointer arithmetic. Whole arrays cannot be assigned or compared using a single built-in operator. There is no "array" keyword in use or definition; instead, square brackets indicate arrays syntactically, for example month[11].
Enumerated types are possible with the enum keyword. They are freely interconvertible with integers.
Strings are not a distinct data type, but are conventionally implemented as null-terminated character arrays.
Low-level access to computer memory is possible by converting machine addresses to pointers.
Procedures (subroutines not returning values) are a special case of function, with an empty return type void.
Memory can be allocated to a program with calls to library routines.
A preprocessor performs macro definition, source code file inclusion, and conditional compilation.
There is a basic form of modularity: files can be compiled separately and linked together, with control over which functions and data objects are visible to other files via static and extern attributes.
Complex functionality such as I/O, string manipulation, and mathematical functions are consistently delegated to library routines.
The generated code after compilation has relatively straightforward needs on the underlying platform, which makes it suitable for creating operating systems and for use in embedded systems.
While C does not include certain features found in other languages (such as object orientation and garbage collection), these can be implemented or emulated, often through the use of external libraries (e.g., the GLib Object System or the Boehm garbage collector).
=== Relations to other languages ===
Many later languages have borrowed directly or indirectly from C, including C++, C#, Unix's C shell, D, Go, Java, JavaScript (including transpilers), Julia, Limbo, LPC, Objective-C, Perl, PHP, Python, Ruby, Rust, Swift, Verilog and SystemVerilog (hardware description languages). These languages have drawn many of their control structures and other basic features from C. Most of them also express highly similar syntax to C, and they tend to combine the recognizable expression and statement syntax of C with underlying type systems, data models, and semantics that can be radically different.
== History ==
=== Early developments ===
The origin of C is closely tied to the development of the Unix operating system, originally implemented in assembly language on a PDP-7 by Dennis Ritchie and Ken Thompson, incorporating several ideas from colleagues. Eventually, they decided to port the operating system to a PDP-11. The original PDP-11 version of Unix was also developed in assembly language.
==== B ====
Thompson wanted a programming language for developing utilities for the new platform. He first tried writing a Fortran compiler, but he soon gave up the idea and instead created a cut-down version of the recently developed systems programming language called BCPL. The official description of BCPL was not available at the time, and Thompson modified the syntax to be less 'wordy' and similar to a simplified ALGOL known as SMALGOL. He called the result B, describing it as "BCPL semantics with a lot of SMALGOL syntax". Like BCPL, B had a bootstrapping compiler to facilitate porting to new machines. Ultimately, few utilities were written in B because it was too slow and could not take advantage of PDP-11 features such as byte addressability.
==== New B and first C release ====
In 1971 Ritchie started to improve B, to use the features of the more-powerful PDP-11. A significant addition was a character data type. He called this New B (NB). Thompson started to use NB to write the Unix kernel, and his requirements shaped the direction of the language development. Through to 1972, richer types were added to the NB language: NB had arrays of int and char. Pointers, the ability to generate pointers to other types, arrays of all types, and types to be returned from functions were all also added. Arrays within expressions became pointers. A new compiler was written, and the language was renamed C.
The C compiler and some utilities made with it were included in Version 2 Unix, which is also known as Research Unix.
==== Structures and Unix kernel re-write ====
At Version 4 Unix, released in November 1973, the Unix kernel was extensively re-implemented in C. By this time, the C language had acquired some powerful features such as struct types.
The preprocessor was introduced around 1973 at the urging of Alan Snyder and also in recognition of the usefulness of the file-inclusion mechanisms available in BCPL and PL/I. Its original version provided only included files and simple string replacements: #include and #define of parameterless macros. Soon after that, it was extended, mostly by Mike Lesk and then by John Reiser, to incorporate macros with arguments and conditional compilation.
Unix was one of the first operating system kernels implemented in a language other than assembly. Earlier instances include the Multics system (which was written in PL/I) and Master Control Program (MCP) for the Burroughs B5000 (which was written in ALGOL) in 1961. In around 1977, Ritchie and Stephen C. Johnson made further changes to the language to facilitate portability of the Unix operating system. Johnson's Portable C Compiler served as the basis for several implementations of C on new platforms.
=== K&R C ===
In 1978 Brian Kernighan and Dennis Ritchie published the first edition of The C Programming Language. Known as K&R from the initials of its authors, the book served for many years as an informal specification of the language. The version of C that it describes is commonly referred to as "K&R C". As this was released in 1978, it is now also referred to as C78. The second edition of the book covers the later ANSI C standard, described below.
K&R introduced several language features:
Standard I/O library
long int data type
unsigned int data type
Compound assignment operators of the form =op (such as =-) were changed to the form op= (that is, -=) to remove the semantic ambiguity created by constructs such as i=-10, which had been interpreted as i =- 10 (decrement i by 10) instead of the possibly intended i = -10 (let i be −10).
Even after the publication of the 1989 ANSI standard, for many years K&R C was still considered the "lowest common denominator" to which C programmers restricted themselves when maximum portability was desired, since many older compilers were still in use, and because carefully written K&R C code can be legal Standard C as well.
In early versions of C, only functions that return types other than int must be declared if used before the function definition; functions used without prior declaration were presumed to return type int.
For example:
The int type specifiers which are commented out could be omitted in K&R C, but are required in later standards.
Since K&R function declarations did not include any information about function arguments, function parameter type checks were not performed, although some compilers would issue a warning message if a local function was called with the wrong number of arguments, or if different calls to an external function used different numbers or types of arguments. Separate tools such as Unix's lint utility were developed that (among other things) could check for consistency of function use across multiple source files.
In the years following the publication of K&R C, several features were added to the language, supported by compilers from AT&T (in particular PCC) and some other vendors. These included:
void functions (i.e., functions with no return value)
functions returning struct or union types (previously only a single pointer, integer or float could be returned)
assignment for struct data types
enumerated types (previously, preprocessor definitions for integer fixed values were used, e.g. #define GREEN 3)
The large number of extensions and lack of agreement on a standard library, together with the language popularity and the fact that not even the Unix compilers precisely implemented the K&R specification, led to the necessity of standardization.
=== ANSI C and ISO C ===
During the late 1970s and 1980s, versions of C were implemented for a wide variety of mainframe computers, minicomputers, and microcomputers, including the IBM PC, as its popularity began to increase significantly.
In 1983 the American National Standards Institute (ANSI) formed a committee, X3J11, to establish a standard specification of C. X3J11 based the C standard on the Unix implementation; however, the non-portable portion of the Unix C library was handed off to the IEEE working group 1003 to become the basis for the 1988 POSIX standard. In 1989, the C standard was ratified as ANSI X3.159-1989 "Programming Language C". This version of the language is often referred to as ANSI C, Standard C, or sometimes C89.
In 1990 the ANSI C standard (with formatting changes) was adopted by the International Organization for Standardization (ISO) as ISO/IEC 9899:1990, which is sometimes called C90. Therefore, the terms "C89" and "C90" refer to the same programming language.
ANSI, like other national standards bodies, no longer develops the C standard independently, but defers to the international C standard, maintained by the working group ISO/IEC JTC1/SC22/WG14. National adoption of an update to the international standard typically occurs within a year of ISO publication.
One of the aims of the C standardization process was to produce a superset of K&R C, incorporating many of the subsequently introduced unofficial features. The standards committee also included several additional features such as function prototypes (borrowed from C++), void pointers, support for international character sets and locales, and preprocessor enhancements. Although the syntax for parameter declarations was augmented to include the style used in C++, the K&R interface continued to be permitted, for compatibility with existing source code.
C89 is supported by current C compilers, and most modern C code is based on it. Any program written only in Standard C and without any hardware-dependent assumptions will run correctly on any platform with a conforming C implementation, within its resource limits. Without such precautions, programs may compile only on a certain platform or with a particular compiler, due, for example, to the use of non-standard libraries, such as GUI libraries, or to a reliance on compiler- or platform-specific attributes such as the exact size of data types and byte endianness.
In cases where code must be compilable by either standard-conforming or K&R C-based compilers, the __STDC__ macro can be used to split the code into Standard and K&R sections to prevent the use on a K&R C-based compiler of features available only in Standard C.
After the ANSI/ISO standardization process, the C language specification remained relatively static for several years. In 1995, Normative Amendment 1 to the 1990 C standard (ISO/IEC 9899/AMD1:1995, known informally as C95) was published, to correct some details and to add more extensive support for international character sets.
=== C99 ===
The C standard was further revised in the late 1990s, leading to the publication of ISO/IEC 9899:1999 in 1999, which is commonly referred to as "C99". It has since been amended three times by Technical Corrigenda.
C99 introduced several new features, including inline functions, several new data types (including long long int and a complex type to represent complex numbers), variable-length arrays and flexible array members, improved support for IEEE 754 floating point, support for variadic macros (macros of variable arity), and support for one-line comments beginning with //, as in BCPL or C++. Many of these had already been implemented as extensions in several C compilers.
C99 is for the most part backward compatible with C90, but is stricter in some ways; in particular, a declaration that lacks a type specifier no longer has int implicitly assumed. A standard macro __STDC_VERSION__ is defined with value 199901L to indicate that C99 support is available. GCC, Solaris Studio, and other C compilers now support many or all of the new features of C99. The C compiler in Microsoft Visual C++, however, implements the C89 standard and those parts of C99 that are required for compatibility with C++11.
In addition, the C99 standard requires support for identifiers using Unicode in the form of escaped characters (e.g. \u0040 or \U0001f431) and suggests support for raw Unicode names.
=== C11 ===
Work began in 2007 on another revision of the C standard, informally called "C1X" until its official publication of ISO/IEC 9899:2011 on December 8, 2011. The C standards committee adopted guidelines to limit the adoption of new features that had not been tested by existing implementations.
The C11 standard adds numerous new features to C and the library, including type generic macros, anonymous structures, improved Unicode support, atomic operations, multi-threading, and bounds-checked functions. It also makes some portions of the existing C99 library optional, and improves compatibility with C++. The standard macro __STDC_VERSION__ is defined as 201112L to indicate that C11 support is available.
=== C17 ===
C17 is an informal name for ISO/IEC 9899:2018, a standard for the C programming language published in June 2018. It introduces no new language features, only technical corrections, and clarifications to defects in C11. The standard macro __STDC_VERSION__ is defined as 201710L to indicate that C17 support is available.
=== C23 ===
C23 is an informal name for the current major C language standard revision. It was informally known as "C2X" through most of its development. C23 was published in October 2024 as ISO/IEC 9899:2024. The standard macro __STDC_VERSION__ is defined as 202311L to indicate that C23 support is available.
=== C2Y ===
C2Y is an informal name for the next major C language standard revision, after C23 (C2X), that is hoped to be released later in the 2020s, hence the '2' in "C2Y". An early working draft of C2Y was released in February 2024 as N3220 by the working group ISO/IEC JTC1/SC22/WG14.
=== Embedded C ===
Historically, embedded C programming requires non-standard extensions to the C language to support exotic features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations.
In 2008, the C Standards Committee published a technical report extending the C language to address these issues by providing a common standard for all implementations to adhere to. It includes a number of features not available in normal C, such as fixed-point arithmetic, named address spaces, and basic I/O hardware addressing.
