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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 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 β 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" β 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 asse
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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 compiler produces an executable program.
Computer architecture has strongly influenced the design of programming languages, with the most common type (imperative languagesβwhich 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 languagesβoften classified as imperative, functional, logic, or object-orientedβhave been developed for a wide variety of uses. Many aspects of programming language design involve tradeoffsβfor 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 languagesβ2GLs) 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 languagesβ3GLs)βrevolutionized programming. These languages abstracted away the details of the hardware, instead being designed to express
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C (pronounced β 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.
A
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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.
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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 programmat
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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 t
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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 SchΓΆnfinkel 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
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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 log
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Extreme programming (XP) is a software development methodology intended to improve software quality and responsiveness to changing customer requirements. As a type of agile software development, it advocates frequent releases in short development cycles, intended to improve productivity and introduce checkpoints at which new customer requirements can be adopted.
Other elements of extreme programming include programming in pairs or doing extensive code review, unit testing of all code, not programming features until they are actually needed, a flat management structure, code simplicity and clarity, expecting changes in the customer's requirements as time passes and the problem is better understood, and frequent communication with the customer and among programmers. The methodology takes its name from the idea that the beneficial elements of traditional software engineering practices are taken to "extreme" levels. As an example, code reviews are considered a beneficial practice; taken to the extreme, code can be reviewed continuously (i.e. the practice of pair programming).
== History ==
Kent Beck developed extreme programming during his work on the Chrysler Comprehensive Compensation System (C3) payroll project. Beck became the C3 project leader in March 1996. He began to refine the development methodology used in the project and wrote a book on the methodology (Extreme Programming Explained, published in October 1999). Chrysler cancelled the C3 project in February 2000, after seven years, when Daimler-Benz acquired the company. Ward Cunningham was another major influence on XP.
Many extreme-programming practices have been around for some time; the methodology takes "best practices" to extreme levels. For example, the "practice of test-first development, planning and writing tests before each micro-increment" was used as early as NASA's Project Mercury, in the early 1960s. To shorten the total development time, some formal test documents (such as for acceptance testing) have been developed in parallel with (or shortly before) the software being ready for testing. A NASA independent test group can write the test procedures, based on formal requirements and logical limits, before programmers write the software and integrate it with the hardware. XP takes this concept to the extreme level, writing automated tests (sometimes inside software modules) which validate the operation of even small sections of software coding, rather than only testing the larger features.
=== Origins ===
Two major influences shaped software development in the 1990s:
Internally, object-oriented programming replaced procedural programming as the programming paradigm favored by some developers.
Externally, the rise of the Internet and the dot-com boom emphasized speed-to-market and company growth as competitive business factors.
Rapidly changing requirements demanded shorter product life-cycles, and often clashed with traditional methods of software development.
The Chrysler Comprehensive Compensation System (C3) started in order to determine the best way to use object technologies, using the payroll systems at Chrysler as the object of research, with Smalltalk as the language and GemStone as the data access layer. Chrysler brought in Kent Beck, a prominent Smalltalk practitioner, to do performance tuning on the system, but his role expanded as he noted several problems with the development process. He took this opportunity to propose and implement some changes in development practices - based on his work with his frequent collaborator, Ward Cunningham. Beck describes the early conception of the methods:
The first time I was asked to lead a team, I asked them to do a little bit of the things I thought were sensible, like testing and reviews. The second time there was a lot more on the line. I thought, "Damn the torpedoes, at least this will make a good article," [and] asked the team to crank up all the knobs to 10 on the things I thought were essent
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Defensive programming is a form of defensive design intended to develop programs that are capable of detecting potential security abnormalities and make predetermined responses. It ensures the continuing function of a piece of software under unforeseen circumstances. Defensive programming practices are often used where high availability, safety, or security is needed.
Defensive programming is an approach to improve software and source code, in terms of:
General quality β reducing the number of software bugs and problems.
Making the source code comprehensible β the source code should be readable and understandable so it is approved in a code audit.
Making the software behave in a predictable manner despite unexpected inputs or user actions.
Overly defensive programming, however, may safeguard against errors that will never be encountered, thus incurring run-time and maintenance costs.
== Secure programming ==
Secure programming is the subset of defensive programming concerned with computer security. Security is the concern, not necessarily safety or availability (the software may be allowed to fail in certain ways). As with all kinds of defensive programming, avoiding bugs is a primary objective; however, the motivation is not as much to reduce the likelihood of failure in normal operation (as if safety were the concern), but to reduce the attack surface β the programmer must assume that the software might be misused actively to reveal bugs, and that bugs could be exploited maliciously.
The function will result in undefined behavior when the input is over 1000 characters. Some programmers may not feel that this is a problem, supposing that no user will enter such a long input. This particular bug demonstrates a vulnerability which enables buffer overflow exploits. Here is a solution to this example:
== Offensive programming ==
Offensive programming is a category of defensive programming, with the added emphasis that certain errors should not be handled defensively. In this practice, only errors from outside the program's control are to be handled (such as user input); the software itself, as well as data from within the program's line of defense, are to be trusted in this methodology.
=== Trusting internal data validity ===
Overly defensive programming
Offensive programming
=== Trusting software components ===
Overly defensive programming
Offensive programming
== Techniques ==
Here are some defensive programming techniques:
=== Intelligent source code reuse ===
If existing code is tested and known to work, reusing it may reduce the chance of bugs being introduced.
However, reusing code is not always good practice. Reuse of existing code, especially when widely distributed, can allow for exploits to be created that target a wider audience than would otherwise be possible and brings with it all the security and vulnerabilities of the reused code.
When considering using existing source code, a quick review of the modules(sub-sections such as classes or functions) will help eliminate or make the developer aware of any potential vulnerabilities and ensure it is suitable to use in the project.
==== Legacy problems ====
Before reusing old source code, libraries, APIs, configurations and so forth, it must be considered if the old work is valid for reuse, or if it is likely to be prone to legacy problems.
Legacy problems are problems inherent when old designs are expected to work with today's requirements, especially when the old designs were not developed or tested with those requirements in mind.
Many software products have experienced problems with old legacy source code; for example:
Legacy code may not have been designed under a defensive programming initiative, and might therefore be of much lower quality than newly designed source code.
Legacy code may have been written and tested under conditions which no longer apply. The old quality assurance tests may have no validity any more.
Example 1: legacy code may
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Ajax (also AJAX ; short for "asynchronous JavaScript and XML") is a set of web development techniques that uses various web technologies on the client-side to create asynchronous web applications. With Ajax, web applications can send and retrieve data from a server asynchronously (in the background) without interfering with the display and behaviour of the existing page. By decoupling the data interchange layer from the presentation layer, Ajax allows web pages and, by extension, web applications, to change content dynamically without the need to reload the entire page. In practice, modern implementations commonly utilize JSON instead of XML.
Ajax is not a technology, but rather a programming pattern. HTML and CSS can be used in combination to mark up and style information. The webpage can be modified by JavaScript to dynamically display (and allow the user to interact with) the new information. The built-in XMLHttpRequest object is used to execute Ajax on webpages, allowing websites to load content onto the screen without refreshing the page. Ajax is not a new technology, nor is it a new language. Instead, it is existing technologies used in a new way.
== History ==
In the early-to-mid 1990s, most Websites were based on complete HTML pages. Each user action required a complete new page to be loaded from the server. This process was inefficient, as reflected by the user experience: all page content disappeared, then the new page appeared. Each time the browser reloaded a page because of a partial change, all the content had to be re-sent, even though only some of the information had changed. This placed additional load on the server and made bandwidth a limiting factor in performance.
In 1996, the iframe tag was introduced by Internet Explorer; like the object element, it can load a part of the web page asynchronously. In 1998, the Microsoft Outlook Web Access team developed the concept behind the XMLHttpRequest scripting object. It appeared as XMLHTTP in the second version of the MSXML library, which shipped with Internet Explorer 5.0 in March 1999.
The functionality of the Windows XMLHTTP ActiveX control in IE 5 was later implemented by Mozilla Firefox, Safari, Opera, Google Chrome, and other browsers as the XMLHttpRequest JavaScript object. Microsoft adopted the native XMLHttpRequest model as of Internet Explorer 7. The ActiveX version is still supported in Internet Explorer and on "Internet Explorer mode" in Microsoft Edge. The utility of these background HTTP requests and asynchronous Web technologies remained fairly obscure until it started appearing in large scale online applications such as Outlook Web Access (2000) and Oddpost (2002).
Google made a wide deployment of standards-compliant, cross browser Ajax with Gmail (2004) and Google Maps (2005). In October 2004 Kayak.com's public beta release was among the first large-scale e-commerce uses of what their developers at that time called "the xml http thing". This increased interest in Ajax among web program developers.
The term AJAX was publicly used on 18 February 2005 by Jesse James Garrett in an article titled Ajax: A New Approach to Web Applications, based on techniques used on Google pages.
On 5 April 2006, the World Wide Web Consortium (W3C) released the first draft specification for the XMLHttpRequest object in an attempt to create an official Web standard.
The latest draft of the XMLHttpRequest object was published on 6 October 2016, and the XMLHttpRequest specification is now a living standard.
== Technologies ==
The term Ajax has come to represent a broad group of Web technologies that can be used to implement a Web application that communicates with a server in the background, without interfering with the current state of the page. In the article that coined the term Ajax, Jesse James Garrett explained that the following technologies are incorporated:
HTML (or XHTML) and CSS for presentation
The Document Object Model (DOM) for dynamic display of and in
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https://en.wikipedia.org/wiki/Ajax_(programming)
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In computer science, imperative programming is a programming paradigm of software that uses statements that change a program's state. In much the same way that the imperative mood in natural languages expresses commands, an imperative program consists of commands for the computer to perform. Imperative programming focuses on describing how a program operates step by step (generally order of the steps being determined in source code by the placement of statements one below the other), rather than on high-level descriptions of its expected results.
The term is often used in contrast to declarative programming, which focuses on what the program should accomplish without specifying all the details of how the program should achieve the result.
== Procedural programming ==
Procedural programming is a type of imperative programming in which the program is built from one or more procedures (also termed subroutines or functions). The terms are often used as synonyms, but the use of procedures has a dramatic effect on how imperative programs appear and how they are constructed. Heavy procedural programming, in which state changes are localized to procedures or restricted to explicit arguments and returns from procedures, is a form of structured programming. Since the 1960s, structured programming and modular programming in general have been promoted as techniques to improve the maintainability and overall quality of imperative programs. The concepts behind object-oriented programming attempt to extend this approach.
Procedural programming could be considered a step toward declarative programming. A programmer can often tell, simply by looking at the names, arguments, and return types of procedures (and related comments), what a particular procedure is supposed to do, without necessarily looking at the details of how it achieves its result. At the same time, a complete program is still imperative since it fixes the statements to be executed and their order of execution to a large extent.
== Rationale and foundations of imperative programming ==
The programming paradigm used to build programs for almost all computers typically follows an imperative model. Digital computer hardware is designed to execute machine code, which is native to the computer and is usually written in the imperative style, although low-level compilers and interpreters using other paradigms exist for some architectures such as lisp machines.
From this low-level perspective, the program state is defined by the contents of memory, and the statements are instructions in the native machine language of the computer. Higher-level imperative languages use variables and more complex statements, but still follow the same paradigm. Recipes and process checklists, while not computer programs, are also familiar concepts that are similar in style to imperative programming; each step is an instruction, and the physical world holds the state. Since the basic ideas of imperative programming are both conceptually familiar and directly embodied in the hardware, most computer languages are in the imperative style.
Assignment statements, in imperative paradigm, perform an operation on information located in memory and store the results in memory for later use. High-level imperative languages, in addition, permit the evaluation of complex expressions, which may consist of a combination of arithmetic operations and function evaluations, and the assignment of the resulting value to memory. Looping statements (as in while loops, do while loops, and for loops) allow a sequence of statements to be executed multiple times. Loops can either execute the statements they contain a predefined number of times, or they can execute them repeatedly until some condition is met. Conditional branching statements allow a sequence of statements to be executed only if some condition is met. Otherwise, the statements are skipped and the execution sequence continues from the statement following them. Uncon
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Java is a high-level, general-purpose, memory-safe, object-oriented programming language. It is intended to let programmers write once, run anywhere (WORA), meaning that compiled Java code can run on all platforms that support Java without the need to recompile. Java applications are typically compiled to bytecode that can run on any Java virtual machine (JVM) regardless of the underlying computer architecture. The syntax of Java is similar to C and C++, but has fewer low-level facilities than either of them. The Java runtime provides dynamic capabilities (such as reflection and runtime code modification) that are typically not available in traditional compiled languages.
Java gained popularity shortly after its release, and has been a popular programming language since then. Java was the third most popular programming language in 2022 according to GitHub. Although still widely popular, there has been a gradual decline in use of Java in recent years with other languages using JVM gaining popularity.
Java was designed by James Gosling at Sun Microsystems. It was released in May 1995 as a core component of Sun's Java platform. The original and reference implementation Java compilers, virtual machines, and class libraries were released by Sun under proprietary licenses. As of May 2007, in compliance with the specifications of the Java Community Process, Sun had relicensed most of its Java technologies under the GPL-2.0-only license. Oracle, which bought Sun in 2010, offers its own HotSpot Java Virtual Machine. However, the official reference implementation is the OpenJDK JVM, which is open-source software used by most developers and is the default JVM for almost all Linux distributions.
Java 24 is the version current as of March 2025. Java 8, 11, 17, and 21 are long-term support versions still under maintenance.
== History ==
James Gosling, Mike Sheridan, and Patrick Naughton initiated the Java language project in June 1991. Java was originally designed for interactive television, but it was too advanced for the digital cable television industry at the time. The language was initially called Oak after an oak tree that stood outside Gosling's office. Later the project went by the name Green and was finally renamed Java, from Java coffee, a type of coffee from Indonesia. Gosling designed Java with a C/C++-style syntax that system and application programmers would find familiar.
