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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
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al 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. Whi
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ved 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
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rogrammable 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
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d 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
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de-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
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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, we
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cord 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 develop
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ary 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
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-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.
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l 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
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x 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
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ever 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 antic
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-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 brea
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anticipated 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 b
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fferences 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 environment
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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 caref
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n): 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 ti
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eck 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 mo
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ity 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 p
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he 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 as
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, 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. Techni
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ents (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
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emory 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 appro
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(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 Mod
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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 p
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lt 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 p
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anguages 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 designe
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tion 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 pr
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equences 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 dif
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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 r
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nd-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
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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
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vailability 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-orie
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w-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:
T
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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.
Rep
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xecute 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 practic
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tory 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
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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 mi
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elopers. 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 speci
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fessional 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, ma
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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 G
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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
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rs.
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.
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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
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omas 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 journ
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mers, 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
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h 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 c
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983).
=== 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 Nor
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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 publishe
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try 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
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ammers. 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
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on, 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. N
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s, 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 sof
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ment 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 movement
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ft 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,
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ers 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: Por
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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., & EBSC
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. 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, G
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ractice 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
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ions related to Programming at Wikiquote
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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 compil
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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 simplicit
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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 classific
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ntation, 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
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g 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
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tical 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, an
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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). Th
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t 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. There
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pensive, 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
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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
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h 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 195
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er 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
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ithmic 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
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st 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, desi
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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
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ntroduced 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,
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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.
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l 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
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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 classifi
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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 oper
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damental 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 la
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ers, 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 synt
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s 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 sy
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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
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less 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 se
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ning 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
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ue 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 lang
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sky 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 bl
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ontrast 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 essen
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ms. 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
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o 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 in
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ce 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 languag
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s 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 valu
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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, a
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typed 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
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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
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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 o
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t 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 eleme
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erences 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 ma
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