== Syntax ==
C has a formal grammar specified by the C standard. Line endings are generally not significant in C; however, line boundaries do have significance during the preprocessing phase. Comments may appear either between the delimiters /* and */, or (since C99) following // until the end of the line. Comments delimited by /* and */ do not nest, and these sequences of characters are not interpreted as comment delimiters if they appear inside string or character literals.
C source files contain declarations and function definitions. Function definitions, in turn, contain declarations and statements. Declarations either define new types using keywords such as struct, union, and enum, or assign types to and perhaps reserve storage for new variables, usually by writing the type followed by the variable name. Keywords such as char and int specify built-in types. Sections of code are enclosed in braces ({ and }, sometimes called "curly brackets") to limit the scope of declarations and to act as a single statement for control structures.
As an imperative language, C uses statements to specify actions. The most common statement is an expression statement, consisting of an expression to be evaluated, followed by a semicolon; as a side effect of the evaluation, functions may be called and variables assigned new values. To modify the normal sequential execution of statements, C provides several control-flow statements identified by reserved keywords. Structured programming is supported by if ... [else] conditional execution and by do ... while, while, and for iterative execution (looping). The for statement has separate initialization, testing, and reinitialization expressions, any or all of which can be omitted. break and continue can be used within the loop. Break is used to leave the innermost enclosing loop statement and continue is used to skip to its reinitialisation. There is also a non-structured goto statement which branches directly to the designated label within the function. switch selects a case to be executed based on the value of an integer expression. Different from many other languages, control-flow will fall through to the next case unless terminated by a break.
Expressions can use a variety of built-in operators and may contain function calls. The order in which arguments to functions and operands to most operators are evaluated is unspecified. The evaluations may even be interleaved. However, all side effects (including storage to variables) will occur before the next "sequence point"; sequence points include the end of each expression statement, and the entry to and return from each function call. Sequence points also occur during evaluation of expressions containing certain operators (&&, ||, ?: and the comma operator). This permits a high degree of object code optimization by the compiler, but requires C programmers to take more care to obtain reliable results than is needed for other programming languages.
Kernighan and Ritchie say in the Introduction of The C Programming Language: "C, like any other language, has its blemishes. Some of the operators have the wrong precedence; some parts of the syntax could be better." The C standard did not attempt to correct many of these blemishes, because of the impact of such changes on already existing software.
=== Character set ===
The basic C source character set includes the following characters:
Lowercase and uppercase letters of the ISO basic Latin alphabet: a–z, A–Z
Decimal digits: 0–9
Graphic characters: ! " # % & ' ( ) * + , - . / : ; < = > ? [ \ ] ^ _ { | } ~
Whitespace characters: space, horizontal tab, vertical tab, form feed, newline
The newline character indicates the end of a text line; it need not correspond to an actual single character, although for convenience C treats it as such.
Additional multi-byte encoded characters may be used in string literals, but they are not entirely portable. Since C99 multi-national Unicode characters can be embedded portably within C source text by using \uXXXX or \UXXXXXXXX encoding (where X denotes a hexadecimal character).
The basic C execution character set contains the same characters, along with representations for alert, backspace, and carriage return. Run-time support for extended character sets has increased with each revision of the C standard.
=== Reserved words ===
The following reserved words are case sensitive.
C89 has 32 reserved words, also known as 'keywords', which cannot be used for any purposes other than those for which they are predefined:
C99 added five more reserved words: (‡ indicates an alternative spelling alias for a C23 keyword)
C11 added seven more reserved words: (‡ indicates an alternative spelling alias for a C23 keyword)
C23 reserved fifteen more words:
Most of the recently reserved words begin with an underscore followed by a capital letter, because identifiers of that form were previously reserved by the C standard for use only by implementations. Since existing program source code should not have been using these identifiers, it would not be affected when C implementations started supporting these extensions to the programming language. Some standard headers do define more convenient synonyms for underscored identifiers. Some of those words were added as keywords with their conventional spelling in C23 and the corresponding macros were removed.
Prior to C89, entry was reserved as a keyword. In the second edition of their book The C Programming Language, which describes what became known as C89, Kernighan and Ritchie wrote, "The ... [keyword] entry, formerly reserved but never used, is no longer reserved." and "The stillborn entry keyword is withdrawn."
=== Operators ===
C supports a rich set of operators, which are symbols used within an expression to specify the manipulations to be performed while evaluating that expression. C has operators for:
arithmetic: +, -, *, /, %
assignment: =
augmented assignment: +=, -=, *=, /=, %=, &=, |=, ^=, <<=, >>=
bitwise logic: ~, &, |, ^
bitwise shifts: <<, >>
Boolean logic: !, &&, ||
conditional evaluation: ? :
equality testing: ==, !=
calling functions: ( )
increment and decrement: ++, --
member selection: ., ->
object size: sizeof
type: typeof, typeof_unqual since C23
order relations: <, <=, >, >=
reference and dereference: &, *, [ ]
sequencing: ,
subexpression grouping: ( )
type conversion: (typename)
C uses the operator = (used in mathematics to express equality) to indicate assignment, following the precedent of Fortran and PL/I, but unlike ALGOL and its derivatives. C uses the operator == to test for equality. The similarity between the operators for assignment and equality may result in the accidental use of one in place of the other, and in many cases the mistake does not produce an error message (although some compilers produce warnings). For example, the conditional expression if (a == b + 1) might mistakenly be written as if (a = b + 1), which will be evaluated as true unless the value of a is 0 after the assignment.
The C operator precedence is not always intuitive. For example, the operator == binds more tightly than (is executed prior to) the operators & (bitwise AND) and | (bitwise OR) in expressions such as x & 1 == 0, which must be written as (x & 1) == 0 if that is the coder's intent.
== "Hello, world" example ==
The "hello, world" example that appeared in the first edition of K&R has become the model for an introductory program in most programming textbooks. The program prints "hello, world" to the standard output, which is usually a terminal or screen display.
The original version was:
A standard-conforming "hello, world" program is:
The first line of the program contains a preprocessing directive, indicated by #include. This causes the compiler to replace that line of code with the entire text of the stdio.h header file, which contains declarations for standard input and output functions such as printf and scanf. The angle brackets surrounding stdio.h indicate that the header file can be located using a search strategy that prefers headers provided with the compiler to other headers having the same name (as opposed to double quotes which typically include local or project-specific header files).
The second line indicates that a function named main is being defined. The main function serves a special purpose in C programs; the run-time environment calls the main function to begin program execution. The type specifier int indicates that the value returned to the invoker (in this case the run-time environment) as a result of evaluating the main function, is an integer. The keyword void as a parameter list indicates that the main function takes no arguments.
The opening curly brace indicates the beginning of the code that defines the main function.
The next line of the program is a statement that calls (i.e. diverts execution to) a function named printf, which in this case is supplied from a system library. In this call, the printf function is passed (i.e. provided with) a single argument, which is the address of the first character in the string literal "hello, world\n". The string literal is an unnamed array set up automatically by the compiler, with elements of type char and a final NULL character (ASCII value 0) marking the end of the array (to allow printf to determine the length of the string). The NULL character can also be written as the escape sequence \0. The \n is a standard escape sequence that C translates to a newline character, which, on output, signifies the end of the current line. The return value of the printf function is of type int, but it is silently discarded since it is not used. (A more careful program might test the return value to check that the printf function succeeded.) The semicolon ; terminates the statement.
The closing curly brace indicates the end of the code for the main function. According to the C99 specification and newer, the main function (unlike any other function) will implicitly return a value of 0 upon reaching the } that terminates the function. The return value of 0 is interpreted by the run-time system as an exit code indicating successful execution of the function.
== Data types ==
The type system in C is static and weakly typed, which makes it similar to the type system of ALGOL descendants such as Pascal. There are built-in types for integers of various sizes, both signed and unsigned, floating-point numbers, and enumerated types (enum). Integer type char is often used for single-byte characters. C99 added a Boolean data type. There are also derived types including arrays, pointers, records (struct), and unions (union).
C is often used in low-level systems programming where escapes from the type system may be necessary. The compiler attempts to ensure type correctness of most expressions, but the programmer can override the checks in various ways, either by using a type cast to explicitly convert a value from one type to another, or by using pointers or unions to reinterpret the underlying bits of a data object in some other way.
Some find C's declaration syntax unintuitive, particularly for function pointers. (Ritchie's idea was to declare identifiers in contexts resembling their use: "declaration reflects use".)
C's usual arithmetic conversions allow for efficient code to be generated, but can sometimes produce unexpected results. For example, a comparison of signed and unsigned integers of equal width requires a conversion of the signed value to unsigned. This can generate unexpected results if the signed value is negative.
=== Pointers ===
C supports the use of pointers, a type of reference that records the address or location of an object or function in memory. Pointers can be dereferenced to access data stored at the address pointed to, or to invoke a pointed-to function. Pointers can be manipulated using assignment or pointer arithmetic. The run-time representation of a pointer value is typically a raw memory address (perhaps augmented by an offset-within-word field), but since a pointer's type includes the type of the thing pointed to, expressions including pointers can be type-checked at compile time. Pointer arithmetic is automatically scaled by the size of the pointed-to data type.
Pointers are used for many purposes in C. Text strings are commonly manipulated using pointers into arrays of characters. Dynamic memory allocation is performed using pointers; the result of a malloc is usually cast to the data type of the data to be stored. Many data types, such as trees, are commonly implemented as dynamically allocated struct objects linked together using pointers. Pointers to other pointers are often used in multi-dimensional arrays and arrays of struct objects. Pointers to functions (function pointers) are useful for passing functions as arguments to higher-order functions (such as qsort or bsearch), in dispatch tables, or as callbacks to event handlers.
A null pointer value explicitly points to no valid location. Dereferencing a null pointer value is undefined, often resulting in a segmentation fault. Null pointer values are useful for indicating special cases such as no "next" pointer in the final node of a linked list, or as an error indication from functions returning pointers. In appropriate contexts in source code, such as for assigning to a pointer variable, a null pointer constant can be written as 0, with or without explicit casting to a pointer type, as the NULL macro defined by several standard headers or, since C23 with the constant nullptr. In conditional contexts, null pointer values evaluate to false, while all other pointer values evaluate to true.
Void pointers (void *) point to objects of unspecified type, and can therefore be used as "generic" data pointers. Since the size and type of the pointed-to object is not known, void pointers cannot be dereferenced, nor is pointer arithmetic on them allowed, although they can easily be (and in many contexts implicitly are) converted to and from any other object pointer type.
Careless use of pointers is potentially dangerous. Because they are typically unchecked, a pointer variable can be made to point to any arbitrary location, which can cause undesirable effects. Although properly used pointers point to safe places, they can be made to point to unsafe places by using invalid pointer arithmetic; the objects they point to may continue to be used after deallocation (dangling pointers); they may be used without having been initialized (wild pointers); or they may be directly assigned an unsafe value using a cast, union, or through another corrupt pointer. In general, C is permissive in allowing manipulation of and conversion between pointer types, although compilers typically provide options for various levels of checking. Some other programming languages address these problems by using more restrictive reference types.