Sun Microsystems released the first public implementation as Java 1.0 in 1996. It promised write once, run anywhere (WORA) functionality, providing no-cost run-times on popular platforms. Fairly secure and featuring configurable security, it allowed network- and file-access restrictions. Major web browsers soon incorporated the ability to run Java applets within web pages, and Java quickly became popular. The Java 1.0 compiler was re-written in Java by Arthur van Hoff to comply strictly with the Java 1.0 language specification. With the advent of Java 2 (released initially as J2SE 1.2 in December 1998 β 1999), new versions had multiple configurations built for different types of platforms. J2EE included technologies and APIs for enterprise applications typically run in server environments, while J2ME featured APIs optimized for mobile applications. The desktop version was renamed J2SE. In 2006, for marketing purposes, Sun renamed new J2 versions as Java EE, Java ME, and Java SE, respectively.
In 1997, Sun Microsystems approached the ISO/IEC JTC 1 standards body and later the Ecma International to formalize Java, but it soon withdrew from the process. Java remains a de facto standard, controlled through the Java Community Process. At one time, Sun made most of its Java implementations available without charge, despite their proprietary software status. Sun generated revenue from Java through the selling of licenses for specialized products such as the Java Enterprise System.
On November 13, 2006, Sun released much of its Java virtual machine (JVM) as free
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https://en.wikipedia.org/wiki/Java_(programming_language)
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In computer science, array programming refers to solutions that allow the application of operations to an entire set of values at once. Such solutions are commonly used in scientific and engineering settings.
Modern programming languages that support array programming (also known as vector or multidimensional languages) have been engineered specifically to generalize operations on scalars to apply transparently to vectors, matrices, and higher-dimensional arrays. These include APL, J, Fortran, MATLAB, Analytica, Octave, R, Cilk Plus, Julia, Perl Data Language (PDL), Raku (programming language). In these languages, an operation that operates on entire arrays can be called a vectorized operation, regardless of whether it is executed on a vector processor, which implements vector instructions. Array programming primitives concisely express broad ideas about data manipulation. The level of concision can be dramatic in certain cases: it is not uncommon to find array programming language one-liners that require several pages of object-oriented code.
== Concepts of array ==
The fundamental idea behind array programming is that operations apply at once to an entire set of values. This makes it a high-level programming model as it allows the programmer to think and operate on whole aggregates of data, without having to resort to explicit loops of individual scalar operations.
Kenneth E. Iverson described the rationale behind array programming (actually referring to APL) as follows:
most programming languages are decidedly inferior to mathematical notation and are little used as tools of thought in ways that would be considered significant by, say, an applied mathematician.
The thesis is that the advantages of executability and universality found in programming languages can be effectively combined, in a single coherent language, with the advantages offered by mathematical notation. it is important to distinguish the difficulty of describing and of learning a piece of notation from the difficulty of mastering its implications. For example, learning the rules for computing a matrix product is easy, but a mastery of its implications (such as its associativity, its distributivity over addition, and its ability to represent linear functions and geometric operations) is a different and much more difficult matter.
Indeed, the very suggestiveness of a notation may make it seem harder to learn because of the many properties it suggests for explorations.
[...]
Users of computers and programming languages are often concerned primarily with the efficiency of execution of algorithms, and might, therefore, summarily dismiss many of the algorithms presented here. Such dismissal would be short-sighted since a clear statement of an algorithm can usually be used as a basis from which one may easily derive a more efficient algorithm.
The basis behind array programming and thinking is to find and exploit the properties of data where individual elements are similar or adjacent. Unlike object orientation which implicitly breaks down data to its constituent parts (or scalar quantities), array orientation looks to group data and apply a uniform handling.
Function rank is an important concept to array programming languages in general, by analogy to tensor rank in mathematics: functions that operate on data may be classified by the number of dimensions they act on. Ordinary multiplication, for example, is a scalar ranked function because it operates on zero-dimensional data (individual numbers). The cross product operation is an example of a vector rank function because it operates on vectors, not scalars. Matrix multiplication is an example of a 2-rank function, because it operates on 2-dimensional objects (matrices). Collapse operators reduce the dimensionality of an input data array by one or more dimensions. For example, summing over elements collapses the input array by 1 dimension.
== Uses ==
Array programming is very well suited to implicit parall
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Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.
Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.
Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language, and he first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.
Python consistently ranks as one of the most popular programming languages, and it has gained widespread use in the machine learning community.
== History ==
Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands; it was conceived as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Python implementation began in December 1989. Van Rossum assumed sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from responsibilities as Python's "benevolent dictator for life" (BDFL); this title was bestowed on him by the Python community to reflect his long-term commitment as the project's chief decision-maker. (He has since come out of retirement and is self-titled "BDFL-emeritus".) In January 2019, active Python core developers elected a five-member Steering Council to lead the project.
The name Python is said to derive from the British comedy series Monty Python's Flying Circus.
Python 2.0 was released on 16 October 2000, with many major new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 2.7's end-of-life was initially set for 2015, and then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. It no longer receives security patches or updates. While Python 2.7 and older versions are officially unsupported, a different unofficial Python implementation, PyPy, continues to support Python 2, i.e., "2.7.18+" (plus 3.10), with the plus signifying (at least some) "backported security updates".
Python 3.0 was released on 3 December 2008, with some new semantics and changed syntax. At least every Python release since (the now unsupported) 3.5 has added some syntax to the language; a few later releases have removed outdated modules and have changed semantics, at least in a minor way.
As of 8 April 2025, Python 3.13.3 is the latest stable release (it's highly recommended to upgrade to it, or upgrade any other older 3.x release). This version currently receives full bug-fix and security updates, while Python 3.12βreleased in October 2023βhad active bug-fix support only until April 2025, and since then only security fixes. Python 3.9 is the oldest supported version of Python (albeit in the 'security support' phase), because Python 3.8 has become an end-of-life product. Starting with Python 3.13, it and later versions receive two years of full support (which has increased from one and a half years), followed by three years of security support; this is the same total duration of support as previously.
Security updates were expedited in 2021 and again twice in 2022. More issues were fixed in 2023 and in September 2024 (for Python versions 3.8.20 through 3.12.6)βall versions (including 2.7) had been insecure because of issues leading to possible remote code execution and web-cache poisoning.
Python 3.10 added the | union type operator and added structural pattern matching capability t
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Generic programming is a style of computer programming in which algorithms are written in terms of data types to-be-specified-later that are then instantiated when needed for specific types provided as parameters. This approach, pioneered in the programming language ML in 1973, permits writing common functions or data types that differ only in the set of types on which they operate when used, thus reducing duplicate code.
Generic programming was introduced to the mainstream with Ada in 1977. With templates in C++, generic programming became part of the repertoire of professional library design. The techniques were further improved and parameterized types were introduced in the influential 1994 book Design Patterns.
New techniques were introduced by Andrei Alexandrescu in his 2001 book Modern C++ Design: Generic Programming and Design Patterns Applied. Subsequently, D implemented the same ideas.
Such software entities are known as generics in Ada, C#, Delphi, Eiffel, F#, Java, Nim, Python, Go, Rust, Swift, TypeScript, and Visual Basic (.NET). They are known as parametric polymorphism in ML, Scala, Julia, and Haskell. (Haskell terminology also uses the term generic for a related but somewhat different concept.)
The term generic programming was originally coined by David Musser and Alexander Stepanov in a more specific sense than the above, to describe a programming paradigm in which fundamental requirements on data types are abstracted from across concrete examples of algorithms and data structures and formalized as concepts, with generic functions implemented in terms of these concepts, typically using language genericity mechanisms as described above.
== StepanovβMusser and other generic programming paradigms ==
Generic programming is defined in Musser & Stepanov (1989) as follows,
Generic programming centers around the idea of abstracting from concrete, efficient algorithms to obtain generic algorithms that can be combined with different data representations to produce a wide variety of useful software.
The "generic programming" paradigm is an approach to software decomposition whereby fundamental requirements on types are abstracted from across concrete examples of algorithms and data structures and formalized as concepts, analogously to the abstraction of algebraic theories in abstract algebra. Early examples of this programming approach were implemented in Scheme and Ada, although the best known example is the Standard Template Library (STL), which developed a theory of iterators that is used to decouple sequence data structures and the algorithms operating on them.
For example, given N sequence data structures, e.g. singly linked list, vector etc., and M algorithms to operate on them, e.g. find, sort etc., a direct approach would implement each algorithm specifically for each data structure, giving N Γ M combinations to implement. However, in the generic programming approach, each data structure returns a model of an iterator concept (a simple value type that can be dereferenced to retrieve the current value, or changed to point to another value in the sequence) and each algorithm is instead written generically with arguments of such iterators, e.g. a pair of iterators pointing to the beginning and end of the subsequence or range to process. Thus, only N + M data structure-algorithm combinations need be implemented. Several iterator concepts are specified in the STL, each a refinement of more restrictive concepts e.g. forward iterators only provide movement to the next value in a sequence (e.g. suitable for a singly linked list or a stream of input data), whereas a random-access iterator also provides direct constant-time access to any element of the sequence (e.g. suitable for a vector). An important point is that a data structure will return a model of the most general concept that can be implemented efficientlyβcomputational complexity requirements are explicitly part of the concept definition. This limits the data
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Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization).
More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Its feasible region is a convex polytope, which is a set defined as the intersection of finitely many half spaces, each of which is defined by a linear inequality. Its objective function is a real-valued affine (linear) function defined on this polytope. A linear programming algorithm finds a point in the polytope where this function has the largest (or smallest) value if such a point exists.
Linear programs are problems that can be expressed in standard form as:
Find a vector
x
that maximizes
c
T
x
subject to
A
x
β€
b
and
x
β₯
0
.
{\displaystyle {\begin{aligned}&{\text{Find a vector}}&&\mathbf {x} \\&{\text{that maximizes}}&&\mathbf {c} ^{\mathsf {T}}\mathbf {x} \\&{\text{subject to}}&&A\mathbf {x} \leq \mathbf {b} \\&{\text{and}}&&\mathbf {x} \geq \mathbf {0} .\end{aligned}}}
Here the components of
x
{\displaystyle \mathbf {x} }
are the variables to be determined,
c
{\displaystyle \mathbf {c} }
and
b
{\displaystyle \mathbf {b} }
are given vectors, and
A
{\displaystyle A}
is a given matrix. The function whose value is to be maximized (
x
β¦
c
T
x
{\displaystyle \mathbf {x} \mapsto \mathbf {c} ^{\mathsf {T}}\mathbf {x} }
in this case) is called the objective function. The constraints
A
x
β€
b
{\displaystyle A\mathbf {x} \leq \mathbf {b} }
and
x
β₯
0
{\displaystyle \mathbf {x} \geq \mathbf {0} }
specify a convex polytope over which the objective function is to be optimized.
Linear programming can be applied to various fields of study. It is widely used in mathematics and, to a lesser extent, in bus
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Dynamic programming is both a mathematical optimization method and an algorithmic paradigm. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics.
In both contexts it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively. Likewise, in computer science, if a problem can be solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have optimal substructure.
If sub-problems can be nested recursively inside larger problems, so that dynamic programming methods are applicable, then there is a relation between the value of the larger problem and the values of the sub-problems. In the optimization literature this relationship is called the Bellman equation.
== Overview ==
=== Mathematical optimization ===
In terms of mathematical optimization, dynamic programming usually refers to simplifying a decision by breaking it down into a sequence of decision steps over time.
This is done by defining a sequence of value functions V1, V2, ..., Vn taking y as an argument representing the state of the system at times i from 1 to n.
The definition of Vn(y) is the value obtained in state y at the last time n.
The values Vi at earlier times i = n β1, n β 2, ..., 2, 1 can be found by working backwards, using a recursive relationship called the Bellman equation.
For i = 2, ..., n, Viβ1 at any state y is calculated from Vi by maximizing a simple function (usually the sum) of the gain from a decision at time i β 1 and the function Vi at the new state of the system if this decision is made.
Since Vi has already been calculated for the needed states, the above operation yields Viβ1 for those states.
Finally, V1 at the initial state of the system is the value of the optimal solution. The optimal values of the decision variables can be recovered, one by one, by tracking back the calculations already performed.
=== Control theory ===
In control theory, a typical problem is to find an admissible control
u
β
{\displaystyle \mathbf {u} ^{\ast }}
which causes the system
x
Λ
(
t
)
=
g
(
x
(
t
)
,
u
(
t
)
,
t
)
{\displaystyle {\dot {\mathbf {x} }}(t)=\mathbf {g} \left(\mathbf {x} (t),\mathbf {u} (t),t\right)}
to follow an admissible trajectory
x
β
{\displaystyle \mathbf {x} ^{\ast }}
on a continuous time interval
t
0
β€
t
β€
t
1
{\displaystyle t_{0}\leq t\leq t_{1}}
that minimizes a cost function
J
=
b
(
x
(
t
1
)
,
t
1
)
+
β«
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A programming game is a video game that incorporates elements of computer programming, enabling the player to direct otherwise autonomous units within the game to follow commands in a domain-specific programming language, often represented as a visual language to simplify the programming metaphor. Programming games broadly fall into two areas: single-player games where the programming elements either make up part of or the whole of a puzzle game, and multiplayer games where the player's automated program is pitted against other players' programs.
== As puzzle games ==
Early games in the genre include System 15000 and Hacker, released in 1984 and 1985 respectively.
Programming games have been used as part of puzzle games, challenging the player to achieve a specific result once the program starts operating. An example of such a game is SpaceChem, where the player must use its visual language to manipulate two waldos as to disassemble and reassemble chemical molecules. In such games, players are able to test and debug their program as often as necessary until they find a solution that works. Many of these games encourage the player to find the most efficient program, measured by the number of timesteps needed or number of commands required. Other similar games include Human Resource Machine, Infinifactory, and TIS-100. Zachtronics is a video game development company known for its programming-centric puzzle games.