=== Arrays ===
Array types in C are traditionally of a fixed, static size specified at compile time. The more recent C99 standard also allows a form of variable-length arrays. However, it is also possible to allocate a block of memory (of arbitrary size) at run-time, using the standard library's malloc function, and treat it as an array.
Since arrays are always accessed (in effect) via pointers, array accesses are typically not checked against the underlying array size, although some compilers may provide bounds checking as an option. Array bounds violations are therefore possible and can lead to various repercussions, including illegal memory accesses, corruption of data, buffer overruns, and run-time exceptions.
C does not have a special provision for declaring multi-dimensional arrays, but rather relies on recursion within the type system to declare arrays of arrays, which effectively accomplishes the same thing. The index values of the resulting "multi-dimensional array" can be thought of as increasing in row-major order. Multi-dimensional arrays are commonly used in numerical algorithms (mainly from applied linear algebra) to store matrices. The structure of the C array is well suited to this particular task. However, in early versions of C the bounds of the array must be known fixed values or else explicitly passed to any subroutine that requires them, and dynamically sized arrays of arrays cannot be accessed using double indexing. (A workaround for this was to allocate the array with an additional "row vector" of pointers to the columns.) C99 introduced "variable-length arrays" which address this issue.
The following example using modern C (C99 or later) shows allocation of a two-dimensional array on the heap and the use of multi-dimensional array indexing for accesses (which can use bounds-checking on many C compilers):
And here is a similar implementation using C99's Auto VLA feature:
=== Array–pointer interchangeability ===
The subscript notation x[i] (where x designates a pointer) is syntactic sugar for *(x+i). Taking advantage of the compiler's knowledge of the pointer type, the address that x + i points to is not the base address (pointed to by x) incremented by i bytes, but rather is defined to be the base address incremented by i multiplied by the size of an element that x points to. Thus, x[i] designates the i+1th element of the array.
Furthermore, in most expression contexts (a notable exception is as operand of sizeof), an expression of array type is automatically converted to a pointer to the array's first element. This implies that an array is never copied as a whole when named as an argument to a function, but rather only the address of its first element is passed. Therefore, although function calls in C use pass-by-value semantics, arrays are in effect passed by reference.
The total size of an array x can be determined by applying sizeof to an expression of array type. The size of an element can be determined by applying the operator sizeof to any dereferenced element of an array A, as in n = sizeof A[0]. Thus, the number of elements in a declared array A can be determined as sizeof A / sizeof A[0]. Note, that if only a pointer to the first element is available as it is often the case in C code because of the automatic conversion described above, the information about the full type of the array and its length are lost.
== Memory management ==
One of the most important functions of a programming language is to provide facilities for managing memory and the objects that are stored in memory. C provides three principal ways to allocate memory for objects:
Static memory allocation: space for the object is provided in the binary at compile-time; these objects have an extent (or lifetime) as long as the binary which contains them is loaded into memory.
Automatic memory allocation: temporary objects can be stored on the stack, and this space is automatically freed and reusable after the block in which they are declared is exited.
Dynamic memory allocation: blocks of memory of arbitrary size can be requested at run-time using library functions such as malloc from a region of memory called the heap; these blocks persist until subsequently freed for reuse by calling the library function realloc or free.
These three approaches are appropriate in different situations and have various trade-offs. For example, static memory allocation has little allocation overhead, automatic allocation may involve slightly more overhead, and dynamic memory allocation can potentially have a great deal of overhead for both allocation and deallocation. The persistent nature of static objects is useful for maintaining state information across function calls, automatic allocation is easy to use but stack space is typically much more limited and transient than either static memory or heap space, and dynamic memory allocation allows convenient allocation of objects whose size is known only at run-time. Most C programs make extensive use of all three.
Where possible, automatic or static allocation is usually simplest because the storage is managed by the compiler, freeing the programmer of the potentially error-prone chore of manually allocating and releasing storage. However, many data structures can change in size at runtime, and since static allocations (and automatic allocations before C99) must have a fixed size at compile-time, there are many situations in which dynamic allocation is necessary. Prior to the C99 standard, variable-sized arrays were a common example of this. (See the article on C dynamic memory allocation for an example of dynamically allocated arrays.) Unlike automatic allocation, which can fail at run time with uncontrolled consequences, the dynamic allocation functions return an indication (in the form of a null pointer value) when the required storage cannot be allocated. (Static allocation that is too large is usually detected by the linker or loader, before the program can even begin execution.)
Unless otherwise specified, static objects contain zero or null pointer values upon program startup. Automatically and dynamically allocated objects are initialized only if an initial value is explicitly specified; otherwise they initially have indeterminate values (typically, whatever bit pattern happens to be present in the storage, which might not even represent a valid value for that type). If the program attempts to access an uninitialized value, the results are undefined. Many modern compilers try to detect and warn about this problem, but both false positives and false negatives can occur.
Heap memory allocation has to be synchronized with its actual usage in any program to be reused as much as possible. For example, if the only pointer to a heap memory allocation goes out of scope or has its value overwritten before it is deallocated explicitly, then that memory cannot be recovered for later reuse and is essentially lost to the program, a phenomenon known as a memory leak. Conversely, it is possible for memory to be freed, but is referenced subsequently, leading to unpredictable results. Typically, the failure symptoms appear in a portion of the program unrelated to the code that causes the error, making it difficult to diagnose the failure. Such issues are ameliorated in languages with automatic garbage collection.
== Libraries ==
The C programming language uses libraries as its primary method of extension. In C, a library is a set of functions contained within a single "archive" file. Each library typically has a header file, which contains the prototypes of the functions contained within the library that may be used by a program, and declarations of special data types and macro symbols used with these functions. For a program to use a library, it must include the library's header file, and the library must be linked with the program, which in many cases requires compiler flags (e.g., -lm, shorthand for "link the math library").
The most common C library is the C standard library, which is specified by the ISO and ANSI C standards and comes with every C implementation (implementations which target limited environments such as embedded systems may provide only a subset of the standard library). This library supports stream input and output, memory allocation, mathematics, character strings, and time values. Several separate standard headers (for example, stdio.h) specify the interfaces for these and other standard library facilities.
Another common set of C library functions are those used by applications specifically targeted for Unix and Unix-like systems, especially functions which provide an interface to the kernel. These functions are detailed in various standards such as POSIX and the Single UNIX Specification.
Since many programs have been written in C, there are a wide variety of other libraries available. Libraries are often written in C because C compilers generate efficient object code; programmers then create interfaces to the library so that the routines can be used from higher-level languages like Java, Perl, and Python.
=== File handling and streams ===
File input and output (I/O) is not part of the C language itself but instead is handled by libraries (such as the C standard library) and their associated header files (e.g. stdio.h). File handling is generally implemented through high-level I/O which works through streams. A stream is from this perspective a data flow that is independent of devices, while a file is a concrete device. The high-level I/O is done through the association of a stream to a file. In the C standard library, a buffer (a memory area or queue) is temporarily used to store data before it is sent to the final destination. This reduces the time spent waiting for slower devices, for example a hard drive or solid-state drive. Low-level I/O functions are not part of the standard C library but are generally part of "bare metal" programming (programming that is independent of any operating system such as most embedded programming). With few exceptions, implementations include low-level I/O.
== Language tools ==
A number of tools have been developed to help C programmers find and fix statements with undefined behavior or possibly erroneous expressions, with greater rigor than that provided by the compiler.
Automated source code checking and auditing tools exist, such as Lint. A common practice is to use Lint to detect questionable code when a program is first written. Once a program passes Lint, it is then compiled using the C compiler. Also, many compilers can optionally warn about syntactically valid constructs that are likely to actually be errors. MISRA C is a proprietary set of guidelines to avoid such questionable code, developed for embedded systems.
There are also compilers, libraries, and operating system level mechanisms for performing actions that are not a standard part of C, such as bounds checking for arrays, detection of buffer overflow, serialization, dynamic memory tracking, and automatic garbage collection.
Memory management checking tools like Purify or Valgrind and linking with libraries containing special versions of the memory allocation functions can help uncover runtime errors in memory usage.
== Uses ==
=== Rationale for use in systems programming ===
C is widely used for systems programming in implementing operating systems and embedded system applications. This is for several reasons:
The C language permits platform hardware and memory to be accessed with pointers and type punning, so system-specific features (e.g. Control/Status Registers, I/O registers) can be configured and used with code written in C – it allows fullest control of the platform it is running on.
The code generated after compilation does not demand many system features, and can be invoked from some boot code in a straightforward manner – it is simple to execute.
The C language statements and expressions typically map well on to sequences of instructions for the target processor, and consequently there is a low run-time demand on system resources – it is fast to execute.
With its rich set of operators, the C language can use many of the features of target CPUs. Where a particular CPU has more esoteric instructions, a language variant can be constructed with perhaps intrinsic functions to exploit those instructions – it can use practically all the target CPU's features.
The language makes it easy to overlay structures onto blocks of binary data, allowing the data to be comprehended, navigated and modified – it can write data structures, even file systems.
The language supports a rich set of operators, including bit manipulation, for integer arithmetic and logic, and perhaps different sizes of floating point numbers – it can process appropriately-structured data effectively.
C is a fairly small language, with only a handful of statements, and without too many features that generate extensive target code – it is comprehensible.
C has direct control over memory allocation and deallocation, which gives reasonable efficiency and predictable timing to memory-handling operations, without any concerns for sporadic stop-the-world garbage collection events – it has predictable performance.
C permits the use and implementation of different memory allocation schemes, including a typical malloc and free; a more sophisticated mechanism with arenas; or a version for an OS kernel that may suit DMA, use within interrupt handlers, or integrated with the virtual memory system.
Depending on the linker and environment, C code can also call libraries written in assembly language, and may be called from assembly language – it interoperates well with other lower-level code.
C and its calling conventions and linker structures are commonly used in conjunction with other high-level languages, with calls both to C and from C supported – it interoperates well with other high-level code.
C has a very mature and broad ecosystem, including libraries, frameworks, open source compilers, debuggers and utilities, and is the de facto standard. It is likely the drivers already exist in C, or that there is a similar CPU architecture as a back-end of a C compiler, so there is reduced incentive to choose another language.
=== Used for computationally-intensive libraries ===
C enables programmers to create efficient implementations of algorithms and data structures, because the layer of abstraction from hardware is thin, and its overhead is low, an important criterion for computationally intensive programs. For example, the GNU Multiple Precision Arithmetic Library, the GNU Scientific Library, Mathematica, and MATLAB are completely or partially written in C. Many languages support calling library functions in C, for example, the Python-based framework NumPy uses C for the high-performance and hardware-interacting aspects.
=== Games ===
Computer games are often built from a combination of languages. C has featured significantly, especially for those games attempting to obtain best performance from computer platforms. Examples include Doom from 1993.
=== C as an intermediate language ===
C is sometimes used as an intermediate language by implementations of other languages. This approach may be used for portability or convenience; by using C as an intermediate language, additional machine-specific code generators are not necessary. C has some features, such as line-number preprocessor directives and optional superfluous commas at the end of initializer lists, that support compilation of generated code. However, some of C's shortcomings have prompted the development of other C-based languages specifically designed for use as intermediate languages, such as C--. Also, contemporary major compilers GCC and LLVM both feature an intermediate representation that is not C, and those compilers support front ends for many languages including C.