Other games incorporate the elements of programming as portions of puzzles in the larger game. For example, Hack 'n' Slash include a metaphor of being able to access the internal programs and variables of objects represented in the game world, pausing the rest of the game as the player engages this programming interface, and modify the object's program as to progress further; this might be changing the state of an object from being indestructible to destructible. Other similar games with this type of programming approach include Transistor, else Heart.Break(), Glitchspace, and Pony Island.
Another approach used in some graphical games with programming elements is to present the player with a command line interface to issue orders via a domain-specific language to direct objects within the game, allowing the player to reissue commands as the situation changes rather than crafting a pre-made program. Games like Quadrilateral Cowboy and Duskers have the user command several small robotic creatures in tandem through the language of code to reach a certain goal. Hackmud presents the player with a simulated mainframe interface through which they issue commands to progress forward.
== As competitive games ==
Many programming games involve controlling entities such as robots, tanks or bacteria which seek to destroy each other. Such games can be considered environments of digital organisms, related to artificial life simulations. An early example is Core War (1984), where programs written in a standardized assembly-like language battle for space in a finite memory (virtual magnetic cores). Players are given tools to develop and test out their programs within the game's domain-specific language before submitting the program to a central server. The server then executes the program against others and reports the results to the player, from which they can make changes or improvements to the program.
There are different tournaments and leagues for the programming games where the characters can compete with each other. Usually a script is optimized for a special strategy. Similar approaches are used for more traditional games; the World Computer Chess Championship consists of matches between programs written for the abstract strategy game of chess.
The competitive programming game has also found its way to various board games such as RoboRally or Robot Turtles, typically where a program becomes a premade deck of playing cards played one by one to execute that code.
Researchers presented RoboCode as a "problem-based learning" subs
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Procedural programming is a programming paradigm, classified as imperative programming, that involves implementing the behavior of a computer program as procedures (a.k.a. functions, subroutines) that call each other. The resulting program is a series of steps that forms a hierarchy of calls to its constituent procedures.
The first major procedural programming languages appeared c.β1957β1964, including Fortran, ALGOL, COBOL, PL/I and BASIC. Pascal and C were published c.β1970β1972.
Computer processors provide hardware support for procedural programming through a stack register and instructions for calling procedures and returning from them. Hardware support for other types of programming is possible, like Lisp machines or Java processors, but no attempt was commercially successful.
== Development practices ==
Certain software development practices are often employed with procedural programming in order to enhance quality and lower development and maintenance costs.
=== Modularity and scoping ===
Modularity is about organizing the procedures of a program into separate modulesβeach of which has a specific and understandable purpose.
Minimizing the scope of variables and procedures can enhance software quality by reducing the cognitive load of procedures and modules.
A program lacking modularity or wide scoping tends to have procedures that consume many variables that other procedures also consume. The resulting code is relatively hard to understand and to maintain.
=== Sharing ===
Since a procedure can specify a well-defined interface and be self-contained it supports code reuseβin particular via the software library.
== Comparison with other programming paradigms ==
=== Imperative programming ===
Procedural programming is classified as an imperative programming, because it involves direct command of execution.
Procedural is a sub-class of imperative since procedural includes block and scope concepts, whereas imperative describes a more general concept that does not require such features. Procedural languages generally use reserved words that define blocks, such as if, while, and for, to implement control flow, whereas non-structured imperative languages (i.e. assembly language) use goto and branch tables for this purpose.
=== Object-oriented programming ===
Also classified as imperative, object-oriented programming (OOP) involves dividing a program implementation into objects that expose behavior (methods) and data (members) via a well-defined interface. In contrast, procedural programming is about dividing the program implementation into variables, data structures, and subroutines. An important distinction is that while procedural involves procedures to operate on data structures, OOP bundles the two together. An object is a data structure and the behavior associated with that data structure.
Some OOP languages support the class concept which allows for creating an object based on a definition.
Nomenclature varies between the two, although they have similar semantics:
=== Functional programming ===
The principles of modularity and code reuse in functional languages are fundamentally the same as in procedural languages, since they both stem from structured programming. For example:
Procedures correspond to functions. Both allow the reuse of the same code in various parts of the programs, and at various points of its execution.
By the same token, procedure calls correspond to function application.
Functions and their modularly separated from each other in the same manner, by the use of function arguments, return values and variable scopes.
The main difference between the styles is that functional programming languages remove or at least deemphasize the imperative elements of procedural programming. The feature set of functional languages is therefore designed to support writing programs as much as possible in terms of pure functions:
Whereas procedural languages model execution of the program as a sequence of i
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Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (MCDA). It can be thought of as an extension or generalisation of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Deviations are measured from these goals both above and below the target. Unwanted deviations from this set of target values are then minimised in an achievement function. This can be a vector or a weighted sum dependent on the goal programming variant used. As satisfaction of the target is deemed to satisfy the decision maker(s), an underlying satisficing philosophy is assumed. Goal programming is used to perform three types of analysis:
Determine the required resources to achieve a desired set of objectives.
Determine the degree of attainment of the goals with the available resources.
Providing the best satisfying solution under a varying amount of resources and priorities of the goals.
== History ==
Goal programming was first used by Charnes, Cooper and Ferguson in 1955, although the actual name first appeared in a 1961 text by Charnes and Cooper. Seminal works by Lee, Ignizio, Ignizio and Cavalier, and Romero followed. Schniederjans gives in a bibliography of a large number of pre-1995 articles relating to goal programming, and Jones and Tamiz give an annotated bibliography of the period 1990-2000. A recent textbook by Jones and Tamiz . gives a comprehensive overview of the state-of-the-art in goal programming.
The first engineering application of goal programming, due to Ignizio in 1962, was the design and placement of the antennas employed on the second stage of the Saturn V. This was used to launch the Apollo space capsule that landed the first men on the moon.
== Variants ==
The initial goal programming formulations ordered the unwanted deviations into a number of priority levels, with the minimisation of a deviation in a higher priority level being infinitely more important than any deviations in lower priority levels. This is known as lexicographic or pre-emptive goal programming. Ignizio gives an algorithm showing how a lexicographic goal programme can be solved as a series of linear programmes. Lexicographic goal programming is used when there exists a clear priority ordering amongst the goals to be achieved.
If the decision maker is more interested in direct comparisons of the objectives then weighted or non-pre-emptive goal programming should be used. In this case, all the unwanted deviations are multiplied by weights, reflecting their relative importance, and added together as a single sum to form the achievement function. Deviations measured in different units cannot be summed directly due to the phenomenon of incommensurability.
Hence each unwanted deviation is multiplied by a normalisation constant to allow direct comparison. Popular choices for normalisation constants are the goal target value of the corresponding objective (hence turning all deviations into percentages) or the range of the corresponding objective (between the best and the worst possible values, hence mapping all deviations onto a zero-one range). For decision makers more interested in obtaining a balance between the competing objectives, Chebyshev goal programming is used. Introduced by Flavell in 1976, this variant seeks to minimise the maximum unwanted deviation, rather than the sum of deviations. This utilises the Chebyshev distance metric.
== Strengths and weaknesses ==
A major strength of goal programming is its simplicity and ease of use. This accounts for the large number of goal programming applications in many and diverse fields. Linear goal programmes can be solved using linear programming software as either a single linear programme, or in the case of the lexicographic variant, a series of connected linear programmes.
Goal programming can hence handle relatively large numbers of variab
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In computer programming, initialization or initialisation is the assignment of an initial value for a data object or variable. The manner in which initialization is performed depends on the programming language, as well as the type, storage class, etc., of an object to be initialized. Programming constructs which perform initialization are typically called initializers and initializer lists. Initialization is distinct from (and preceded by) declaration, although the two can sometimes be conflated in practice. The complement of initialization is finalization, which is primarily used for objects, but not variables.
Initialization is done either by statically embedding the value at compile time, or else by assignment at run time. A section of code that performs such initialization is generally known as "initialization code" and may include other, one-time-only, functions such as opening files; in object-oriented programming, initialization code may be part of a constructor (class method) or an initializer (instance method). Setting a memory location to hexadecimal zeroes is also sometimes known as "clearing" and is often performed by an exclusive or instruction (both operands specifying the same variable), at machine code level, since it requires no additional memory access.
== C family of languages ==
=== Initializer ===
In C/C99/C++, an initializer is an optional part of a declarator. It consists of the '=' character followed by an expression or a comma-separated list of expressions placed in curly brackets (braces). The latter list is sometimes called the "initializer list" or "initialization list" (although the term "initializer list" is formally reserved for initialization of class/struct members in C++; see below).
A declaration which creates a data object, instead of merely describing its existence, is commonly called a definition.
Many find it convenient to draw a distinction between the terms "declaration" and "definition", as in the commonly seen phrase "the distinction between a declaration and definition...", implying that a declaration merely designates a data object (or function). In fact, according to the C++ standard, a definition is a declaration. Still, the usage "declarations and definitions", although formally incorrect, is common. Although all definitions are declarations, not all declarations are definitions.
C examples:
C++ examples:
=== Initializer list ===
In C++, a constructor of a class/struct can have an initializer list within the definition but prior to the constructor body. It is important to note that when you use an initialization list, the values are not assigned to the variable. They are initialized. In the below example, 0 is initialized into re and im.
Example:
Here, the construct : re(0), im(0) is the initializer list.
Sometimes the term "initializer list" is also used to refer to the list of expressions in the array or struct initializer.
C++11 provides for a more powerful concept of initializer lists, by means of a template, called std::initializer_list.
=== Default initialization ===
Data initialization may occur without explicit syntax in a program to do so. For example, if static variables are declared without an initializer, then those of primitive data types are initialized with the value of zero of the corresponding type, while static objects of class type are initialized with their default constructors.
== See also ==
Object lifetime
Finalizer Process & related Finalization Pattern
== References ==
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Pair programming is a software development technique in which two programmers work together at one workstation. One, the driver, writes code while the other, the observer or navigator, reviews each line of code as it is typed in. The two programmers switch roles frequently.
While reviewing, the observer also considers the "strategic" direction of the work, coming up with ideas for improvements and likely future problems to address. This is intended to free the driver to focus all of their attention on the "tactical" aspects of completing the current task, using the observer as a safety net and guide.
== Economics ==
Pair programming increases the man-hours required to deliver code compared to programmers working individually. However, the resulting code has fewer defects. Along with code development time, other factors like field support costs and quality assurance also figure into the return on investment. Pair programming might theoretically offset these expenses by reducing defects in the programs.
In addition to preventing mistakes as they are made, other intangible benefits may exist. For example, the courtesy of rejecting phone calls or other distractions while working together, taking fewer breaks at agreed-upon intervals or sharing breaks to return phone calls (but returning to work quickly since someone is waiting). One member of the team might have more focus and help drive or awaken the other if they lose focus, and that role might periodically change. One member might know about a topic or technique that the other does not, which might eliminate delays to finding or testing a solution, or allow for a better solution, thus effectively expanding the skill set, knowledge, and experience of a programmer as compared to working alone. Each of these intangible benefits, and many more, may be challenging to accurately measure but can contribute to more efficient working hours.
== Design quality ==
A system with two programmers possesses greater potential for the generation of more diverse solutions to problems for three reasons:
the programmers bring different prior experiences to the task;
they may assess information relevant to the task in different ways;
they stand in different relationships to the problem by their functional roles.
In an attempt to share goals and plans, the programmers must overtly negotiate a shared course of action when a conflict arises between them. In doing so, they consider a larger number of ways of solving the problem than a single programmer alone might do. This significantly improves the design quality of the program as it reduces the chances of selecting a poor method.
== Satisfaction ==
In an online survey of pair programmers from 2000, 96% of programmers stated that they enjoyed working more while pair programming than programming alone. Furthermore, 95% said that they were more confident in their work when they pair programmed. However, as the survey was among self-selected pair programmers, it did not account for programmers who were forced to pair program.
== Learning ==
Knowledge is constantly shared between pair programmers, whether in the industry or in a classroom. Many sources suggest that students show higher confidence when programming in pairs, and many learn whether it be from tips on programming language rules to overall design skills. In "promiscuous pairing", each programmer communicates and works with all the other programmers on the team rather than pairing only with one partner, which causes knowledge of the system to spread throughout the whole team. Pair programming allows programmers to examine their partner's code and provide feedback, which is necessary to increase their own ability to develop monitoring mechanisms for their own learning activities.
== Team-building and communication ==
Pair programming allows team members to share quickly, making them less likely to have agendas hidden from each other. This helps pair programmers learn to communicat
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In computer science, a semaphore is a variable or abstract data type used to control access to a common resource by multiple threads and avoid critical section problems in a concurrent system such as a multitasking operating system. Semaphores are a type of synchronization primitive. A trivial semaphore is a plain variable that is changed (for example, incremented or decremented, or toggled) depending on programmer-defined conditions.
A useful way to think of a semaphore as used in a real-world system is as a record of how many units of a particular resource are available, coupled with operations to adjust that record safely (i.e., to avoid race conditions) as units are acquired or become free, and, if necessary, wait until a unit of the resource becomes available.
Though semaphores are useful for preventing race conditions, they do not guarantee their absence. Semaphores that allow an arbitrary resource count are called counting semaphores, while semaphores that are restricted to the values 0 and 1 (or locked/unlocked, unavailable/available) are called binary semaphores and are used to implement locks.
The semaphore concept was invented by Dutch computer scientist Edsger Dijkstra in 1962 or 1963, when Dijkstra and his team were developing an operating system for the Electrologica X8. That system eventually became known as the THE multiprogramming system.
== Library analogy ==
Suppose a physical library has ten identical study rooms, to be used by one student at a time. Students must request a room from the front desk. If no rooms are free, students wait at the desk until someone relinquishes a room. When a student has finished using a room, the student must return to the desk and indicate that the room is free.