=== Other languages written in C ===
A consequence of C's wide availability and efficiency is that compilers, libraries and interpreters of other programming languages are often implemented in C. For example, the reference implementations of Python, Perl, Ruby, and PHP are written in C.
=== Once used for web development ===
Historically, C was sometimes used for web development using the Common Gateway Interface (CGI) as a "gateway" for information between the web application, the server, and the browser. C may have been chosen over interpreted languages because of its speed, stability, and near-universal availability. It is no longer common practice for web development to be done in C, and many other web development languages are popular. Applications where C-based web development continues include the HTTP configuration pages on routers, IoT devices and similar, although even here some projects have parts in higher-level languages e.g. the use of Lua within OpenWRT.
=== Web servers ===
The two most popular web servers, Apache HTTP Server and Nginx, are both written in C. These web servers interact with the operating system, listen on TCP ports for HTTP requests, and then serve up static web content, or cause the execution of other languages handling to 'render' content such as PHP, which is itself primarily written in C. C's close-to-the-metal approach allows for the construction of these high-performance software systems.
=== End-user applications ===
C has also been widely used to implement end-user applications. However, such applications can also be written in newer, higher-level languages.
== Limitations ==
the power of assembly language and the convenience of ... assembly language
While C has been popular, influential and hugely successful, it has drawbacks, including:
The standard dynamic memory handling with malloc and free is error prone. Improper use can lead to memory leaks and dangling pointers.
The use of pointers and the direct manipulation of memory means corruption of memory is possible, perhaps due to programmer error, or insufficient checking of bad data.
There is some type checking, but it does not apply to areas like variadic functions, and the type checking can be trivially or inadvertently circumvented. It is weakly typed.
Since the code generated by the compiler contains few checks itself, there is a burden on the programmer to consider all possible outcomes, to protect against buffer overruns, array bounds checking, stack overflows, memory exhaustion, and consider race conditions, thread isolation, etc.
The use of pointers and the run-time manipulation of these means there may be two ways to access the same data (aliasing), which is not determinable at compile time. This means that some optimisations that may be available to other languages are not possible in C. FORTRAN is considered faster.
Some of the standard library functions, e.g. scanf or strncat, can lead to buffer overruns.
There is limited standardisation in support for low-level variants in generated code, for example: different function calling conventions and ABI; different structure packing conventions; different byte ordering within larger integers (including endianness). In many language implementations, some of these options may be handled with the preprocessor directive #pragma, and some with additional keywords e.g. use __cdecl calling convention. The directive and options are not consistently supported.
String handling using the standard library is code-intensive, with explicit memory management required.
The language does not directly support object orientation, introspection, run-time expression evaluation, generics, etc.
There are few guards against inappropriate use of language features, which may lead to unmaintainable code. In particular, the C preprocessor can hide troubling effects such as double evaluation and worse. This facility for tricky code has been celebrated with competitions such as the International Obfuscated C Code Contest and the Underhanded C Contest.
C lacks standard support for exception handling and only offers return codes for error checking. The setjmp and longjmp standard library functions have been used to implement a try-catch mechanism via macros.
For some purposes, restricted styles of C have been adopted, e.g. MISRA C or CERT C, in an attempt to reduce the opportunity for bugs. Databases such as CWE attempt to count the ways C etc. has vulnerabilities, along with recommendations for mitigation.
There are tools that can mitigate against some of the drawbacks. Contemporary C compilers include checks which may generate warnings to help identify many potential bugs.
== Related languages ==
C has both directly and indirectly influenced many later languages such as C++ and Java. The most pervasive influence has been syntactical; all of the languages mentioned combine the statement and (more or less recognizably) expression syntax of C with type systems, data models or large-scale program structures that differ from those of C, sometimes radically.
Several C or near-C interpreters exist, including Ch and CINT, which can also be used for scripting.
When object-oriented programming languages became popular, C++ and Objective-C were two different extensions of C that provided object-oriented capabilities. Both languages were originally implemented as source-to-source compilers; source code was translated into C, and then compiled with a C compiler.
The C++ programming language (originally named "C with Classes") was devised by Bjarne Stroustrup as an approach to providing object-oriented functionality with a C-like syntax. C++ adds greater typing strength, scoping, and other tools useful in object-oriented programming, and permits generic programming via templates. Nearly a superset of C, C++ now supports most of C, with a few exceptions.
Objective-C was originally a very "thin" layer on top of C, and remains a strict superset of C that permits object-oriented programming using a hybrid dynamic/static typing paradigm. Objective-C derives its syntax from both C and Smalltalk: syntax that involves preprocessing, expressions, function declarations, and function calls is inherited from C, while the syntax for object-oriented features was originally taken from Smalltalk.
In addition to C++ and Objective-C, Ch, Cilk, and Unified Parallel C are nearly supersets of C.
== See also ==
Compatibility of C and C++
Comparison of Pascal and C
Comparison of programming languages
International Obfuscated C Code Contest
List of C-family programming languages
List of C compilers
== Notes ==
== References ==
== Sources ==
Ritchie, Dennis M. (March 1993). "The Development of the C Language". ACM SIGPLAN Notices. 28 (3). ACM: 201–208. doi:10.1145/155360.155580.
By courtesy of the author, also at Ritchie, Dennis M. "Chistory". www.bell-labs.com. Retrieved March 29, 2022.
Ritchie, Dennis M. (1993). "The Development of the C Language". The Second ACM SIGPLAN Conference on History of Programming Languages (HOPL-II). ACM. pp. 201–208. doi:10.1145/154766.155580. ISBN 0-89791-570-4. Archived from the original on April 11, 2019. Retrieved November 4, 2014.
Kernighan, Brian W.; Ritchie, Dennis M. (1988). The C Programming Language (2nd ed.). Prentice Hall. ISBN 0-13-110362-8.
== Further reading ==
Plauger, P.J. (1992). The Standard C Library (1 ed.). Prentice Hall. ISBN 978-0131315099. (source)
Banahan, M.; Brady, D.; Doran, M. (1991). The C Book: Featuring the ANSI C Standard (2 ed.). Addison-Wesley. ISBN 978-0201544336. (free)
Feuer, Alan R. (1985). The C Puzzle Book (1 ed.). Prentice Hall. ISBN 0131099345.
Harbison, Samuel; Steele, Guy Jr. (2002). C: A Reference Manual (5 ed.). Pearson. ISBN 978-0130895929. (archive)
King, K.N. (2008). C Programming: A Modern Approach (2 ed.). W. W. Norton. ISBN 978-0393979503. (archive)
Griffiths, David; Griffiths, Dawn (2012). Head First C (1 ed.). O'Reilly. ISBN 978-1449399917.
Perry, Greg; Miller, Dean (2013). C Programming: Absolute Beginner's Guide (3 ed.). Que. ISBN 978-0789751980.
Deitel, Paul; Deitel, Harvey (2015). C: How to Program (8 ed.). Pearson. ISBN 978-0133976892.
Gustedt, Jens (2019). Modern C (2 ed.). Manning. ISBN 978-1617295812. (free)
== External links ==
ISO C Working Group official website
ISO/IEC 9899, publicly available official C documents, including the C99 Rationale
"C99 with Technical corrigenda TC1, TC2, and TC3 included" (PDF). Archived (PDF) from the original on October 25, 2007. (3.61 MB)
comp.lang.c Frequently Asked Questions
A History of C, by Dennis Ritchie
C Library Reference and Examples
|
https://en.wikipedia.org/wiki/C_(programming_language)
|
In computer programming, a programming idiom, code idiom or simply idiom is a code fragment having a semantic role which recurs frequently across software projects. It often expresses a special feature of a recurring construct in one or more programming languages, frameworks or libraries. This definition is rooted in the linguistic definition of "idiom".
The idiom can be seen by developers as an action on a programming concept underlying a pattern in code, which is represented in implementation by contiguous or scattered code snippets. Generally speaking, a programming idiom's semantic role is a natural language expression of a simple task, algorithm, or data structure that is not a built-in feature in the programming language being used, or, conversely, the use of an unusual or notable feature that is built into a programming language.
Knowing the idioms associated with a programming language and how to use them is an important part of gaining fluency in that language. It also helps to transfer knowledge in the form of analogies from one language or framework to another. Such idiomatic knowledge is widely used in crowdsourced repositories to help developers overcome programming barriers.
Mapping code idioms to idiosyncrasies can be a helpful way to navigate the tradeoffs between generalization and specificity. By identifying common patterns and idioms, developers can create mental models and schemata that help them quickly understand and navigate new code. Furthermore, by mapping these idioms to idiosyncrasies and specific use cases, developers can ensure that they are applying the correct approach and not overgeneralizing it.
One way to do this is by creating a reference or documentation that maps common idioms to specific use cases, highlighting where they may need to be adapted or modified to fit a particular project or development team. This can help ensure that developers are working with a shared understanding of best practices and can make informed decisions about when to use established idioms and when to adapt them to fit their specific needs.
A common misconception is to use the adverbial or adjectival form of the term as "using a programming language in a typical way", which really refers to a idiosyncrasy. An idiom implies the semantics of some code in a programming language has similarities to other languages or frameworks. For example, an idiosyncratic way to manage dynamic memory in C would be to use the C standard library functions malloc and free, whereas idiomatic refers to manual memory management as recurring semantic role that can be achieved with code fragments malloc in C, or pointer = new type [number_of_elements] in C++. In both cases, the semantics of the code are intelligible to developers familiar with C or C++, once the idiomatic or idiosyncratic rationale is exposed to them. However, while idiomatic rationale is often general to the programming domain, idiosyncratic rationale is frequently tied to specific API terminology.
== Examples ==
=== Printing Hello World ===
One of the most common starting points to learn to program or notice the syntax differences between a known language and a new one.
It has several implementations, among them the code fragments for C++:
For Java:
=== Inserting an element in an array ===
This idiom helps developers understand how to manipulate collections in a given language, particularly inserting an element x at a position i in a list s and moving the elements to its right.
Code fragments:
For Python:
For JavaScript:
For Perl:
== See also ==
Algorithmic skeleton
Embedded SQL
Idiom
== References ==
== External links ==
programming-idioms.org shows short idioms implementations in most mainstream languages.
C++ programming idioms from Wikibooks.
|
https://en.wikipedia.org/wiki/Programming_idiom
|
In computer science, reflective programming or reflection is the ability of a process to examine, introspect, and modify its own structure and behavior.
== Historical background ==
The earliest computers were programmed in their native assembly languages, which were inherently reflective, as these original architectures could be programmed by defining instructions as data and using self-modifying code. As the bulk of programming moved to higher-level compiled languages such as ALGOL, COBOL, Fortran, Pascal, and C, this reflective ability largely disappeared until new programming languages with reflection built into their type systems appeared.
Brian Cantwell Smith's 1982 doctoral dissertation introduced the notion of computational reflection in procedural programming languages and the notion of the meta-circular interpreter as a component of 3-Lisp.
== Uses ==
Reflection helps programmers make generic software libraries to display data, process different formats of data, perform serialization and deserialization of data for communication, or do bundling and unbundling of data for containers or bursts of communication.