In the simplest implementation, the clerk at the front desk knows only the number of free rooms available. This requires that all of the students use their room while they have signed up for it and return it when they are done. When a student requests a room, the clerk decreases this number. When a student releases a room, the clerk increases this number. The room can be used for as long as desired, and so it is not possible to book rooms ahead of time.
In this scenario, the front desk count-holder represents a counting semaphore, the rooms are the resource, and the students represent processes/threads. The value of the semaphore in this scenario is initially 10, with all rooms empty. When a student requests a room, they are granted access, and the value of the semaphore is changed to 9. After the next student comes, it drops to 8, then 7, and so on. If someone requests a room and the current value of the semaphore is 0, they are forced to wait until a room is freed (when the count is increased from 0). If one of the rooms was released, but there are several students waiting, then any method can be used to select the one who will occupy the room (like FIFO or randomly picking one). And of course, a student must inform the clerk about releasing their room only after really leaving it.
=== Important observations ===
When used to control access to a pool of resources, a semaphore tracks only how many resources are free. It does not keep track of which of the resources are free. Some other mechanism (possibly involving more semaphores) may be required to select a particular free resource.
The paradigm is especially powerful because the semaphore count may serve as a useful trigger for a number of different actions. The librarian above may turn the lights off in the study hall when there are no students remaining, or may place a sign that says the rooms are very busy when most of the rooms are occupied.
The success of the protocol requires applications to follow it correctly. Fairness and safety are likely to be compromised (which practically means a program may behave slowly, act erratically, hang, or crash) if even a single process acts incorrectly. This includes:
requesting a resource and forgetting to release it;
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In computer science, declarative programming is a programming paradigmβa style of building the structure and elements of computer programsβthat expresses the logic of a computation without describing its control flow.
Many languages that apply this style attempt to minimize or eliminate side effects by describing what the program must accomplish in terms of the problem domain, rather than describing how to accomplish it as a sequence of the programming language primitives (the how being left up to the language's implementation). This is in contrast with imperative programming, which implements algorithms in explicit steps.
Declarative programming often considers programs as theories of a formal logic, and computations as deductions in that logic space. Declarative programming may greatly simplify writing parallel programs.
Common declarative languages include those of database query languages (e.g., SQL, XQuery), regular expressions, logic programming (e.g. Prolog, Datalog, answer set programming), functional programming, configuration management, and algebraic modeling systems.
== Definition ==
Declarative programming is often defined as any style of programming that is not imperative. A number of other common definitions attempt to define it by simply contrasting it with imperative programming. For example:
A high-level program that describes what a computation should perform.
Any programming language that lacks side effects (or more specifically, is referentially transparent).
A language with a clear correspondence to mathematical logic.
These definitions overlap substantially.
Declarative programming is a non-imperative style of programming in which programs describe their desired results without explicitly listing commands or steps that must be performed. Functional and logic programming languages are characterized by a declarative programming style. In logic programming, programs consist of sentences expressed in logical form, and computation uses those sentences to solve problems, which are also expressed in logical form.
In a pure functional language, such as Haskell, all functions are without side effects, and state changes are only represented as functions that transform the state, which is explicitly represented as a first-class object in the program. Although pure functional languages are non-imperative, they often provide a facility for describing the effect of a function as a series of steps. Other functional languages, such as Lisp, OCaml and Erlang, support a mixture of procedural and functional programming.
Some logic programming languages, such as Prolog, and database query languages, such as SQL, while declarative in principle, also support a procedural style of programming.
== Subparadigms ==
Declarative programming is an umbrella term that includes a number of better-known programming paradigms.
=== Constraint programming ===
Constraint programming states relations between variables in the form of constraints that specify the properties of the target solution. The set of constraints is solved by giving a value to each variable so that the solution is consistent with the maximum number of constraints. Constraint programming often complements other paradigms: functional, logical, or even imperative programming.
=== Domain-specific languages ===
Well-known examples of declarative domain-specific languages (DSLs) include the yacc parser generator input language, QML, the Make build specification language, Puppet's configuration management language, regular expressions, Datalog, answer set programming and a subset of SQL (SELECT queries, for example). DSLs have the advantage of being useful while not necessarily needing to be Turing-complete, which makes it easier for a language to be purely declarative.
Many markup languages such as HTML, MXML, XAML, XSLT or other user-interface markup languages are often declarative. HTML, for example, only describes what should appear on a webpage - it specifies
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Modular programming is a software design technique that emphasizes separating the functionality of a program into independent, interchangeable modules, such that each contains everything necessary to execute only one aspect or "concern" of the desired functionality.
A module interface expresses the elements that are provided and required by the module. The elements defined in the interface are detectable by other modules. The implementation contains the working code that corresponds to the elements declared in the interface. Modular programming is closely related to structured programming and object-oriented programming, all having the same goal of facilitating construction of large software programs and systems by decomposition into smaller pieces, and all originating around the 1960s. While the historical usage of these terms has been inconsistent, "modular programming" now refers to the high-level decomposition of the code of an entire program into pieces: structured programming to the low-level code use of structured control flow, and object-oriented programming to the data use of objects, a kind of data structure.
In object-oriented programming, the use of interfaces as an architectural pattern to construct modules is known as interface-based programming.
== History ==
Modular programming, in the form of subsystems (particularly for I/O) and software libraries, dates to early software systems, where it was used for code reuse. Modular programming per se, with a goal of modularity, developed in the late 1960s and 1970s, as a larger-scale analog of the concept of structured programming (1960s). The term "modular programming" dates at least to the National Symposium on Modular Programming, organized at the Information and Systems Institute in July 1968 by Larry Constantine; other key concepts were information hiding (1972) and separation of concerns (SoC, 1974).
Modules were not included in the original specification for ALGOL 68 (1968), but were included as extensions in early implementations, ALGOL 68-R (1970) and ALGOL 68C (1970), and later formalized. One of the first languages designed from the start for modular programming was the short-lived Modula (1975), by Niklaus Wirth. Another early modular language was Mesa (1970s), by Xerox PARC, and Wirth drew on Mesa as well as the original Modula in its successor, Modula-2 (1978), which influenced later languages, particularly through its successor, Modula-3 (1980s). Modula's use of dot-qualified names, like M.a to refer to object a from module M, coincides with notation to access a field of a record (and similarly for attributes or methods of objects), and is now widespread, seen in C++, C#, Dart, Go, Java, OCaml, and Python, among others. Modular programming became widespread from the 1980s: the original Pascal language (1970) did not include modules, but later versions, notably UCSD Pascal (1978) and Turbo Pascal (1983) included them in the form of "units", as did the Pascal-influenced Ada (1980). The Extended Pascal ISO 10206:1990 standard kept closer to Modula2 in its modular support. Standard ML (1984) has one of the most complete module systems, including functors (parameterized modules) to map between modules.
In the 1980s and 1990s, modular programming was overshadowed by and often conflated with object-oriented programming, particularly due to the popularity of C++ and Java. For example, the C family of languages had support for objects and classes in C++ (originally C with Classes, 1980) and Objective-C (1983), only supporting modules 30 years or more later. Java (1995) supports modules in the form of packages, though the primary unit of code organization is a class. However, Python (1991) prominently used both modules and objects from the start, using modules as the primary unit of code organization and "packages" as a larger-scale unit; and Perl 5 (1994) includes support for both modules and objects, with a vast array of modules being available from CPAN (1993
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Programming is a form of music production and performance using electronic devices and computer software, such as sequencers and workstations or hardware synthesizers, sampler and sequencers, to generate sounds of musical instruments. These musical sounds are created through the use of music coding languages. There are many music coding languages of varying complexity. Music programming is also frequently used in modern pop and rock music from various regions of the world, and sometimes in jazz and contemporary classical music. It gained popularity in the 1950s and has been emerging ever since.
Music programming is the process in which a musician produces a sound or "patch" (be it from scratch or with the aid of a synthesizer/sampler), or uses a sequencer to arrange a song.
== Coding languages ==
Music coding languages are used to program the electronic devices to produce the instrumental sounds they make. Each coding language has its own level of difficulty and function.
=== Alda ===
The music coding language Alda provides a tutorial on coding music and is, "designed for musicians who do not know how to program, as well as programmers who do not know how to music". The website also has links to install, tutorial, cheat sheet, docs, and community for anyone visiting the website.
=== LC ===
LC computer music programming language is a more complex computer music programming language meant for more experienced coders. One of the differences between this language and other music coding languages is that, "Unlike existing unit-generator languages, LC provides objects as well as library functions and methods that can directly represent microsounds and related manipulations that are involved in microsound synthesis."
== History and development ==
Music programming has had a vast history of development leading to the creation of different programs and languages. Each development comes with more function and utility and each decade tends to favor a certain program and or piece of equipment.
=== MUSIC-N ===
The first digital synthesis family of computer programs and languages being MUSIC-N created by Max Mathews. The development of these programs, allowed for more flexibility and utility, eventually leading them to become fully developed languages. As programs such as MUSIC I, MUSIC II and MUSIC III were developed, which were all created by Max Matthews, new technologies were incorporated in such as the table-lookup oscillator in MUSIC II and the unit generator in MUSIC III. The breakthrough technologies such as the unit generator, which acted as a building block for music programming software, and the acoustic compiler, which allowed "unlimited number of sound synthesis structures to be created in the computer", further the complexity and evolution of music programming systems.
=== Drum machines ===
Around the time of the 1950s, electric rhythm machines began to make way into popular music. These machines began to gain much traction amongst many artists as they saw it as a way to create percussion sounds in an easier and more efficient way. Artists who used this kind of technology include J. J. Cale, Sly Stone, Phil Collins, Marvin Gaye, and Prince. Some of the popular drum machines through the time of the 1950s-1970s were the Side Man, Ace Tone's Rhythm Ace, Korg's Doncamatic, and Maestro's Rhythm King. In 1979, the LM-1 drum machine computer was released by guitarist Roger Linn, its goal being to help artists achieve realistic sounding drum sounds. This drum machine had eight different drum sounds: kick drum, snare, hi-hat, cabasa, tambourine, two tom toms, two congas, cowbell, clave, and handclaps. The different sounds could be recorded individually and they sounded real because of the high frequencies of the sound (28 kHz). Some notable artists who used the LM-1 were Peter Gabriel, Stevie Wonder, Michael Jackson, and Madonna. These developments continued to happen in future decades leading to the creation of new electr
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An integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear.
Integer programming is NP-complete. In particular, the special case of 0β1 integer linear programming, in which unknowns are binary, and only the restrictions must be satisfied, is one of Karp's 21 NP-complete problems.
If some decision variables are not discrete, the problem is known as a mixed-integer programming problem.
== Canonical and standard form for ILPs ==
In integer linear programming, the canonical form is distinct from the standard form. An integer linear program in canonical form is expressed thus (note that it is the
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{\displaystyle {\begin{aligned}&{\underset {\mathbf {x} \in \mathbb {Z} ^{n}}{\text{maximize}}}&&\mathbf {c} ^{\mathrm {T} }\mathbf {x} \\&{\text{subject to}}&&A\mathbf {x} \leq \mathbf {b} ,\\&&&\mathbf {x} \geq \mathbf {0} \end{aligned}}}
and an ILP in standard form is expressed as
maximize
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Literate programming is a programming paradigm introduced in 1984 by Donald Knuth in which a computer program is given as an explanation of how it works in a natural language, such as English, interspersed (embedded) with snippets of macros and traditional source code, from which compilable source code can be generated. The approach is used in scientific computing and in data science routinely for reproducible research and open access purposes. Literate programming tools are used by millions of programmers today.
The literate programming paradigm, as conceived by Donald Knuth, represents a move away from writing computer programs in the manner and order imposed by the compiler, and instead gives programmers macros to develop programs in the order demanded by the logic and flow of their thoughts. Literate programs are written as an exposition of logic in more natural language in which macros are used to hide abstractions and traditional source code, more like the text of an essay.
Literate programming (LP) tools are used to obtain two representations from a source file: one understandable by a compiler or interpreter, the "tangled" code, and another for viewing as formatted documentation, which is said to be "woven" from the literate source. While the first generation of literate programming tools were computer language-specific, the later ones are language-agnostic and exist beyond the individual programming languages.
== History and philosophy ==
Literate programming was first introduced in 1984 by Donald Knuth, who intended it to create programs that were suitable literature for human beings. He implemented it at Stanford University as a part of his research on algorithms and digital typography. The implementation was called "WEB" since he believed that it was one of the few three-letter words of English that had not yet been applied to computing. However, it resembles the complicated nature of software delicately pieced together from simple materials. The practice of literate programming has seen an important resurgence in the 2010s with the use of computational notebooks, especially in data science.
== Concept ==
Literate programming is writing out the program logic in a human language with included (separated by a primitive markup) code snippets and macros. Macros in a literate source file are simply title-like or explanatory phrases in a human language that describe human abstractions created while solving the programming problem, and hiding chunks of code or lower-level macros. These macros are similar to the algorithms in pseudocode typically used in teaching computer science. These arbitrary explanatory phrases become precise new operators, created on the fly by the programmer, forming a meta-language on top of the underlying programming language.
A preprocessor is used to substitute arbitrary hierarchies, or rather "interconnected 'webs' of macros", to produce the compilable source code with one command ("tangle"), and documentation with another ("weave"). The preprocessor also provides an ability to write out the content of the macros and to add to already created macros in any place in the text of the literate program source file, thereby disposing of the need to keep in mind the restrictions imposed by traditional programming languages or to interrupt the flow of thought.
=== Advantages ===
According to Knuth,
literate programming provides higher-quality programs, since it forces programmers to explicitly state the thoughts behind the program, making poorly thought-out design decisions more obvious. Knuth also claims that literate programming provides a first-rate documentation system, which is not an add-on, but is grown naturally in the process of exposition of one's thoughts during a program's creation. The resulting documentation allows the author to restart their own thought processes at any later time, and allows other programmers to understand the construction of the program more easily. This differs
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Fetal programming, also known as prenatal programming, is the theory that environmental cues experienced during fetal development play a seminal role in determining health trajectories across the lifespan.
Three main forms of programming that occur due to changes in the maternal environment are:
Changes in development that lead to greater disease risk;
Genetic changes that alter disease risk;
Epigenetic changes which alter disease risk of not only the child but also that of the next generation - i.e., after a famine, grandchildren of women who were pregnant during the famine, are born smaller than the normal size, despite nutritional deficiencies having been fulfilled.
These changes in the maternal environmental can be due to nutritional alteration, hormonal fluctuations or exposure to toxins.
== History ==
=== Dutch famine 1944β45 ===
In 1944β45, the German blockade of the Netherlands led to a lack of food supplies, causing the Dutch famine of 1944β45. The famine caused severe malnutrition among the population, including women in various stages of pregnancy. The Dutch Famine Birth Cohort Study examined the impact of lack of nutrition on children born during or after this famine. It showed that throughout their life, these children were at greater risk of diabetes, cardiovascular disease, obesity, and other non-communicable diseases.
=== Barker hypothesis ===
In the 1980s, David Barker began a research study on this topic. The Barker Hypothesis, or Thrifty phenotype, forms the basis for much of the research conducted on fetal programming. This hypothesis states that if the fetus is exposed to low nutrition, it will adapt to that environment. Nutrients are diverted towards the developing heart, brain, and other essential fetal organs. The body also undergoes metabolic alterations that ensure survival despite low nutrition but may cause problems with normal or high nutrition. This leads to increased risk of metabolic syndrome.
== Nutritional status ==
The developing fetus forms an impression of the world into which it will be born via its mother's nutritional status. Its development is thus modulated to create the best chance of survival. However, excessive or insufficient nutrition in the mother can provoke maladaptive developmental responses in the fetus, which in turn manifest in the form of post-natal diseases. This may have such a profound effect on the fetusβs adult life that it can even outweigh lifestyle factors.
=== Excessive nutrition ===
Body mass index before pregnancy and weight gain during pregnancy are linked to high blood pressure in the offspring during adulthood. Mouse models suggest that this is due to high levels of the fetal hormone leptin, which is present in the blood of individuals who are overweight or obese. There is a theory that this hormone hurts the regulatory systems of the fetus, and renders it impossible to maintain normal blood pressure levels.
=== Insufficient nutrition ===
Pre-eclampsia, involving oxygen deprivation and death of trophoblastic cells that make up most of the placenta, is a disease which is often associated with maladaptive long-term consequences of inappropriate fetal programming. Here, an inadequately developed and poorly functioning placenta fails to meet the fetusβs nutritional needs during gestation, either by altering its selection for nutrients that can cross into fetal blood or restricting total volume thereof. Consequences of this for the fetus in adult life include cardiovascular and metabolic conditions.
== Hormonal influence ==
A delicate balance of hormones during pregnancy is regarded as highly relevant to fetal programming and may significantly influence the outcome of the offspring. Placental endocrine transfer from the mother to the developing fetus could be altered by the mental state of the mother, due to affected glucocorticoid transfer that takes place across the placenta.
=== Thyroid ===
Thyroid hormones play an instrumental role during the
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Logic programming is a programming, database and knowledge representation paradigm based on formal logic. A logic program is a set of sentences in logical form, representing knowledge about some problem domain. Computation is performed by applying logical reasoning to that knowledge, to solve problems in the domain. Major logic programming language families include Prolog, Answer Set Programming (ASP) and Datalog. In all of these languages, rules are written in the form of clauses:
A :- B1, ..., Bn.
and are read as declarative sentences in logical form:
A if B1 and ... and Bn.
A is called the head of the rule, B1, ..., Bn is called the body, and the Bi are called literals or conditions. When n = 0, the rule is called a fact and is written in the simplified form:
A.
Queries (or goals) have the same syntax as the bodies of rules and are commonly written in the form:
?- B1, ..., Bn.
In the simplest case of Horn clauses (or "definite" clauses), all of the A, B1, ..., Bn are atomic formulae of the form p(t1 ,..., tm), where p is a predicate symbol naming a relation, like "motherhood", and the ti are terms naming objects (or individuals). Terms include both constant symbols, like "charles", and variables, such as X, which start with an upper case letter.
Consider, for example, the following Horn clause program:
Given a query, the program produces answers.
For instance for a query ?- parent_child(X, william), the single answer is
Various queries can be asked. For instance
the program can be queried both to generate grandparents and to generate grandchildren. It can even be used to generate all pairs of grandchildren and grandparents, or simply to check if a given pair is such a pair:
Although Horn clause logic programs are Turing complete, for most practical applications, Horn clause programs need to be extended to "normal" logic programs with negative conditions. For example, the definition of sibling uses a negative condition, where the predicate = is defined by the clause X = X :
Logic programming languages that include negative conditions have the knowledge representation capabilities of a non-monotonic logic.
In ASP and Datalog, logic programs have only a declarative reading, and their execution is performed by means of a proof procedure or model generator whose behaviour is not meant to be controlled by the programmer. However, in the Prolog family of languages, logic programs also have a procedural interpretation as goal-reduction procedures. From this point of view, clause A :- B1,...,Bn is understood as:
to solve A, solve B1, and ... and solve Bn.
Negative conditions in the bodies of clauses also have a procedural interpretation, known as negation as failure: A negative literal not B is deemed to hold if and only if the positive literal B fails to hold.
Much of the research in the field of logic programming has been concerned with trying to develop a logical semantics for negation as failure and with developing other semantics and other implementations for negation. These developments have been important, in turn, for supporting the development of formal methods for logic-based program verification and program transformation.
== History ==
The use of mathematical logic to represent and execute computer programs is also a feature of the lambda calculus, developed by Alonzo Church in the 1930s. However, the first proposal to use the clausal form of logic for representing computer programs was made by Cordell Green. This used an axiomatization of a subset of LISP, together with a representation of an input-output relation, to compute the relation by simulating the execution of the program in LISP. Foster and Elcock's Absys, on the other hand, employed a combination of equations and lambda calculus in an assertional programming language that places no constraints on the order in which operations are performed.
Logic programming, with its current syntax of facts and rules, can be traced back to debates in the lat
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The terms local programme, local programming, local content or local television refers to a television program made by a television station or independent television producer for broadcast only within the station's transmission area or television market. Local programmes can encompass the whole range of programme genres but will usually only cover subjects or people of particular interest to an audience within the station's coverage area.
For example, a local sports programme will present results, interviews and coverage of games or matches, just like a network sports programme, but it would only feature teams and players from within the broadcaster's transmission area.
In some cases a television network programme may include a local element as well. This is particularly the case in the United Kingdom and still happens today with Politics Show. The BBC regions will all opt-out at the same time from the main programme to present a locally produced segment.
Sometimes locally made programmes that are not too specific to the transmission area, will be sold to other local stations for broadcast in their region.
Historically there was a large percentage of local programming on television. Late in the 20th century this has significantly fallen. In many cases the only local programmes on a television station today will be the local newscast.
The above can also apply to radio. A national radio network may have local studios or affiliates who opt-out at various times to present local programs and content.
In the late-1950s, many of the early Australian television series such as Melbourne Magazine (1957), Sydney Tonight (1956β1959), and TV Talent Scout (1957β1958) were broadcast in only a single city.
== Canada ==
In Canada, historically local television stations produced a significant volume of local programming, including newscasts, locally or regionally oriented talk shows, and variety entertainment programs such as Tiny Talent Time or Homegrown Cafe; a few stations, such as CHCH-TV in Hamilton, Ontario and CJOH in Ottawa, also distributed some of their local programming more widely through television syndication, most notably CHCH's Hilarious House of Frightenstein and CJOH's You Can't Do That on Television, both of which were broadcast across both Canada and the United States.
With the cross-national consolidation of Canadian media ownership in the 1990s and 2000s, network-affiliated stations now rarely produce much more than their own local or regional newscasts, although some stations may continue to produce a small amount of additional local programming. Independent stations may produce more local programming, although such stations are now rare in the Canadian media landscape.
In radio, virtually all Canadian commercial radio stations are officially programmed locally, although many stations cut costs by contracting some dayparts out to voice-tracked hosts who are not actually located in the station's physical studio or even necessarily in the same city, using a home studio, and may even be performing their show from the United States. The CBC Radio One, CBC Radio 2, Ici Radio-Canada Première and Ici Musique networks consist primarily of networked national programming, although all include some degree of local programming in certain time blocks. Radio One and Première stations have a significant number of production centres which create and air their own local morning and/or afternoon talk shows, while Radio 2's and Ici Musique's local content is limited to local news and weather updates.
== United States ==
The term is also generally accepted to refer to television programming that is not produced by a broadcast or other media source for national or international distribution (broadcast syndication). Usually programming of local interest is produced by either a Public, educational, and government access (PEG) television organization, cable TV operator or broadcast network affiliate stations that offer local radio news
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Broadcast programming is the practice of organizing or ordering (scheduling) of broadcast media shows, typically radio and television, in a daily, weekly, monthly, quarterly, or season-long schedule.
Modern broadcasters use broadcast automation to regularly change the scheduling of their shows to build an audience for a new show, retain that audience, or compete with other broadcasters' shows. Most broadcast television shows are presented weekly in prime time or daily in other dayparts, though there are many exceptions.
At a micro level, scheduling is the minute planning of the transmission; what to broadcast and when, ensuring an adequate or maximum utilization of airtime. Television scheduling strategies are employed to give shows the best possible chance of attracting and retaining an audience. They are used to deliver shows to audiences when they are most likely to want to watch them and deliver audiences to advertisers in the composition that makes their advertising most likely to be effective.
With the growth of digital platforms and services allowing non-linear, on-demand access to television content, this approach to broadcasting has since been referred to using the retronym linear (such as linear television and linear channels).
== History ==
With the beginning of scheduled television in 1936, television programming was initially only concerned with filling a few hours each evening β the hours now known as prime time. Over time, though, television began to be seen during the daytime and late at night, as well on the weekends. As air time increased, so did the demand for new material. With the exception of sports television, variety shows became much more important in prime time.
== Scheduling strategies ==
=== Lead-ins and lead-outs ===
Broadcasters may schedule a program to air before or after a widely viewed tent-pole program, such as a popular series, or a special such as a high-profile sporting event (such as, in the United States, the Super Bowl), in the hope that audience flow will encourage the audience to tune-in early or stay for the second program. The second program is usually one that the broadcaster wants to promote to a wider audience, such as a new or lower-profile series. Sometimes, a lower-profile program may be scheduled between two tentpole programs, a technique known as hammocking.
Lead-outs can sometimes help to launch new programs and talent; in 1982, NBC premiered Late Night with David Letterman as a lead-out for its long-running late-night talk show The Tonight Show Starring Johnny Carson. Characterized by an off-beat style appealing to young adults, Late Night helped launch the career of host David Letterman, and influence later entries into the genre. Despite Carson's endorsement of Letterman as a successor following his 1992 retirement, NBC chose Jay Leno instead, and Letterman departed for CBS to host a spiritual successorβLate Show with David Lettermanβbeginning in the 1993β94 season. Late Night would continue as a franchise with hosts such as Conan O'Brien and Jimmy Fallonβboth of whom would later go on to host The Tonight Show.
The same season, Fox scheduled The X-Files as a lead-out for its sci-fi western The Adventures of Brisco County Jr., with the expectation that Brisco County Jr. would serve as the anchor of its Friday-night lineup. However, The X-Files proved to be significantly more successful, and would eventually run for nine seasons. By contrast, viewership for Brisco County Jr. declined throughout the season, and the show was cancelled. Fox attempted to use other sci-fi shows as a lead-in for The X-Files (such as Sliders and VR.5), but they were similarly unsuccessful.
A weak lead-in can have an impact on the viewership of programs that follow; NBC's 2009 attempt to strip the talk show The Jay Leno Show (a spiritual successor to Leno's tenure of The Tonight Show after Conan O'Brien succeeded him) in a 10:00 p.m. ET/PT timeslot proved detrimental to the viewership of lat
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Object-oriented programming (OOP) is a programming paradigm based on the concept of objects. Objects can contain data (called fields, attributes or properties) and have actions they can perform (called procedures or methods and implemented in code). In OOP, computer programs are designed by making them out of objects that interact with one another.
Many of the most widely used programming languages (such as C++, Java, and Python) support object-oriented programming to a greater or lesser degree, typically as part of multiple paradigms in combination with others such as imperative programming and declarative programming.
Significant object-oriented languages include Ada, ActionScript, C++, Common Lisp, C#, Dart, Eiffel, Fortran 2003, Haxe, Java, JavaScript, Kotlin, Logo, MATLAB, Objective-C, Object Pascal, Perl, PHP, Python, R, Raku, Ruby, Scala, SIMSCRIPT, Simula, Smalltalk, Swift, Vala and Visual Basic.NET.
== History ==
The idea of "objects" in programming started with the artificial intelligence group at MIT in the late 1950s and early 1960s. Here, "object" referred to LISP atoms with identified properties (attributes).
Another early example was Sketchpad created by Ivan Sutherland at MIT in 1960β1961. In the glossary of his technical report, Sutherland defined terms like "object" and "instance" (with the class concept covered by "master" or "definition"), albeit specialized to graphical interaction. Later, in 1968, AED-0, MIT's version of the ALGOL programming language, connected data structures ("plexes") and procedures, prefiguring what were later termed "messages", "methods", and "member functions".
Topics such as data abstraction and modular programming were common points of discussion at this time.
Meanwhile, in Norway, Simula was developed during the years 1961β1967. Simula introduced essential object-oriented ideas, such as classes, inheritance, and dynamic binding.