Effective use of reflection almost always requires a plan: A design framework, encoding description, object library, a map of a database or entity relations.
Reflection makes a language more suited to network-oriented code. For example, it assists languages such as Java to operate well in networks by enabling libraries for serialization, bundling and varying data formats. Languages without reflection such as C are required to use auxiliary compilers for tasks like Abstract Syntax Notation to produce code for serialization and bundling.
Reflection can be used for observing and modifying program execution at runtime. A reflection-oriented program component can monitor the execution of an enclosure of code and can modify itself according to a desired goal of that enclosure. This is typically accomplished by dynamically assigning program code at runtime.
In object-oriented programming languages such as Java, reflection allows inspection of classes, interfaces, fields and methods at runtime without knowing the names of the interfaces, fields, methods at compile time. It also allows instantiation of new objects and invocation of methods.
Reflection is often used as part of software testing, such as for the runtime creation/instantiation of mock objects.
Reflection is also a key strategy for metaprogramming.
In some object-oriented programming languages such as C# and Java, reflection can be used to bypass member accessibility rules. For C#-properties this can be achieved by writing directly onto the (usually invisible) backing field of a non-public property. It is also possible to find non-public methods of classes and types and manually invoke them. This works for project-internal files as well as external libraries such as .NET's assemblies and Java's archives.
== Implementation ==
A language that supports reflection provides a number of features available at runtime that would otherwise be difficult to accomplish in a lower-level language. Some of these features are the abilities to:
Discover and modify source-code constructions (such as code blocks, classes, methods, protocols, etc.) as first-class objects at runtime.
Convert a string matching the symbolic name of a class or function into a reference to or invocation of that class or function.
Evaluate a string as if it were a source-code statement at runtime.
Create a new interpreter for the language's bytecode to give a new meaning or purpose for a programming construct.
These features can be implemented in different ways. In MOO, reflection forms a natural part of everyday programming idiom. When verbs (methods) are called, various variables such as verb (the name of the verb being called) and this (the object on which the verb is called) are populated to give the context of the call. Security is typically managed by accessing the caller stack programmatically: Since callers() is a list of the methods by which the current verb was eventually called, performing tests on callers()[0] (the command invoked by the original user) allows the verb to protect itself against unauthorised use.
Compiled languages rely on their runtime system to provide information about the source code. A compiled Objective-C executable, for example, records the names of all methods in a block of the executable, providing a table to correspond these with the underlying methods (or selectors for these methods) compiled into the program. In a compiled language that supports runtime creation of functions, such as Common Lisp, the runtime environment must include a compiler or an interpreter.
Reflection can be implemented for languages without built-in reflection by using a program transformation system to define automated source-code changes.
== Security considerations ==
Reflection may allow a user to create unexpected control flow paths through an application, potentially bypassing security measures. This may be exploited by attackers. Historical vulnerabilities in Java caused by unsafe reflection allowed code retrieved from potentially untrusted remote machines to break out of the Java sandbox security mechanism. A large scale study of 120 Java vulnerabilities in 2013 concluded that unsafe reflection is the most common vulnerability in Java, though not the most exploited.
== Examples ==
The following code snippets create an instance foo of class Foo and invoke its method PrintHello. For each programming language, normal and reflection-based call sequences are shown.
=== Common Lisp ===
The following is an example in Common Lisp using the Common Lisp Object System:
=== C# ===
The following is an example in C#:
=== Delphi, Object Pascal ===
This Delphi and Object Pascal example assumes that a TFoo class has been declared in a unit called Unit1:
=== eC ===
The following is an example in eC:
=== Go ===
The following is an example in Go:
=== Java ===
The following is an example in Java:
=== JavaScript ===
The following is an example in JavaScript:
=== Julia ===
The following is an example in Julia:
=== Objective-C ===
The following is an example in Objective-C, implying either the OpenStep or Foundation Kit framework is used:
=== Perl ===
The following is an example in Perl:
=== PHP ===
The following is an example in PHP:
=== Python ===
The following is an example in Python:
=== R ===
The following is an example in R:
=== Ruby ===
The following is an example in Ruby:
=== Xojo ===
The following is an example using Xojo:
== See also ==
List of reflective programming languages and platforms
Mirror (programming)
Programming paradigms
Self-hosting (compilers)
Self-modifying code
Type introspection
typeof
== References ==
=== Citations ===
=== Sources ===
== Further reading ==
Ira R. Forman and Nate Forman, Java Reflection in Action (2005), ISBN 1-932394-18-4
Ira R. Forman and Scott Danforth, Putting Metaclasses to Work (1999), ISBN 0-201-43305-2
== External links ==
Reflection in logic, functional and object-oriented programming: a short comparative study
An Introduction to Reflection-Oriented Programming
Brian Foote's pages on Reflection in Smalltalk
Java Reflection API Tutorial from Oracle
|
https://en.wikipedia.org/wiki/Reflective_programming
|
In computer science, extensible programming is a style of computer programming that focuses on mechanisms to extend the programming language, compiler, and runtime system (environment). Extensible programming languages, supporting this style of programming, were an active area of work in the 1960s, but the movement was marginalized in the 1970s. Extensible programming has become a topic of renewed interest in the 21st century.
== Historical movement ==
The first paper usually associated with the extensible programming language movement is M. Douglas McIlroy's 1960 paper on macros for high-level programming languages. Another early description of the principle of extensibility occurs in Brooker and Morris's 1960 paper on the compiler-compiler. The peak of the movement was marked by two academic symposia, in 1969 and 1971. By 1975, a survey article on the movement by Thomas A. Standish was essentially a post mortem. The Forth was an exception, but it went essentially unnoticed.
=== Character of the historical movement ===
As typically envisioned, an extensible language consisted of a base language providing elementary computing facilities, and a metalanguage able to modify the base language. A program then consisted of metalanguage modifications and code in the modified base language.
The most prominent language-extension technique used in the movement was macro definition. Grammar modification was also closely associated with the movement, resulting in the eventual development of adaptive grammar formalisms. The Lisp language community remained separate from the extensible language community, apparently because, as one researcher observed,
any programming language in which programs and data are essentially interchangeable can be regarded as an extendible [sic] language. ... this can be seen very easily from the fact that Lisp has been used as an extendible language for years.
At the 1969 conference, Simula was presented as an extensible language.
Standish described three classes of language extension, which he named paraphrase, orthophrase, and metaphrase (otherwise paraphrase and metaphrase being translation terms).
Paraphrase defines a facility by showing how to exchange it for something formerly defined (or to be defined). As examples, he mentions macro definitions, ordinary procedure definitions, grammatical extensions, data definitions, operator definitions, and control structure extensions.
Orthophrase adds features to a language that could not be achieved using the base language, such as adding an input/output (I/O) system to a base language formerly with no I/O primitives. Extensions must be understood as orthophrase relative to some given base language, since a feature not defined in terms of the base language must be defined in terms of some other language. This corresponds to the modern notion of plug-ins.
Metaphrase modifies the interpretation rules used for pre-existing expressions. This corresponds to the modern notion of reflective programming (reflection).
=== Death of the historical movement ===
Standish attributed the failure of the extensibility movement to the difficulty of programming successive extensions. A programmer might build a first shell of macros around a base language. Then, if a second shell of macros is built around that, any subsequent programmer must be intimately familiar with both the base language, and the first shell. A third shell would require familiarity with the base and both the first and second shells, and so on. Shielding a programmer from lower-level details is the intent of the abstraction movement that supplanted the extensibility movement.
Despite the earlier presentation of Simula as extensible, by 1975, Standish's survey does not seem in practice to have included the newer abstraction-based technologies (though he used a very general definition of extensibility that technically could have included them). A 1978 history of programming abstraction from the invention of the computer until then, made no mention of macros, and gave no hint that the extensible languages movement had ever occurred. Macros were tentatively admitted into the abstraction movement by the late 1980s (perhaps due to the advent of hygienic macros), by being granted the pseudonym syntactic abstractions.
== Modern movement ==
In the modern sense, a system that supports extensible programming will provide all of the features described below.
=== Extensible syntax ===
This simply means that the source language(s) to be compiled must not be closed, fixed, or static. It must be possible to add new keywords, concepts, and structures to the source language(s). Languages which allow the addition of constructs with user defined syntax include Coq, Racket, Camlp4, OpenC++, Seed7, Red, Rebol, and Felix. While it is acceptable for some fundamental and intrinsic language features to be immutable, the system must not rely solely on those language features. It must be possible to add new ones.
=== Extensible compiler ===
In extensible programming, a compiler is not a monolithic program that converts source code input into binary executable output. The compiler itself must be extensible to the point that it is really a collection of plugins that assist with the translation of source language input into anything. For example, an extensible compiler will support the generation of object code, code documentation, re-formatted source code, or any other desired output. The architecture of the compiler must permit its users to "get inside" the compilation process and provide alternative processing tasks at every reasonable step in the compilation process.
For just the task of translating source code into something that can be executed on a computer, an extensible compiler should:
use a plug-in or component architecture for nearly every aspect of its function
determine which language or language variant is being compiled and locate the appropriate plug-in to recognize and validate that language
use formal language specifications to syntactically and structurally validate arbitrary source languages
assist with the semantic validation of arbitrary source languages by invoking an appropriate validation plug-in
allow users to select from different kinds of code generators so that the resulting executable can be targeted for different processors, operating systems, virtual machines, or other execution environment.
provide facilities for error generation and extensions to it
allow new kinds of nodes in the abstract syntax tree (AST),
allow new values in nodes of the AST,
allow new kinds of edges between nodes,
support the transformation of the input AST, or portions thereof, by some external "pass"
support the translation of the input AST, or portions thereof, into another form by some external "pass"
assist with the flow of information between internal and external passes as they both transform and translate the AST into new ASTs or other representations
=== Extensible runtime ===
At runtime, extensible programming systems must permit languages to extend the set of operations that it permits. For example, if the system uses a byte-code interpreter, it must allow new byte-code values to be defined. As with extensible syntax, it is acceptable for there to be some (smallish) set of fundamental or intrinsic operations that are immutable. However, it must be possible to overload or augment those intrinsic operations so that new or additional behavior can be supported.
=== Content separated from form ===
Extensible programming systems should regard programs as data to be processed. Those programs should be completely devoid of any kind of formatting information. The visual display and editing of programs to users should be a translation function, supported by the extensible compiler, that translates the program data into forms more suitable for viewing or editing. Naturally, this should be a two-way translation. This is important because it must be possible to easily process extensible programs in a variety of ways. It is unacceptable for the only uses of source language input to be editing, viewing and translation to machine code. The arbitrary processing of programs is facilitated by de-coupling the source input from specifications of how it should be processed (formatted, stored, displayed, edited, etc.).
=== Source language debugging support ===
Extensible programming systems must support the debugging of programs using the constructs of the original source language regardless of the extensions or transformation the program has undergone in order to make it executable. Most notably, it cannot be assumed that the only way to display runtime data is in structures or arrays. The debugger, or more correctly 'program inspector', must permit the display of runtime data in forms suitable to the source language. For example, if the language supports a data structure for a business process or work flow, it must be possible for the debugger to display that data structure as a fishbone chart or other form provided by a plugin.