Simula was used mainly by researchers involved with physical modelling, like the movement of ships and their content through cargo ports. Simula is generally accepted as being the first language with the primary features and framework of an object-oriented language.
Influenced by both MIT and Simula, Alan Kay began developing his own ideas in November 1966. He would go on to create Smalltalk, an influential object-oriented programming language. By 1967, Kay was already using the term "object-oriented programming" in conversation. Although sometimes called the "father" of object-oriented programming, Kay has said his ideas differ from how object-oriented programming is commonly understood, and has implied that the computer science establishment did not adopt his notion.
A 1976 MIT memo co-authored by Barbara Liskov lists Simula 67, CLU, and Alphard as object-oriented languages, but does not mention Smalltalk.
In the 1970s, the first version of the Smalltalk programming language was developed at Xerox PARC by Alan Kay, Dan Ingalls and Adele Goldberg. Smalltalk-72 was notable for use of objects at the language level and its graphical development environment. Smalltalk was a fully dynamic system, allowing users to create and modify classes as they worked. Much of the theory of OOP was developed in the context of Smalltalk, for example multiple inheritance.
In the late 1970s and 1980s, object-oriented programming rose to prominence. The Flavors object-oriented Lisp was developed starting 1979, introducing multiple inheritance and mixins. In August 1981, Byte Magazine highlighted Smalltalk and OOP, introducing these ideas to a wide audience. LOOPS, the object system for Interlisp-D, was influenced by Smalltalk and Flavors, and a paper about it was published in 1982. In 1986, the first Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) was attended by 1,000 people. This conference marked the beginning of efforts to consolidate Lisp object systems, eventually resulting in the Common Lisp Object System. In the 1980s, ther
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In aspect and functional programming, advice describes a class of functions which modify other functions when the latter are run; it is a certain function, method or procedure that is to be applied at a given join point of a program.
== Use ==
The practical use of advice functions is generally to modify or otherwise extend the behavior of functions which cannot or should not be readily modified or extended. For instance, the Emacspeak Emacs-addon makes extensive use of advice: it must modify thousands of existing Emacs modules and functions such that it can produce audio output for the blind corresponding to the visual presentation, but it would be infeasible to copy all of them and redefine them to produce audio output in addition to their normal outputs; so, the Emacspeak programmers define advice functions which run before and after.
For a simple Emacs example: suppose after a user corrected a mis-spelled word using the Emacs ispell module, they wanted to re-spellcheck the entire buffer. ispell-word offers no such functionality, even if the corrected word appears frequently in the buffer. The user could track down the definition of ispell-word, copy it into their personal Emacs files, and add the desired functionality there, but this is tedious and, worse, brittle (the user's version is now out of sync with the core Emacs implementation, if it even works without further refactoring). What the user wants is quite simple β just to run another command any time ispell-word runs. Using advice, it can be done as simply as this:
While this example is obviously trivial, the strength of advice, especially when compared to similar facilities such as Python decorators and Java annotations, lies in the fact that not only do the advised functions / methods not need to be designed to accept advice, but also the advice themselves need not be designed to be usable as advice - they're just normal functions. The availability of evaluation throughout the lifetime of a piece of code (cf. code staging) in Lisp allows advice to be inlined automatically into any other code in a variety of ways. Any piece of code can be advised to carry out any other computation before, after, around, or instead of its original definition.
=== Readability ===
Advice has the potential to introduce confusion, as a piece of advice applied to a function is not apparent to a user who tracks down the function's source definition to learn about it. In such cases, advice acts almost like a COMEFROM, a joke facility added to INTERCAL to spoof the spaghettification attendant to the extensive use of GOTOs. In practice, however, such issues rarely present themselves. Upstream developers and maintainers of Lisp packages and modules never use advice, since there is no advantage to be gained by advising functions when their original source definitions can be freely rewritten to include the desired features. Advice is only useful in that it enables downstream users to subsequently modify default behaviour in a way that does not require propagation of such modifications into the core implementation's source definition.
== Implementations ==
A form of advices were part of C with Classes in the late 1970s and early 1980s, namely functions called call and return defined in a class, which were called before (respectively, after) member functions of the class. However, these were dropped from C++.
Advices are part of the Common Lisp Object System (CLOS), as :before, :after, and :around methods, which are combined with the primary method under "standard method combination".
Common Lisp implementations provide advice functionality (in addition to the standard method combination for CLOS) as extensions. LispWorks supports advising functions, macros and CLOS methods.
EmacsLisp added advice-related code in version 19.28, 1994.
== History ==
The following is taken from a discussion at the mailing list aosd-discuss. Pascal Costanza contributed the following:
The term "advice" goes b
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In computing, reactive programming is a declarative programming paradigm concerned with data streams and the propagation of change. With this paradigm, it is possible to express static (e.g., arrays) or dynamic (e.g., event emitters) data streams with ease, and also communicate that an inferred dependency within the associated execution model exists, which facilitates the automatic propagation of the changed data flow.
For example, in an imperative programming setting, a := b + c would mean that a is being assigned the result of b + c at the instant the expression is evaluated, and later, the values of b and c can be changed with no effect on the value of a. On the other hand, in reactive programming, the value of a is automatically updated whenever the values of b or c change, without the program having to explicitly re-state the statement a := b + c to re-assign the value of a.
Another example is a hardware description language such as Verilog, where reactive programming enables changes to be modeled as they propagate through circuits.
Reactive programming has been proposed as a way to simplify the creation of interactive user interfaces and near-real-time system animation.
For example, in a modelβviewβcontroller (MVC) architecture, reactive programming can facilitate changes in an underlying model being reflected automatically in an associated view.
== Approaches to creating reactive programming languages ==
Several popular approaches are employed in the creation of reactive programming languages. One approach is the specification of dedicated languages that are specific to various domain constraints. Such constraints usually are characterized by real-time, embedded computing or hardware description. Another approach involves the specification of general-purpose languages that include support for reactivity. Other approaches are articulated in the definition, and use of programming libraries, or embedded domain-specific languages, that enable reactivity alongside or on top of the programming language. Specification and use of these different approaches results in language capability trade-offs. In general, the more restricted a language is, the more its associated compilers and analysis tools are able to inform developers (e.g., in performing analysis for whether programs are able to execute in actual real-time). Functional trade-offs in specificity may result in deterioration of the general applicability of a language.
== Programming models and semantics ==
A variety of models and semantics govern reactive programming. We can loosely split them along the following dimensions:
Synchrony: synchronous versus asynchronous model of time
Determinism: deterministic versus non-deterministic evaluation process and results
Update process: callback versus dataflow versus actor
== Implementation techniques and challenges ==
=== Essence of implementations ===
Reactive programming language runtimes are represented by a graph that identifies the dependencies among the involved reactive values. In such a graph, nodes represent the act of computing, and edges model dependency relationships. Such a runtime employs said graph, to help it keep track of the various computations, which must be executed anew, once an involved input changes value.
==== Change propagation algorithms ====
The most common approaches to data propagation are:
Pull: The value consumer is in fact proactive, in that it regularly queries the observed source for values and reacts whenever a relevant value is available. This practice of regularly checking for events or value changes is commonly referred to as polling.
Push: The value consumer receives a value from the source whenever the value becomes available. These values are self-contained, e.g. they contain all the necessary information, and no further information needs to be queried by the consumer.
Push-pull: The value consumer receives a change notification, which is a short description of the change, e
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A symbol in computer programming is a primitive data type whose instances have a human-readable form. Symbols can be used as identifiers. In some programming languages, they are called atoms. Uniqueness is enforced by holding them in a symbol table. The most common use of symbols by programmers is to perform language reflection (particularly for callbacks), and the most common indirectly is their use to create object linkages.
In the most trivial implementation, they are essentially named integers; e.g., the enumerated type in C language.
== Support ==
The following programming languages provide runtime support for symbols:
=== Julia ===
Symbols in Julia are interned strings used to represent identifiers in parsed Julia code(ASTs) and as names or labels to identify entities (for example as keys in a dictionary).
=== Lisp ===
A symbol in Lisp is unique in a namespace (or package in Common Lisp). Symbols can be tested for equality with the function EQ. Lisp programs can generate new symbols at runtime. When Lisp reads data that contains textual represented symbols, existing symbols are referenced. If a symbol is unknown, the Lisp reader creates a new symbol.
In Common Lisp, symbols have the following attributes: a name, a value, a function, a list of properties and a package.
In Common Lisp it is also possible that a symbol is not interned in a package. Such symbols can be printed, but when read back, a new symbol needs to be created. Since it is not interned, the original symbol can not be retrieved from a package.
In Common Lisp symbols may use any characters, including whitespace, such as spaces and newlines. If a symbol contains a whitespace character, it needs to be written as |this is a symbol|. Symbols can be used as identifiers for any kind of named programming constructs: variables, functions, macros, classes, types, goto tags and more.
Symbols can be interned in a package. Keyword symbols are self-evaluating, and interned in the package named KEYWORD.
==== Examples ====
The following is a simple external representation of a Common Lisp symbol:
Symbols can contain whitespace (and all other characters):
In Common Lisp symbols with a leading colon in their printed representations are keyword symbols. These are interned in the keyword package.
A printed representation of a symbol may include a package name. Two colons are written between the name of the package and the name of the symbol.
Packages can export symbols. Then only one colon is written between the name of the package and the name of the symbol.
Symbols, which are not interned in a package, can also be created and have a notation:
=== PostScript ===
In PostScript, references to name objects can be either literal or executable, influencing the behaviour of the interpreter when encountering them. The cvx and cvl operators can be used to convert between the two forms. When names are constructed from strings by means of the cvn operator, the set of allowed characters is unrestricted.
=== Prolog ===
In Prolog, symbols (or atoms) are the main primitive data types, similar to numbers. The exact notation may differ in different Prolog dialects. However, it is always quite simple (no quotations or special beginning characters are necessary).
Contrary to many other languages, it is possible to give symbols a meaning by creating some Prolog facts and/or rules.
==== Examples ====
The following example demonstrates two facts (describing what father is) and one rule (describing the meaning of sibling). These three sentences use symbols (father, zeus, hermes, perseus and sibling) and some abstract variables (X, Y and Z). The mother relationship is omitted for clarity.
=== Ruby ===
In Ruby, symbols can be created with a literal form, or by converting a string.
They can be used as an identifier or an interned string. Two symbols with the same contents will always refer to the same object.
It is considered a best practice to use symbols as keys to an associa
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In computer programming, dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. Dataflow programming languages share some features of functional languages, and were generally developed in order to bring some functional concepts to a language more suitable for numeric processing. Some authors use the term datastream instead of dataflow to avoid confusion with dataflow computing or dataflow architecture, based on an indeterministic machine paradigm. Dataflow programming was pioneered by Jack Dennis and his graduate students at MIT in the 1960s.
== Considerations ==
Traditionally, a program is modelled as a series of operations happening in a specific order; this may be referred to as sequential,:βp.3β
procedural,
control flow (indicating that the program chooses a specific path), or imperative programming. The program focuses on commands, in line with the von Neumann:βp.3β vision of sequential programming, where data is normally "at rest".:βp.7β
In contrast, dataflow programming emphasizes the movement of data and models programs as a series of connections. Explicitly defined inputs and outputs connect operations, which function like black boxes.:βp.2β An operation runs as soon as all of its inputs become valid. Thus, dataflow languages are inherently parallel and can work well in large, decentralized systems.:βp.3β
=== State ===
One of the key concepts in computer programming is the idea of state, essentially a snapshot of various conditions in the system. Most programming languages require a considerable amount of state information, which is generally hidden from the programmer. Often, the computer itself has no idea which piece of information encodes the enduring state. This is a serious problem, as the state information needs to be shared across multiple processors in parallel processing machines. Most languages force the programmer to add extra code to indicate which data and parts of the code are important to the state. This code tends to be both expensive in terms of performance, as well as difficult to read or debug. Explicit parallelism is one of the main reasons for the poor performance of Enterprise Java Beans when building data-intensive, non-OLTP applications.
Where a sequential program can be imagined as a single worker moving between tasks (operations), a dataflow program is more like a series of workers on an assembly line, each doing a specific task whenever materials are available. Since the operations are only concerned with the availability of data inputs, they have no hidden state to track, and are all "ready" at the same time.
=== Representation ===
Dataflow programs are represented in different ways. A traditional program is usually represented as a series of text instructions, which is reasonable for describing a serial system which pipes data between small, single-purpose tools that receive, process, and return. Dataflow programs start with an input, perhaps the command line parameters, and illustrate how that data is used and modified. The flow of data is explicit, often visually illustrated as a line or pipe.
In terms of encoding, a dataflow program might be implemented as a hash table, with uniquely identified inputs as the keys, used to look up pointers to the instructions. When any operation completes, the program scans down the list of operations until it finds the first operation where all inputs are currently valid, and runs it. When that operation finishes, it will typically output data, thereby making another operation become valid.
For parallel operation, only the list needs to be shared; it is the state of the entire program. Thus the task of maintaining state is removed from the programmer and given to the language's runtime. On machines with a single processor core where an implementation designed for parallel operation would simply introduce overhead, this
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A computer program is a sequence or set of instructions in a programming language for a computer to execute. It is one component of software, which also includes documentation and other intangible components.
A computer program in its human-readable form is called source code. Source code needs another computer program to execute because computers can only execute their native machine instructions. Therefore, source code may be translated to machine instructions using a compiler written for the language. (Assembly language programs are translated using an assembler.) The resulting file is called an executable. Alternatively, source code may execute within an interpreter written for the language.
If the executable is requested for execution, then the operating system loads it into memory and starts a process. The central processing unit will soon switch to this process so it can fetch, decode, and then execute each machine instruction.
If the source code is requested for execution, then the operating system loads the corresponding interpreter into memory and starts a process. The interpreter then loads the source code into memory to translate and execute each statement. Running the source code is slower than running an executable. Moreover, the interpreter must be installed on the computer.
== Example computer program ==
The "Hello, World!" program is used to illustrate a language's basic syntax. The syntax of the language BASIC (1964) was intentionally limited to make the language easy to learn. For example, variables are not declared before being used. Also, variables are automatically initialized to zero. Here is an example computer program, in Basic, to average a list of numbers:
Once the mechanics of basic computer programming are learned, more sophisticated and powerful languages are available to build large computer systems.
== History ==
Improvements in software development are the result of improvements in computer hardware. At each stage in hardware's history, the task of computer programming changed dramatically.
=== Analytical Engine ===
In 1837, Jacquard's loom inspired Charles Babbage to attempt to build the Analytical Engine.
The names of the components of the calculating device were borrowed from the textile industry. In the textile industry, yarn was brought from the store to be milled. The device had a store which consisted of memory to hold 1,000 numbers of 50 decimal digits each. Numbers from the store were transferred to the mill for processing. The engine was programmed using two sets of perforated cards. One set directed the operation and the other set inputted the variables. However, the thousands of cogged wheels and gears never fully worked together.
Ada Lovelace worked for Charles Babbage to create a description of the Analytical Engine (1843). The description contained Note G which completely detailed a method for calculating Bernoulli numbers using the Analytical Engine. This note is recognized by some historians as the world's first computer program.
=== Universal Turing machine ===
In 1936, Alan Turing introduced the Universal Turing machine, a theoretical device that can model every computation.
It is a finite-state machine that has an infinitely long read/write tape. The machine can move the tape back and forth, changing its contents as it performs an algorithm. The machine starts in the initial state, goes through a sequence of steps, and halts when it encounters the halt state. All present-day computers are Turing complete.
=== ENIAC ===
The Electronic Numerical Integrator And Computer (ENIAC) was built between July 1943 and Fall 1945. It was a Turing complete, general-purpose computer that used 17,468 vacuum tubes to create the circuits. At its core, it was a series of Pascalines wired together. Its 40 units weighed 30 tons, occupied 1,800 square feet (167 m2), and consumed $650 per hour (in 1940s currency) in electricity when idle. It had 20 base-10 accumulators. Programming t
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In computer programming, a directive or pragma (from "pragmatic") is a language construct that specifies how a compiler (or other translator) should process its input. Depending on the programming language, directives may or may not be part of the grammar of the language and may vary from compiler to compiler. They can be processed by a preprocessor to specify compiler behavior, or function as a form of in-band parameterization.
In some cases directives specify global behavior, while in other cases they only affect a local section, such as a block of programming code. In some cases, such as some C programs, directives are optional compiler hints and may be ignored, but normally they are prescriptive and must be followed. However, a directive does not perform any action in the language itself, but rather only a change in the behavior of the compiler.
This term could be used to refer to proprietary third-party tags and commands (or markup) embedded in code that result in additional executable processing that extend the existing compiler, assembler and language constructs present in the development environment. The term "directive" is also applied in a variety of ways that are similar to the term command.
== The C preprocessor ==
In C and C++, the language supports a simple macro preprocessor. Source lines that should be handled by the preprocessor, such as #define and #include are referred to as preprocessor directives.
Syntactic constructs similar to C's preprocessor directives, such as C#'s #if, are also typically called "directives", although in these cases there may not be any real preprocessing phase involved.
All preprocessor commands, except for defined (when following a conditional directive), begin with a hash symbol (#). Until C++26, the keywords export, import and module were partially handled by the preprocessor.
== History ==
Directives date to JOVIAL.
COBOL has a COPY directive.
In ALGOL 68, directives are known as pragmats (from "pragmatic"), and denoted pragmat or pr; in newer languages, notably C, this has been abbreviated to "pragma" (no 't').
A common use of pragmats in ALGOL 68 is in specifying a stropping regime, meaning "how keywords are indicated". Various such directives follow, specifying the POINT, UPPER, RES (reserved), or quote regimes. Note the use of stropping for the pragmat keyword itself (abbreviated pr), either in the POINT or quote regimes:
Today directives are best known in the C language, of early 1970s vintage, and continued through the current C99 standard, where they are either instructions to the C preprocessor, or, in the form of #pragma, directives to the compiler itself. They are also used to some degree in more modern languages; see below.
== Other languages ==
In Ada, compiler directives are called pragmas (short for "pragmatic information").
In Common Lisp, directives are called declarations, and are specified using the declare construct (also proclaim or declaim). With one exception, declarations are optional, and do not affect the semantics of the program. The one exception is special, which must be specified where appropriate.
In Turbo Pascal, directives are called significant comments, because in the language grammar they follow the same syntax as comments. In Turbo Pascal, a significant comment is a comment whose first character is a dollar sign and whose second character is a letter; for example, the equivalent of C's #include "file" directive is the significant comment {$I "file"}.
In Perl, the keyword "use", which imports modules, can also be used to specify directives, such as use strict; or use utf8;.
Haskell pragmas are specified using a specialized comment syntax, e.g. {-# INLINE foo #-}. It is also possible to use the C preprocessor in Haskell, by writing {-# LANGUAGE CPP #-}.
PHP uses the directive declare(strict_types=1).
In PL/I, directives begin with a Percent sign (%) and end with a semicolon (;), e.g., %INCLUDE foo;, %NOPRINT;, %PAGE;, %POP;, %SKIP;, the
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A "Hello, World!" program is usually a simple computer program that emits (or displays) to the screen (often the console) a message similar to "Hello, World!". A small piece of code in most general-purpose programming languages, this program is used to illustrate a language's basic syntax. Such a program is often the first written by a student of a new programming language, but it can also be used as a sanity check to ensure that the computer software intended to compile or run source code is correctly installed, and that its operator understands how to use it.
== History ==
While several small test programs have existed since the development of programmable computers, the tradition of using the phrase "Hello, World!" as a test message was influenced by an example program in the 1978 book The C Programming Language, with likely earlier use in BCPL. The example program from the book prints "hello, world", and was inherited from a 1974 Bell Laboratories internal memorandum by Brian Kernighan, Programming in C: A Tutorial:
In the above example, the main( ) function defines where the program should start executing. The function body consists of a single statement, a call to the printf() function, which stands for "print formatted"; it outputs to the console whatever is passed to it as the parameter, in this case the string "hello, world".
The C-language version was preceded by Kernighan's own 1972 A Tutorial Introduction to the Language B, where the first known version of the program is found in an example used to illustrate external variables:
The program above prints hello, world! on the terminal, including a newline character. The phrase is divided into multiple variables because in B a character constant is limited to four ASCII characters. The previous example in the tutorial printed hi! on the terminal, and the phrase hello, world! was introduced as a slightly longer greeting that required several character constants for its expression.
The Jargon File reports that "hello, world" instead originated in 1967 with the language BCPL. Outside computing, use of the exact phrase began over a decade prior; it was the catchphrase of New York radio disc jockey William B. Williams beginning in the 1950s.
== Variations ==
"Hello, World!" programs vary in complexity between different languages. In some languages, particularly scripting languages, the "Hello, World!" program can be written as one statement, while in others (more so many low-level languages) many more statements can be required. For example, in Python, to print the string Hello, World! followed by a newline, one only needs to write print("Hello, World!"). In contrast, the equivalent code in C++ requires the import of the input/output (I/O) software library, the manual declaration of an entry point, and the explicit instruction that the output string should be sent to the standard output stream.
The phrase "Hello, World!" has seen various deviations in casing and punctuation, such as "hello world" which lacks the capitalization of the leading H and W, and the presence of the comma or exclamation mark. Some devices limit the format to specific variations, such as all-capitalized versions on systems that support only capital letters, while some esoteric programming languages may have to print a slightly modified string. Other human languages have been used as the output; for example, a tutorial for the Go language emitted both English and Chinese or Japanese characters, demonstrating the language's built-in Unicode support. Another notable example is the Rust language, whose management system automatically inserts a "Hello, World" program when creating new projects.
Some languages change the function of the "Hello, World!" program while maintaining the spirit of demonstrating a simple example. Functional programming languages, such as Lisp, ML, and Haskell, tend to substitute a factorial program for "Hello, World!", as functional programming emphasizes recursive tec
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https://en.wikipedia.org/wiki/%22Hello,_World!%22_program
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Differentiable programming is a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation. This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher-order derivative information. Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.
== Approaches ==
Most differentiable programming frameworks work by constructing a graph containing the control flow and data structures in the program. Attempts generally fall into two groups:
Static, compiled graph-based approaches such as TensorFlow, Theano, and MXNet. They tend to allow for good compiler optimization and easier scaling to large systems, but their static nature limits interactivity and the types of programs that can be created easily (e.g. those involving loops or recursion), as well as making it harder for users to reason effectively about their programs. A proof-of-concept compiler toolchain called Myia uses a subset of Python as a front end and supports higher-order functions, recursion, and higher-order derivatives.
Operator overloading, dynamic graph-based approaches such as PyTorch, NumPy's autograd package, and Pyaudi. Their dynamic and interactive nature lets most programs be written and reasoned about more easily. However, they lead to interpreter overhead (particularly when composing many small operations), poorer scalability, and reduced benefit from compiler optimization.
The use of just-in-time compilation has emerged recently as a possible solution to overcome some of the bottlenecks of interpreted languages. The C++ heyoka and Python package heyoka.py make large use of this technique to offer advanced differentiable programming capabilities (also at high orders). A package for the Julia programming language β Zygote β works directly on Julia's intermediate representation.
A limitation of earlier approaches is that they are only able to differentiate code written in a suitable manner for the framework, limiting their interoperability with other programs. Newer approaches resolve this issue by constructing the graph from the language's syntax or IR, allowing arbitrary code to be differentiated.
== Applications ==
Differentiable programming has been applied in areas such as combining deep learning with physics engines in robotics, solving electronic-structure problems with differentiable density functional theory, differentiable ray tracing, differentiable imaging, image processing, and probabilistic programming.
== Multidisciplinary application ==
Differentiable programming is making significant strides in various fields beyond its traditional applications. In healthcare and life sciences, for example, it is being used for deep learning in biophysics-based modelling of molecular mechanisms, in areas such as protein structure prediction and drug discovery. These applications demonstrate the potential of differentiable programming in contributing to significant advancements in understanding complex biological systems and improving healthcare solutions.
== See also ==
Differentiable function
Machine learning
== Notes ==
== References ==
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https://en.wikipedia.org/wiki/Differentiable_programming
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Offensive programming is a name used for the branch of defensive programming that expressly departs from defensive principles when dealing with errors resulting from software bugs. Although the name is a reaction to extreme interpretations of defensive programming, the two are not fundamentally in conflict. Rather, offensive programming adds an explicit priority of not tolerating errors in wrong places: the point where it departs from extreme interpretations of defensive programming is in preferring the presence of errors from within the program's line of defense to be blatantly obvious over the hypothetical safety benefit of tolerating them. This preference is also what justifies using assertions.
== Distinguishing errors ==
The premise for offensive programming is to distinguish between expectable errors, coming from outside the program's line of defense, however improbable, versus preventable internal errors that shall not happen if all its software components behave as expected.
Contrasting examples:
== Bug detection strategies ==
Offensive programming is concerned with failing, so to disprove the programmer's assumptions. Producing an error message may be a secondary goal.
Strategies:
No unnecessary checks: Trusting that other software components behave as specified, so to not paper over any unknown problem, is the basic principle. In particular, some errors may already be guaranteed to crash the program (depending on programming language or running environment), for example dereferencing a null pointer. As such, null pointer checks are unnecessary for the purpose of stopping the program (but can be used to print error messages).
Assertions β checks that can be disabled β are the preferred way to check things that should be unnecessary to check, such as design contracts between software components.
Remove fallback code (limp mode) and fallback data (default values): These can hide defects in the main implementation, or, from the user point of view, hide the fact that the software is working suboptimally. Special attention to unimplemented parts may be needed as part of factory acceptance testing, as yet unimplemented code is at no stage of test driven development discoverable by failing unit tests.
Remove shortcut code (see the strategy pattern): A simplified code path may hide bugs in a more generic code path if the generic code almost never gets to run. Since the two are supposed to produce the same result, the simplified one can be eliminated.
== See also ==
Fail-fast system
== References ==
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https://en.wikipedia.org/wiki/Offensive_programming
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Neuro-linguistic programming (NLP) is a pseudoscientific approach to communication, personal development, and psychotherapy that first appeared in Richard Bandler and John Grinder's book The Structure of Magic I (1975). NLP asserts a connection between neurological processes, language, and acquired behavioral patterns, and that these can be changed to achieve specific goals in life. According to Bandler and Grinder, NLP can treat problems such as phobias, depression, tic disorders, psychosomatic illnesses, near-sightedness, allergy, the common cold, and learning disorders, often in a single session. They also say that NLP can model the skills of exceptional people, allowing anyone to acquire them.
NLP has been adopted by some hypnotherapists as well as by companies that run seminars marketed as leadership training to businesses and government agencies.
No scientific evidence supports the claims made by NLP advocates, and it has been called a pseudoscience. Scientific reviews have shown that NLP is based on outdated metaphors of the brain's inner workings that are inconsistent with current neurological theory, and that NLP contains numerous factual errors. Reviews also found that research that favored NLP contained significant methodological flaws, and that three times as many studies of a much higher quality failed to reproduce the claims made by Bandler, Grinder, and other NLP practitioners.
== Early development ==
According to Bandler and Grinder, NLP consists of a methodology termed modeling, plus a set of techniques that they derived from its initial applications. They derived many of the fundamental techniques from the work of Virginia Satir, Milton Erickson and Fritz Perls. Bandler and Grinder also drew upon the theories of Gregory Bateson, and Noam Chomsky (particularly transformational grammar).
Bandler and Grinder say that their methodology can codify the structure inherent to the therapeutic "magic" as performed in therapy by Perls, Satir and Erickson, and indeed inherent to any complex human activity. From that codification, they say, the structure and its activity can be learned by others. Their 1975 book, The Structure of Magic I: A Book about Language and Therapy, is intended to be a codification of the therapeutic techniques of Perls and Satir.