== Examples ==
Camlp4
Felix
Nemerle
Seed7
Rebol
Red
Ruby (metaprogramming)
IMP
OpenC++
XL
XML
Forth
Lisp
Racket
Scheme
Lua
PL/I
Smalltalk
== See also ==
Adaptive grammar
Concept programming
Dialecting
Grammar-oriented programming
Language-oriented programming
Homoiconicity
== References ==
== External links ==
=== General ===
Greg Wilson's Article in ACM Queue
Slashdot Discussion
Modern Extensible Languages Archived 2011-06-12 at the Wayback Machine – A paper from Daniel Zingaro
=== Tools ===
MetaL – an extensible programming compiler engine implementation
XPS – eXtensible Programming System (in development)
MPS – JetBrains Metaprogramming system
=== Languages with extensible syntax ===
OpenZz
xtc – eXTensible C
English-script
Nemerle Macros
Boo Syntactic Macros
Stanford University Intermediate Format compiler
Seed7 – The extensible programming language
Katahdin – a language with syntax and semantics that are mutable at runtime
π – a language with extensible syntax, implemented using an Earley parser
|
https://en.wikipedia.org/wiki/Extensible_programming
|
In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that map values to other values, rather than a sequence of imperative statements which update the running state of the program.
In functional programming, functions are treated as first-class citizens, meaning that they can be bound to names (including local identifiers), passed as arguments, and returned from other functions, just as any other data type can. This allows programs to be written in a declarative and composable style, where small functions are combined in a modular manner.
Functional programming is sometimes treated as synonymous with purely functional programming, a subset of functional programming that treats all functions as deterministic mathematical functions, or pure functions. When a pure function is called with some given arguments, it will always return the same result, and cannot be affected by any mutable state or other side effects. This is in contrast with impure procedures, common in imperative programming, which can have side effects (such as modifying the program's state or taking input from a user). Proponents of purely functional programming claim that by restricting side effects, programs can have fewer bugs, be easier to debug and test, and be more suited to formal verification.
Functional programming has its roots in academia, evolving from the lambda calculus, a formal system of computation based only on functions. Functional programming has historically been less popular than imperative programming, but many functional languages are seeing use today in industry and education, including Common Lisp, Scheme, Clojure, Wolfram Language, Racket, Erlang, Elixir, OCaml, Haskell, and F#. Lean is a functional programming language commonly used for verifying mathematical theorems. Functional programming is also key to some languages that have found success in specific domains, like JavaScript in the Web, R in statistics, J, K and Q in financial analysis, and XQuery/XSLT for XML. Domain-specific declarative languages like SQL and Lex/Yacc use some elements of functional programming, such as not allowing mutable values. In addition, many other programming languages support programming in a functional style or have implemented features from functional programming, such as C++11, C#, Kotlin, Perl, PHP, Python, Go, Rust, Raku, Scala, and Java (since Java 8).
== History ==
The lambda calculus, developed in the 1930s by Alonzo Church, is a formal system of computation built from function application. In 1937 Alan Turing proved that the lambda calculus and Turing machines are equivalent models of computation, showing that the lambda calculus is Turing complete. Lambda calculus forms the basis of all functional programming languages. An equivalent theoretical formulation, combinatory logic, was developed by Moses Schönfinkel and Haskell Curry in the 1920s and 1930s.
Church later developed a weaker system, the simply typed lambda calculus, which extended the lambda calculus by assigning a data type to all terms. This forms the basis for statically typed functional programming.
The first high-level functional programming language, Lisp, was developed in the late 1950s for the IBM 700/7000 series of scientific computers by John McCarthy while at Massachusetts Institute of Technology (MIT). Lisp functions were defined using Church's lambda notation, extended with a label construct to allow recursive functions. Lisp first introduced many paradigmatic features of functional programming, though early Lisps were multi-paradigm languages, and incorporated support for numerous programming styles as new paradigms evolved. Later dialects, such as Scheme and Clojure, and offshoots such as Dylan and Julia, sought to simplify and rationalise Lisp around a cleanly functional core, while Common Lisp was designed to preserve and update the paradigmatic features of the numerous older dialects it replaced.
Information Processing Language (IPL), 1956, is sometimes cited as the first computer-based functional programming language. It is an assembly-style language for manipulating lists of symbols. It does have a notion of generator, which amounts to a function that accepts a function as an argument, and, since it is an assembly-level language, code can be data, so IPL can be regarded as having higher-order functions. However, it relies heavily on the mutating list structure and similar imperative features.
Kenneth E. Iverson developed APL in the early 1960s, described in his 1962 book A Programming Language (ISBN 9780471430148). APL was the primary influence on John Backus's FP. In the early 1990s, Iverson and Roger Hui created J. In the mid-1990s, Arthur Whitney, who had previously worked with Iverson, created K, which is used commercially in financial industries along with its descendant Q.
In the mid-1960s, Peter Landin invented SECD machine, the first abstract machine for a functional programming language, described a correspondence between ALGOL 60 and the lambda calculus, and proposed the ISWIM programming language.
John Backus presented FP in his 1977 Turing Award lecture "Can Programming Be Liberated From the von Neumann Style? A Functional Style and its Algebra of Programs". He defines functional programs as being built up in a hierarchical way by means of "combining forms" that allow an "algebra of programs"; in modern language, this means that functional programs follow the principle of compositionality. Backus's paper popularized research into functional programming, though it emphasized function-level programming rather than the lambda-calculus style now associated with functional programming.
The 1973 language ML was created by Robin Milner at the University of Edinburgh, and David Turner developed the language SASL at the University of St Andrews. Also in Edinburgh in the 1970s, Burstall and Darlington developed the functional language NPL. NPL was based on Kleene Recursion Equations and was first introduced in their work on program transformation. Burstall, MacQueen and Sannella then incorporated the polymorphic type checking from ML to produce the language Hope. ML eventually developed into several dialects, the most common of which are now OCaml and Standard ML.
In the 1970s, Guy L. Steele and Gerald Jay Sussman developed Scheme, as described in the Lambda Papers and the 1985 textbook Structure and Interpretation of Computer Programs. Scheme was the first dialect of lisp to use lexical scoping and to require tail-call optimization, features that encourage functional programming.
In the 1980s, Per Martin-Löf developed intuitionistic type theory (also called constructive type theory), which associated functional programs with constructive proofs expressed as dependent types. This led to new approaches to interactive theorem proving and has influenced the development of subsequent functional programming languages.
The lazy functional language, Miranda, developed by David Turner, initially appeared in 1985 and had a strong influence on Haskell. With Miranda being proprietary, Haskell began with a consensus in 1987 to form an open standard for functional programming research; implementation releases have been ongoing as of 1990.
More recently it has found use in niches such as parametric CAD in the OpenSCAD language built on the CGAL framework, although its restriction on reassigning values (all values are treated as constants) has led to confusion among users who are unfamiliar with functional programming as a concept.
Functional programming continues to be used in commercial settings.
== Concepts ==
A number of concepts and paradigms are specific to functional programming, and generally foreign to imperative programming (including object-oriented programming). However, programming languages often cater to several programming paradigms, so programmers using "mostly imperative" languages may have utilized some of these concepts.
=== First-class and higher-order functions ===
Higher-order functions are functions that can either take other functions as arguments or return them as results. In calculus, an example of a higher-order function is the differential operator
d
/
d
x
{\displaystyle d/dx}
, which returns the derivative of a function
f
{\displaystyle f}
.
Higher-order functions are closely related to first-class functions in that higher-order functions and first-class functions both allow functions as arguments and results of other functions. The distinction between the two is subtle: "higher-order" describes a mathematical concept of functions that operate on other functions, while "first-class" is a computer science term for programming language entities that have no restriction on their use (thus first-class functions can appear anywhere in the program that other first-class entities like numbers can, including as arguments to other functions and as their return values).
Higher-order functions enable partial application or currying, a technique that applies a function to its arguments one at a time, with each application returning a new function that accepts the next argument. This lets a programmer succinctly express, for example, the successor function as the addition operator partially applied to the natural number one.
=== Pure functions ===
Pure functions (or expressions) have no side effects (memory or I/O). This means that pure functions have several useful properties, many of which can be used to optimize the code:
If the result of a pure expression is not used, it can be removed without affecting other expressions.
If a pure function is called with arguments that cause no side-effects, the result is constant with respect to that argument list (sometimes called referential transparency or idempotence), i.e., calling the pure function again with the same arguments returns the same result. (This can enable caching optimizations such as memoization.)
If there is no data dependency between two pure expressions, their order can be reversed, or they can be performed in parallel and they cannot interfere with one another (in other terms, the evaluation of any pure expression is thread-safe).
If the entire language does not allow side-effects, then any evaluation strategy can be used; this gives the compiler freedom to reorder or combine the evaluation of expressions in a program (for example, using deforestation).
While most compilers for imperative programming languages detect pure functions and perform common-subexpression elimination for pure function calls, they cannot always do this for pre-compiled libraries, which generally do not expose this information, thus preventing optimizations that involve those external functions. Some compilers, such as gcc, add extra keywords for a programmer to explicitly mark external functions as pure, to enable such optimizations. Fortran 95 also lets functions be designated pure. C++11 added constexpr keyword with similar semantics.
=== Recursion ===
Iteration (looping) in functional languages is usually accomplished via recursion. Recursive functions invoke themselves, letting an operation be repeated until it reaches the base case. In general, recursion requires maintaining a stack, which consumes space in a linear amount to the depth of recursion. This could make recursion prohibitively expensive to use instead of imperative loops. However, a special form of recursion known as tail recursion can be recognized and optimized by a compiler into the same code used to implement iteration in imperative languages. Tail recursion optimization can be implemented by transforming the program into continuation passing style during compiling, among other approaches.
The Scheme language standard requires implementations to support proper tail recursion, meaning they must allow an unbounded number of active tail calls. Proper tail recursion is not simply an optimization; it is a language feature that assures users that they can use recursion to express a loop and doing so would be safe-for-space. Moreover, contrary to its name, it accounts for all tail calls, not just tail recursion. While proper tail recursion is usually implemented by turning code into imperative loops, implementations might implement it in other ways. For example, Chicken intentionally maintains a stack and lets the stack overflow. However, when this happens, its garbage collector will claim space back, allowing an unbounded number of active tail calls even though it does not turn tail recursion into a loop.
Common patterns of recursion can be abstracted away using higher-order functions, with catamorphisms and anamorphisms (or "folds" and "unfolds") being the most obvious examples. Such recursion schemes play a role analogous to built-in control structures such as loops in imperative languages.
Most general purpose functional programming languages allow unrestricted recursion and are Turing complete, which makes the halting problem undecidable, can cause unsoundness of equational reasoning, and generally requires the introduction of inconsistency into the logic expressed by the language's type system. Some special purpose languages such as Coq allow only well-founded recursion and are strongly normalizing (nonterminating computations can be expressed only with infinite streams of values called codata). As a consequence, these languages fail to be Turing complete and expressing certain functions in them is impossible, but they can still express a wide class of interesting computations while avoiding the problems introduced by unrestricted recursion. Functional programming limited to well-founded recursion with a few other constraints is called total functional programming.
=== Strict versus non-strict evaluation ===
Functional languages can be categorized by whether they use strict (eager) or non-strict (lazy) evaluation, concepts that refer to how function arguments are processed when an expression is being evaluated. The technical difference is in the denotational semantics of expressions containing failing or divergent computations. Under strict evaluation, the evaluation of any term containing a failing subterm fails. For example, the expression:
print length([2+1, 3*2, 1/0, 5-4])
fails under strict evaluation because of the division by zero in the third element of the list. Under lazy evaluation, the length function returns the value 4 (i.e., the number of items in the list), since evaluating it does not attempt to evaluate the terms making up the list. In brief, strict evaluation always fully evaluates function arguments before invoking the function. Lazy evaluation does not evaluate function arguments unless their values are required to evaluate the function call itself.
The usual implementation strategy for lazy evaluation in functional languages is graph reduction. Lazy evaluation is used by default in several pure functional languages, including Miranda, Clean, and Haskell.
Hughes 1984 argues for lazy evaluation as a mechanism for improving program modularity through separation of concerns, by easing independent implementation of producers and consumers of data streams. Launchbury 1993 describes some difficulties that lazy evaluation introduces, particularly in analyzing a program's storage requirements, and proposes an operational semantics to aid in such analysis. Harper 2009 proposes including both strict and lazy evaluation in the same language, using the language's type system to distinguish them.
=== Type systems ===
Especially since the development of Hindley–Milner type inference in the 1970s, functional programming languages have tended to use typed lambda calculus, rejecting all invalid programs at compilation time and risking false positive errors, as opposed to the untyped lambda calculus, that accepts all valid programs at compilation time and risks false negative errors, used in Lisp and its variants (such as Scheme), as they reject all invalid programs at runtime when the information is enough to not reject valid programs. The use of algebraic data types makes manipulation of complex data structures convenient; the presence of strong compile-time type checking makes programs more reliable in absence of other reliability techniques like test-driven development, while type inference frees the programmer from the need to manually declare types to the compiler in most cases.
Some research-oriented functional languages such as Coq, Agda, Cayenne, and Epigram are based on intuitionistic type theory, which lets types depend on terms. Such types are called dependent types. These type systems do not have decidable type inference and are difficult to understand and program with. But dependent types can express arbitrary propositions in higher-order logic. Through the Curry–Howard isomorphism, then, well-typed programs in these languages become a means of writing formal mathematical proofs from which a compiler can generate certified code. While these languages are mainly of interest in academic research (including in formalized mathematics), they have begun to be used in engineering as well. Compcert is a compiler for a subset of the language C that is written in Coq and formally verified.
A limited form of dependent types called generalized algebraic data types (GADT's) can be implemented in a way that provides some of the benefits of dependently typed programming while avoiding most of its inconvenience. GADT's are available in the Glasgow Haskell Compiler, in OCaml and in Scala, and have been proposed as additions to other languages including Java and C#.
=== Referential transparency ===
Functional programs do not have assignment statements, that is, the value of a variable in a functional program never changes once defined. This eliminates any chances of side effects because any variable can be replaced with its actual value at any point of execution. So, functional programs are referentially transparent.
Consider C assignment statement x=x*10, this changes the value assigned to the variable x. Let us say that the initial value of x was 1, then two consecutive evaluations of the variable x yields 10 and 100 respectively. Clearly, replacing x=x*10 with either 10 or 100 gives a program a different meaning, and so the expression is not referentially transparent. In fact, assignment statements are never referentially transparent.
Now, consider another function such as int plusone(int x) {return x+1;} is transparent, as it does not implicitly change the input x and thus has no such side effects.
Functional programs exclusively use this type of function and are therefore referentially transparent.
=== Data structures ===
Purely functional data structures are often represented in a different way to their imperative counterparts. For example, the array with constant access and update times is a basic component of most imperative languages, and many imperative data-structures, such as the hash table and binary heap, are based on arrays. Arrays can be replaced by maps or random access lists, which admit purely functional implementation, but have logarithmic access and update times. Purely functional data structures have persistence, a property of keeping previous versions of the data structure unmodified. In Clojure, persistent data structures are used as functional alternatives to their imperative counterparts. Persistent vectors, for example, use trees for partial updating. Calling the insert method will result in some but not all nodes being created.
== Comparison to imperative programming ==
Functional programming is very different from imperative programming. The most significant differences stem from the fact that functional programming avoids side effects, which are used in imperative programming to implement state and I/O. Pure functional programming completely prevents side-effects and provides referential transparency.
Higher-order functions are rarely used in older imperative programming. A traditional imperative program might use a loop to traverse and modify a list. A functional program, on the other hand, would probably use a higher-order "map" function that takes a function and a list, generating and returning a new list by applying the function to each list item.
=== Imperative vs. functional programming ===
The following two examples (written in JavaScript) achieve the same effect: they multiply all even numbers in an array by 10 and add them all, storing the final sum in the variable result.
Traditional imperative loop:
Functional programming with higher-order functions:
Sometimes the abstractions offered by functional programming might lead to development of more robust code that avoids certain issues that might arise when building upon large amount of complex, imperative code, such as off-by-one errors (see Greenspun's tenth rule).
=== Simulating state ===
There are tasks (for example, maintaining a bank account balance) that often seem most naturally implemented with state. Pure functional programming performs these tasks, and I/O tasks such as accepting user input and printing to the screen, in a different way.
The pure functional programming language Haskell implements them using monads, derived from category theory. Monads offer a way to abstract certain types of computational patterns, including (but not limited to) modeling of computations with mutable state (and other side effects such as I/O) in an imperative manner without losing purity. While existing monads may be easy to apply in a program, given appropriate templates and examples, many students find them difficult to understand conceptually, e.g., when asked to define new monads (which is sometimes needed for certain types of libraries).
Functional languages also simulate states by passing around immutable states. This can be done by making a function accept the state as one of its parameters, and return a new state together with the result, leaving the old state unchanged.
Impure functional languages usually include a more direct method of managing mutable state. Clojure, for example, uses managed references that can be updated by applying pure functions to the current state. This kind of approach enables mutability while still promoting the use of pure functions as the preferred way to express computations.
Alternative methods such as Hoare logic and uniqueness have been developed to track side effects in programs. Some modern research languages use effect systems to make the presence of side effects explicit.
=== Efficiency issues ===
Functional programming languages are typically less efficient in their use of CPU and memory than imperative languages such as C and Pascal. This is related to the fact that some mutable data structures like arrays have a very straightforward implementation using present hardware. Flat arrays may be accessed very efficiently with deeply pipelined CPUs, prefetched efficiently through caches (with no complex pointer chasing), or handled with SIMD instructions. It is also not easy to create their equally efficient general-purpose immutable counterparts. For purely functional languages, the worst-case slowdown is logarithmic in the number of memory cells used, because mutable memory can be represented by a purely functional data structure with logarithmic access time (such as a balanced tree). However, such slowdowns are not universal. For programs that perform intensive numerical computations, functional languages such as OCaml and Clean are only slightly slower than C according to The Computer Language Benchmarks Game. For programs that handle large matrices and multidimensional databases, array functional languages (such as J and K) were designed with speed optimizations.
Immutability of data can in many cases lead to execution efficiency by allowing the compiler to make assumptions that are unsafe in an imperative language, thus increasing opportunities for inline expansion. Even if the involved copying that may seem implicit when dealing with persistent immutable data structures might seem computationally costly, some functional programming languages, like Clojure solve this issue by implementing mechanisms for safe memory sharing between formally immutable data. Rust distinguishes itself by its approach to data immutability which involves immutable references and a concept called lifetimes.
Immutable data with separation of identity and state and shared-nothing schemes can also potentially be more well-suited for concurrent and parallel programming by the virtue of reducing or eliminating the risk of certain concurrency hazards, since concurrent operations are usually atomic and this allows eliminating the need for locks. This is how for example java.util.concurrent classes are implemented, where some of them are immutable variants of the corresponding classes that are not suitable for concurrent use. Functional programming languages often have a concurrency model that instead of shared state and synchronization, leverages message passing mechanisms (such as the actor model, where each actor is a container for state, behavior, child actors and a message queue). This approach is common in Erlang/Elixir or Akka.
Lazy evaluation may also speed up the program, even asymptotically, whereas it may slow it down at most by a constant factor (however, it may introduce memory leaks if used improperly). Launchbury 1993 discusses theoretical issues related to memory leaks from lazy evaluation, and O'Sullivan et al. 2008 give some practical advice for analyzing and fixing them.
However, the most general implementations of lazy evaluation making extensive use of dereferenced code and data perform poorly on modern processors with deep pipelines and multi-level caches (where a cache miss may cost hundreds of cycles) .
==== Abstraction cost ====
Some functional programming languages might not optimize abstractions such as higher order functions like "map" or "filter" as efficiently as the underlying imperative operations. Consider, as an example, the following two ways to check if 5 is an even number in Clojure:
When benchmarked using the Criterium tool on a Ryzen 7900X GNU/Linux PC in a Leiningen REPL 2.11.2, running on Java VM version 22 and Clojure version 1.11.1, the first implementation, which is implemented as:
has the mean execution time of 4.76 ms, while the second one, in which .equals is a direct invocation of the underlying Java method, has a mean execution time of 2.8 μs – roughly 1700 times faster. Part of that can be attributed to the type checking and exception handling involved in the implementation of even?. For instance the lo library for Go, which implements various higher-order functions common in functional programming languages using generics. In a benchmark provided by the library's author, calling map is 4% slower than an equivalent for loop and has the same allocation profile, which can be attributed to various compiler optimizations, such as inlining.
One distinguishing feature of Rust are zero-cost abstractions. This means that using them imposes no additional runtime overhead. This is achieved thanks to the compiler using loop unrolling, where each iteration of a loop, be it imperative or using iterators, is converted into a standalone Assembly instruction, without the overhead of the loop controlling code. If an iterative operation writes to an array, the resulting array's elements will be stored in specific CPU registers, allowing for constant-time access at runtime.
=== Functional programming in non-functional languages ===
It is possible to use a functional style of programming in languages that are not traditionally considered functional languages. For example, both D and Fortran 95 explicitly support pure functions.
JavaScript, Lua, Python and Go had first class functions from their inception. Python had support for "lambda", "map", "reduce", and "filter" in 1994, as well as closures in Python 2.2, though Python 3 relegated "reduce" to the functools standard library module. First-class functions have been introduced into other mainstream languages such as Perl 5.0 in 1994, PHP 5.3, Visual Basic 9, C# 3.0, C++11, and Kotlin.
In Perl, lambda, map, reduce, filter, and closures are fully supported and frequently used. The book Higher-Order Perl, released in 2005, was written to provide an expansive guide on using Perl for functional programming.
In PHP, anonymous classes, closures and lambdas are fully supported. Libraries and language extensions for immutable data structures are being developed to aid programming in the functional style.
In Java, anonymous classes can sometimes be used to simulate closures; however, anonymous classes are not always proper replacements to closures because they have more limited capabilities. Java 8 supports lambda expressions as a replacement for some anonymous classes.