Bandler and Grinder say that they used their own process of modeling to model Virginia Satir so they could produce what they termed the Meta-Model, a model for gathering information and challenging a client's language and underlying thinking. They say that by challenging linguistic distortions, specifying generalizations, and recovering deleted information in the client's statements, the transformational grammar concept of surface structure yields a more complete representation of the underlying deep structure and therefore has therapeutic benefit. Also derived from Satir were anchoring, future pacing and representational systems.
In contrast, the Milton-Modelβa model of the purportedly hypnotic language of Milton Ericksonβwas described by Bandler and Grinder as "artfully vague" and metaphoric. The Milton-Model is used in combination with the Meta-Model as a softener, to induce "trance" and to deliver indirect therapeutic suggestion.
Psychologist Jean Mercer writes that Chomsky's theories "appear to be irrelevant" to NLP. Linguist Karen Stollznow describes Bandler's and Grinder's reference to such experts as namedropping. Other than Satir, the people they cite as influences did not collaborate with Bandler or Grinder. Chomsky himself has no association with NLP, with his work being theoretical in nature and having no therapeutic element. Stollznow writes, "[o]ther than borrowing terminology, NLP does not bear authentic resemblance to any of Chomsky's theories or philosophiesβlinguistic, cognitive or political."
According to AndrΓ© Muller Weitzenhoffer, a researcher in the field of hypnosis, "the major weakness of Bandler and Grinder's linguistic analysis is that so much
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https://en.wikipedia.org/wiki/Neuro-linguistic_programming
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Brokered programming (also known as time-buy and blocktime) is a form of broadcast content in which the show's producer pays a radio or television station for air time, rather than exchanging programming for pay or the opportunity to play spot commercials. A brokered program is typically not capable of garnering enough support from advertisements to pay for itself, and may be controversial, esoteric or an advertisement in itself.
== Overview ==
=== Common examples ===
Common examples are religious and political programs and talk-show-format programs similar to infomercial on television. Others are hobby programs or vanity programs paid for by the host and/or their supporters, and may be intended to promote the host's personality, for instance in preparation for a political campaign, or to promote a product, service or business that the host is closely associated with. A live vanity show may be carried on several stations by remote broadcast or simulcast, with the producer paying multiple stations an airtime fee. Financial advisors and planners often produce this kind of programming.
Brokered commercial programs promote products or services by scripting shows made to sound similar to talk radio or news programming, and may even include calls from actual listeners (or actors playing the part of listeners). The programs are a specific type of infomercial, as they focus on a topic related to the product and repeatedly steer listeners and "callers" to a particular website and/or toll-free telephone number in order to purchase the product being featured. Although presented in the style of live programs, these are typically pre-recorded and supplied to stations on tape, disc, or digital downloadable formats, such as MP3 files.
Such programming is most common on talk radio stations and used to fill non-prime time slots and to augment income from spot-advertisement sales during normal programs.
Most of these programs feature a disclaimer at either the beginning or the end of the program (or both), usually read by the program's host or (most often) by a separate announcer; some radio stations play a standard disclaimer before all such programs.
Certain mainstream sports and entertainment broadcasts may resort to buying brokered airtime to air on television if they cannot secure a deal that pays rights fees or a barter agreement. Examples include the last years of the Professional Bowlers Tour, Major League Baseball's short-lived The Baseball Network venture in the mid-1990s, professional football leagues such as the United Football League and Alliance of American Football, and motorsports events produced and sponsored by Lucas Oil. In the case of professional football, brokered programming has typically not been feasible in the long term, as the sport requires rights fees to make it viable; leagues that have relied on brokering television time have collapsed in short order due to financial losses. Regional sports networks also pad their non-play-by-play schedule with brokered shows catering to niches like high school sports, poker, and all-terrain vehicles. Some packages of high school football and basketball games are brokered more with the specific purposes of college recruiting and future name, image, and likeness deals in mind rather than the actual team matchup, which is mainly prevalent with nationally-ranked high school athletic powers that do not play traditional local schedules against local opponents and highlight certain heavily-recruited players.
=== Radio ===
==== Talk radio ====
Although some syndicators of multi-topic, ad-supported talk shows may pay a fee to stations with very large Arbitron-verified listenership, the same syndicator will normally charge a fee to small stations and may charge nothing to stations with moderate listenership. Each arrangement depends on whether the station can deliver enough listeners to allow the syndicator to earn money from ad sales. Syndicated programs normally carry a number of
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https://en.wikipedia.org/wiki/Brokered_programming
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"Epigrams on Programming" is an article by Alan Perlis published in 1982, for ACM's SIGPLAN journal. The epigrams are a series of short, programming-language-neutral, humorous statements about computers and programming, which are widely quoted.
It first appeared in SIGPLAN Notices 17(9), September 1982.
In epigram #54, Perlis coined the term "Turing tarpit", which he defined as a programming language where "everything is possible but nothing of interest is easy."
== References ==
Perlis, A. J. (September 1982). "Epigrams on programming". ACM SIGPLAN Notices. 17 (9). New York, NY, USA: Association for Computing Machinery: 7β13. doi:10.1145/947955.1083808. Archived from the original on January 17, 1999.
== External links ==
List of quotes (Yale)
Full article text -- (including so-called "meta epigrams", numbers 122-130)
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https://en.wikipedia.org/wiki/Epigrams_on_Programming
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Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed automatically. Probabilistic programming attempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable. It can be used to create systems that help make decisions in the face of uncertainty. Programming languages following the probabilistic programming paradigm are referred to as "probabilistic programming languages" (PPLs).
== Applications ==
Probabilistic reasoning has been used for a wide variety of tasks such as predicting stock prices, recommending movies, diagnosing computers, detecting cyber intrusions and image detection. However, until recently (partially due to limited computing power), probabilistic programming was limited in scope, and most inference algorithms had to be written manually for each task.
Nevertheless, in 2015, a 50-line probabilistic computer vision program was used to generate 3D models of human faces based on 2D images of those faces. The program used inverse graphics as the basis of its inference method, and was built using the Picture package in Julia. This made possible "in 50 lines of code what used to take thousands".
The Gen probabilistic programming library (also written in Julia) has been applied to vision and robotics tasks.
More recently, the probabilistic programming system Turing.jl has been applied in various pharmaceutical and economics applications.
Probabilistic programming in Julia has also been combined with differentiable programming by combining the Julia package Zygote.jl with Turing.jl.
Probabilistic programming languages are also commonly used in Bayesian cognitive science to develop and evaluate models of cognition.
== Probabilistic programming languages ==
PPLs often extend from a basic language. For instance, Turing.jl is based on Julia, Infer.NET is based on .NET Framework, while PRISM extends from Prolog. However, some PPLs, such as WinBUGS, offer a self-contained language that maps closely to the mathematical representation of the statistical models, with no obvious origin in another programming language.
The language for WinBUGS was implemented to perform Bayesian computation using Gibbs Sampling and related algorithms. Although implemented in a relatively unknown programming language (Component Pascal), this language permits Bayesian inference for a wide variety of statistical models using a flexible computational approach. The same BUGS language may be used to specify Bayesian models for inference via different computational choices ("samplers") and conventions or defaults, using a standalone program WinBUGS (or related R packages, rbugs and r2winbugs) and JAGS (Just Another Gibbs Sampler, another standalone program with related R packages including rjags, R2jags, and runjags). More recently, other languages to support Bayesian model specification and inference allow different or more efficient choices for the underlying Bayesian computation, and are accessible from the R data analysis and programming environment, e.g.: Stan, NIMBLE and NUTS. The influence of the BUGS language is evident in these later languages, which even use the same syntax for some aspects of model specification.
Several PPLs are in active development, including some in beta test. Two popular tools are Stan and PyMC.
=== Relational ===
A probabilistic relational programming language (PRPL) is a PPL specially designed to describe and infer with probabilistic relational models (PRMs).
A PRM is usually developed with a set of algorithms for reducing, inference about and discovery of concerned distributions, which are embedded into the corresponding PRPL.
=== Probabilistic logic programming ===
Probabilistic logic programming is a programming paradigm that extends logic programming with probabilities.
Most approaches to probabilistic logic programming ar
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A programming paradigm is a relatively high-level way to conceptualize and structure the implementation of a computer program. A programming language can be classified as supporting one or more paradigms.
Paradigms are separated along and described by different dimensions of programming. Some paradigms are about implications of the execution model, such as allowing side effects, or whether the sequence of operations is defined by the execution model. Other paradigms are about the way code is organized, such as grouping into units that include both state and behavior. Yet others are about syntax and grammar.
Some common programming paradigms include (shown in hierarchical relationship):
Imperative β code directly controls execution flow and state change, explicit statements that change a program state
procedural β organized as procedures that call each other
object-oriented β organized as objects that contain both data structure and associated behavior, uses data structures consisting of data fields and methods together with their interactions (objects) to design programs
Class-based β object-oriented programming in which inheritance is achieved by defining classes of objects, versus the objects themselves
Prototype-based β object-oriented programming that avoids classes and implements inheritance via cloning of instances
Declarative β code declares properties of the desired result, but not how to compute it, describes what computation should perform, without specifying detailed state changes
functional β a desired result is declared as the value of a series of function evaluations, uses evaluation of mathematical functions and avoids state and mutable data
logic β a desired result is declared as the answer to a question about a system of facts and rules, uses explicit mathematical logic for programming
reactive β a desired result is declared with data streams and the propagation of change
Concurrent programming β has language constructs for concurrency, these may involve multi-threading, support for distributed computing, message passing, shared resources (including shared memory), or futures
Actor programming β concurrent computation with actors that make local decisions in response to the environment (capable of selfish or competitive behaviour)
Constraint programming β relations between variables are expressed as constraints (or constraint networks), directing allowable solutions (uses constraint satisfaction or simplex algorithm)
Dataflow programming β forced recalculation of formulas when data values change (e.g. spreadsheets)
Distributed programming β has support for multiple autonomous computers that communicate via computer networks
Generic programming β uses algorithms written in terms of to-be-specified-later types that are then instantiated as needed for specific types provided as parameters
Metaprogramming β writing programs that write or manipulate other programs (or themselves) as their data, or that do part of the work at compile time that would otherwise be done at runtime
Template metaprogramming β metaprogramming methods in which a compiler uses templates to generate temporary source code, which is merged by the compiler with the rest of the source code and then compiled
Reflective programming β metaprogramming methods in which a program modifies or extends itself
Pipeline programming β a simple syntax change to add syntax to nest function calls to language originally designed with none
Rule-based programming β a network of rules of thumb that comprise a knowledge base and can be used for expert systems and problem deduction & resolution
Visual programming β manipulating program elements graphically rather than by specifying them textually (e.g. Simulink); also termed diagrammatic programming'
Online presence management is distinct from
Web presence management in that the former is generally a marketing and messaging discipline while the latter is Governance,risk management, and compliance operational and s
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https://en.wikipedia.org/wiki/Programming_paradigm
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A programming tool or software development tool is a computer program that is used to develop another computer program, usually by helping the developer manage computer files. For example, a programmer may use a tool called a source code editor to edit source code files, and then a compiler to convert the source code into machine code files. They may also use build tools that automatically package executable program and data files into shareable packages or install kits.
A set of tools that are run one after another, with each tool feeding its output to the next one, is called a toolchain. An integrated development environment (IDE) integrates the function of several tools into a single program. Usually, an IDE provides a source code editor as well as other built-in or plug-in tools that help with compiling, debugging, and testing.
Whether a program is considered a development tool can be subjective. Some programs, such as the GNU compiler collection, are used exclusively for software development while others, such as Notepad, are not meant specifically for development but are nevertheless often used for programming.
== Categories ==
Notable categories of development tools:
Assembler β Converts assembly language into machine code
Bug tracking system β Software application that records software bugs
Build automation β Building software via an unattended fashion
Code review software β Activity where one or more people check a program's code
Compiler β Computer program which translates code from one programming language to another
Compiler-compiler β Program that generates parsers or compilers, a.k.a. parser generator
Debugger β Computer program used to test and debug other programs
Decompiler β Program translating executable to source code
Disassembler β Computer program to translate machine language into assembly language
Documentation generator β Automation technology for creating software documentation
Graphical user interface builder β Software development tool
Linker β Program that combines intermediate build files into an executable file
Memory debugger β Software memory problem finder
Minifier β Removal of unnecessary characters in code without changing its functionality
Pretty-printer β Formatting to make code or markup easier to readPages displaying short descriptions of redirect targets
Performance profiler β Measuring the time or resources used by a section of a computer program
Static code analyzer β Analysis of computer programs without executing themPages displaying short descriptions of redirect targets
Source code editor β Text editor specializing in software codePages displaying short descriptions of redirect targets
Source code generation β Type of computer programmingPages displaying short descriptions of redirect targets
Version control system β Stores and tracks versions of files
== See also ==
Call graph β Structure in computing
Comparison of integrated development environments β Notable software packages that are nominal IDE
Computer aided software engineering β Domain of software toolsPages displaying short descriptions of redirect targets
Git β Distributed version control software system
GitHub β Software development collaboration platform
Lint β Tool to flag poor computer code
List of software engineering topics β Overview of and topical guide to software engineeringPages displaying short descriptions of redirect targets
List of unit testing frameworks
Manual memory management β Computer memory management methodology
Memory leak β When a computer program fails to release unnecessary memory
Reverse-engineering β Process of extracting design information from anything artificialPages displaying short descriptions of redirect targets
Revision Control System β Version-control system
Software development kit β Set of software development tools
Software engineering β Engineering approach to software development
SourceForge β Software discovery and hosting platform for B2B and open source software
SWIG β O
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https://en.wikipedia.org/wiki/Programming_tool
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