In C#, anonymous classes are not necessary, because closures and lambdas are fully supported. Libraries and language extensions for immutable data structures are being developed to aid programming in the functional style in C#.
Many object-oriented design patterns are expressible in functional programming terms: for example, the strategy pattern simply dictates use of a higher-order function, and the visitor pattern roughly corresponds to a catamorphism, or fold.
Similarly, the idea of immutable data from functional programming is often included in imperative programming languages, for example the tuple in Python, which is an immutable array, and Object.freeze() in JavaScript.
== Comparison to logic programming ==
Logic programming can be viewed as a generalisation of functional programming, in which functions are a special case of relations.
For example, the function, mother(X) = Y, (every X has only one mother Y) can be represented by the relation mother(X, Y). Whereas functions have a strict input-output pattern of arguments, relations can be queried with any pattern of inputs and outputs. Consider the following logic program:
The program can be queried, like a functional program, to generate mothers from children:
But it can also be queried backwards, to generate children:
It can even be used to generate all instances of the mother relation:
Compared with relational syntax, functional syntax is a more compact notation for nested functions. For example, the definition of maternal grandmother in functional syntax can be written in the nested form:
The same definition in relational notation needs to be written in the unnested form:
Here :- means if and , means and.
However, the difference between the two representations is simply syntactic. In Ciao Prolog, relations can be nested, like functions in functional programming:
Ciao transforms the function-like notation into relational form and executes the resulting logic program using the standard Prolog execution strategy.
== Applications ==
=== Text editors ===
Emacs, a highly extensible text editor family uses its own Lisp dialect for writing plugins. The original author of the most popular Emacs implementation, GNU Emacs and Emacs Lisp, Richard Stallman considers Lisp one of his favorite programming languages.
Helix, since version 24.03 supports previewing AST as S-expressions, which are also the core feature of the Lisp programming language family.
=== Spreadsheets ===
Spreadsheets can be considered a form of pure, zeroth-order, strict-evaluation functional programming system. However, spreadsheets generally lack higher-order functions as well as code reuse, and in some implementations, also lack recursion. Several extensions have been developed for spreadsheet programs to enable higher-order and reusable functions, but so far remain primarily academic in nature.
=== Microservices ===
Due to their composability, functional programming paradigms can be suitable for microservices-based architectures.
=== Academia ===
Functional programming is an active area of research in the field of programming language theory. There are several peer-reviewed publication venues focusing on functional programming, including the International Conference on Functional Programming, the Journal of Functional Programming, and the Symposium on Trends in Functional Programming.
=== Industry ===
Functional programming has been employed in a wide range of industrial applications. For example, Erlang, which was developed by the Swedish company Ericsson in the late 1980s, was originally used to implement fault-tolerant telecommunications systems, but has since become popular for building a range of applications at companies such as Nortel, Facebook, Électricité de France and WhatsApp. Scheme, a dialect of Lisp, was used as the basis for several applications on early Apple Macintosh computers and has been applied to problems such as training-simulation software and telescope control. OCaml, which was introduced in the mid-1990s, has seen commercial use in areas such as financial analysis, driver verification, industrial robot programming and static analysis of embedded software. Haskell, though initially intended as a research language, has also been applied in areas such as aerospace systems, hardware design and web programming.
Other functional programming languages that have seen use in industry include Scala, F#, Wolfram Language, Lisp, Standard ML and Clojure. Scala has been widely used in Data science, while ClojureScript, Elm or PureScript are some of the functional frontend programming languages used in production. Elixir's Phoenix framework is also used by some relatively popular commercial projects, such as Font Awesome or Allegro (one of the biggest e-commerce platforms in Poland)'s classified ads platform Allegro Lokalnie.
Functional "platforms" have been popular in finance for risk analytics (particularly with large investment banks). Risk factors are coded as functions that form interdependent graphs (categories) to measure correlations in market shifts, similar in manner to Gröbner basis optimizations but also for regulatory frameworks such as Comprehensive Capital Analysis and Review. Given the use of OCaml and Caml variations in finance, these systems are sometimes considered related to a categorical abstract machine. Functional programming is heavily influenced by category theory.
=== Education ===
Many universities teach functional programming. Some treat it as an introductory programming concept while others first teach imperative programming methods.
Outside of computer science, functional programming is used to teach problem-solving, algebraic and geometric concepts. It has also been used to teach classical mechanics, as in the book Structure and Interpretation of Classical Mechanics.
In particular, Scheme has been a relatively popular choice for teaching programming for years.
== See also ==
Eager evaluation
Functional reactive programming
Inductive functional programming
List of functional programming languages
List of functional programming topics
Nested function
Purely functional programming
== Notes and references ==
== Further reading ==
Abelson, Hal; Sussman, Gerald Jay (1985). Structure and Interpretation of Computer Programs. MIT Press. Bibcode:1985sicp.book.....A.
Cousineau, Guy and Michel Mauny. The Functional Approach to Programming. Cambridge, UK: Cambridge University Press, 1998.
Curry, Haskell Brooks and Feys, Robert and Craig, William. Combinatory Logic. Volume I. North-Holland Publishing Company, Amsterdam, 1958.
Curry, Haskell B.; Hindley, J. Roger; Seldin, Jonathan P. (1972). Combinatory Logic. Vol. II. Amsterdam: North Holland. ISBN 978-0-7204-2208-5.
Dominus, Mark Jason. Higher-Order Perl. Morgan Kaufmann. 2005.
Felleisen, Matthias; Findler, Robert; Flatt, Matthew; Krishnamurthi, Shriram (2018). How to Design Programs. MIT Press.
Graham, Paul. ANSI Common LISP. Englewood Cliffs, New Jersey: Prentice Hall, 1996.
MacLennan, Bruce J. Functional Programming: Practice and Theory. Addison-Wesley, 1990.
Michaelson, Greg (10 April 2013). An Introduction to Functional Programming Through Lambda Calculus. Courier Corporation. ISBN 978-0-486-28029-5.
O'Sullivan, Brian; Stewart, Don; Goerzen, John (2008). Real World Haskell. O'Reilly.
Pratt, Terrence W. and Marvin Victor Zelkowitz. Programming Languages: Design and Implementation. 3rd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1996.
Salus, Peter H. Functional and Logic Programming Languages. Vol. 4 of Handbook of Programming Languages. Indianapolis, Indiana: Macmillan Technical Publishing, 1998.
Thompson, Simon. Haskell: The Craft of Functional Programming. Harlow, England: Addison-Wesley Longman Limited, 1996.
== External links ==
Ford, Neal. "Functional thinking". Retrieved 2021-11-10.
Akhmechet, Slava (2006-06-19). "defmacro – Functional Programming For The Rest of Us". Retrieved 2013-02-24. An introduction
Functional programming in Python (by David Mertz): part 1, part 2, part 3
|
https://en.wikipedia.org/wiki/Functional_programming
|
Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.
Depending on the programming language used, there are several kinds of inductive programming. Inductive functional programming, which uses functional programming languages such as Lisp or Haskell, and most especially inductive logic programming, which uses logic programming languages such as Prolog and other logical representations such as description logics, have been more prominent, but other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming.
== Definition ==
Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases.
Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language.
In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete.
In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples.
The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action languages, agent languages and many types of imperative languages.
== History ==
Research on the inductive synthesis of recursive functional programs started in the early 1970s and was brought onto firm theoretical foundations with the seminal THESIS system of Summers and work of Biermann.
These approaches were split into two phases: first, input-output examples are transformed into non-recursive programs (traces) using a small set of basic operators; second, regularities in the traces are searched for and used to fold them into a recursive program. The main results until the mid-1980s are surveyed by Smith. Due to limited progress with respect to the range of programs that could be synthesized, research activities decreased significantly in the next decade.
The advent of logic programming brought a new elan but also a new direction in the early 1980s, especially due to the MIS system of Shapiro eventually spawning the new field of inductive logic programming (ILP). The early works of Plotkin, and his "relative least general generalization (rlgg)", had an enormous impact in inductive logic programming. Most of ILP work addresses a wider class of problems, as the focus is not only on recursive logic programs but on machine learning of symbolic hypotheses from logical representations. However, there were some encouraging results on learning recursive Prolog programs such as quicksort from examples together with suitable background knowledge, for example with GOLEM. But again, after initial success, the community got disappointed by limited progress about the induction of recursive programs with ILP less and less focusing on recursive programs and leaning more and more towards a machine learning setting with applications in relational data mining and knowledge discovery.
In parallel to work in ILP, Koza proposed genetic programming in the early 1990s as a generate-and-test based approach to learning programs. The idea of genetic programming was further developed into the inductive programming system ADATE and the systematic-search-based system MagicHaskeller. Here again, functional programs are learned from sets of positive examples together with an output evaluation (fitness) function which specifies the desired input/output behavior of the program to be learned.
The early work in grammar induction (also known as grammatical inference) is related to inductive programming, as rewriting systems or logic programs can be used to represent production rules. In fact, early works in inductive inference considered grammar induction and Lisp program inference as basically the same problem. The results in terms of learnability were related to classical concepts, such as identification-in-the-limit, as introduced in the seminal work of Gold. More recently, the language learning problem was addressed by the inductive programming community.
In the recent years, the classical approaches have been resumed and advanced with great success. Therefore, the synthesis problem has been reformulated on the background of constructor-based term rewriting systems taking into account modern techniques of functional programming, as well as moderate use of search-based strategies and usage of background knowledge as well as automatic invention of subprograms. Many new and successful applications have recently appeared beyond program synthesis, most especially in the area of data manipulation, programming by example and cognitive modelling (see below).
Other ideas have also been explored with the common characteristic of using declarative languages for the representation of hypotheses. For instance, the use of higher-order features, schemes or structured distances have been advocated for a better handling of recursive data types and structures; abstraction has also been explored as a more powerful approach to cumulative learning and function invention.
One powerful paradigm that has been recently used for the representation of hypotheses in inductive programming (generally in the form of generative models) is probabilistic programming (and related paradigms, such as stochastic logic programs and Bayesian logic programming).
== Application areas ==
The first workshop on Approaches and Applications of Inductive Programming (AAIP) held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations".
Since then, these and many other areas have shown to be successful application niches for inductive programming, such as end-user programming, the related areas of programming by example and programming by demonstration, and intelligent tutoring systems.
Other areas where inductive inference has been recently applied are knowledge acquisition, artificial general intelligence, reinforcement learning and theory evaluation, and cognitive science in general. There may also be prospective applications in intelligent agents, games, robotics, personalisation, ambient intelligence and human interfaces.
== See also ==
Evolutionary programming
Inductive reasoning
Test-driven development
== References ==
== Further reading ==
== External links ==
Inductive Programming community page, hosted by the University of Bamberg.
|
https://en.wikipedia.org/wiki/Inductive_programming
|
"Extreme programming (XP) is a software development methodology intended to improve software quality(...TRUNCATED)
|
https://en.wikipedia.org/wiki/Extreme_programming
|
"Ajax (also AJAX ; short for \"asynchronous JavaScript and XML\") is a set of web development techni(...TRUNCATED)
|
https://en.wikipedia.org/wiki/Ajax_(programming)
|
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 11