Editing Programming language

{{Short description|Language for communicating instructions to a machine}}
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{{Use dmy dates|date=September 2020}}

[[File:C Hello World Program.png|thumb|right|upright=1.3|The [[source code]] for a computer program in [[C (programming language)|C]]. The gray lines are [[comment (computer programming)|comments]] that explain the program to humans. When [[compiled]] and [[Execution (computing)|run]], it will give the output "[["Hello, World!" program|Hello, world!]]".]]
A '''programming language''' is a system of notation for writing [[computer program]]s.<ref name="Aaby 2004">{{cite book |last=Aaby |first=Anthony |url=http://www.emu.edu.tr/aelci/Courses/D-318/D-318-Files/plbook/intro.htm |title=Introduction to Programming Languages |year=2004 |access-date=29 September 2012 |archive-url=https://web.archive.org/web/20121108043216/http://www.emu.edu.tr/aelci/Courses/D-318/D-318-Files/plbook/intro.htm |archive-date=8 November 2012 |url-status=dead}}</ref>
Programming languages are described in terms of their [[Syntax (programming languages)|syntax]] (form) and [[semantics (computer science)|semantics]] (meaning), usually defined by a [[formal language]]. Languages usually provide features such as a [[type system]], [[Variable (computer science)|variables]], and mechanisms for [[Exception handling (programming)|error handling]]. An [[Programming language implementation|implementation]] of a programming language is required in order to [[Execution (computing)|execute]] programs, namely an [[Interpreter (computing)|interpreter]] or a [[compiler]]. An interpreter directly executes the source code, while a [[compiler]] produces an [[executable]] program.

[[Computer architecture]] has strongly influenced the design of programming languages, with the most common type ([[imperative languages]]—which implement operations in a specified order) developed to perform well on the popular [[von Neumann architecture]]. While early programming languages were closely tied to the [[Computer hardware|hardware]], over time they have developed more [[abstraction (computer science)|abstraction]] to hide implementation details for greater simplicity.

Thousands of programming languages—often classified as imperative, [[functional programming|functional]], [[logic programming|logic]], or [[object-oriented programming|object-oriented]]—have been developed for a wide variety of uses. Many aspects of programming language design involve tradeoffs—for example, [[exception handling]] simplifies error handling, but at a performance cost. [[Programming language theory]] is the subfield of [[computer science]] that studies the design, implementation, analysis, characterization, and classification of programming languages.

==Definitions==
Programming languages differ from [[natural language]]s in that natural languages are used for interaction between people, while programming languages are designed to allow humans to communicate instructions to machines.{{Citation needed|date=October 2024}}

The term ''[[computer language]]'' is sometimes used interchangeably with "programming language".<ref>Robert A. Edmunds, The Prentice-Hall standard glossary of computer terminology, Prentice-Hall, 1985, p. 91</ref> However, usage of these terms varies among authors.

In one usage, programming languages are described as a subset of computer languages.<ref>Pascal Lando, Anne Lapujade, Gilles Kassel, and Frédéric Fürst, ''[http://home.mis.u-picardie.fr/~site-ic/site/IMG/pdf/ICSOFT2007_final.pdf Towards a General Ontology of Computer Programs]'' {{webarchive|url=https://web.archive.org/web/20150707093557/http://home.mis.u-picardie.fr/~site-ic/site/IMG/pdf/ICSOFT2007_final.pdf|date=7 July 2015}}, [http://dblp.uni-trier.de/db/conf/icsoft/icsoft2007-1.html ICSOFT 2007] {{webarchive|url=https://web.archive.org/web/20100427063709/http://dblp.uni-trier.de/db/conf/icsoft/icsoft2007-1.html|date=27 April 2010}}, pp. 163–170</ref> 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.{{Citation needed|date=October 2024}} Most practical programming languages are Turing complete,<ref name=":0">{{Cite web |title=Turing Completeness |url=https://www.cs.odu.edu/~zeil/cs390/latest/Public/turing-complete/index.html |url-status=live |archive-url=https://web.archive.org/web/20220816145137/https://cs.odu.edu/~zeil/cs390/latest/Public/turing-complete/index.html |archive-date=16 August 2022 |access-date=2022-10-05 |website=www.cs.odu.edu}}</ref> and as such are equivalent in what programs they can compute.

Another usage regards programming languages as theoretical constructs for programming [[abstract machine]]s and computer languages as the subset thereof that runs on physical computers, which have finite hardware resources.<ref>R. Narasimhan, Programming Languages and Computers: A Unified Metatheory, pp. 189—247 in Franz Alt, Morris Rubinoff (eds.) Advances in computers, Volume 8, Academic Press, 1994, {{ISBN|0-12-012108-5}}, p.215: "[...] the model [...] for computer languages differs from that [...] for programming languages in only two respects. In a computer language, there are only finitely many names—or registers—which can assume only finitely many values—or states—and these states are not further distinguished in terms of any other attributes. [author's footnote:] This may sound like a truism but its implications are far-reaching. For example, it would imply that any model for programming languages, by fixing certain of its parameters or features, should be reducible in a natural way to a model for computer languages."</ref> [[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.<ref>John C. Reynolds, "Some thoughts on teaching programming and programming languages", ''[[SIGPLAN]] Notices'', Volume 43, Issue 11, November 2008, p.109</ref>

==History==
{{Main|History of programming languages}}
===Early developments===
The first programmable computers were invented at the end of the 1940s, and with them, the first programming languages.{{sfn|Gabbrielli|Martini|2023|p=519}} The earliest computers were programmed in [[first-generation programming language]]s (1GLs), [[machine language]] (simple instructions that could be directly executed by the processor). This code was very difficult to debug and was not [[portability (computing)|portable]] between different computer systems.{{sfn|Gabbrielli|Martini|2023|pp=520–521}} In order to improve the ease of programming, [[assembly languages]] (or [[second-generation programming language]]s—2GLs) were invented, diverging from the machine language to make programs easier to understand for humans, although they did not increase portability.{{sfn|Gabbrielli|Martini|2023|p=521}}

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.{{sfn|Gabbrielli|Martini|2023|p=522}} The introduction of [[high-level programming language]]s ([[third-generation programming language]]s—3GLs)—revolutionized programming. These languages [[abstraction (computing)|abstracted]] away the details of the hardware, instead being designed to express algorithms that could be understood more easily by humans. For example, arithmetic expressions could now be written in symbolic notation and later translated into machine code that the hardware could execute.{{sfn|Gabbrielli|Martini|2023|p=521}} In 1957, [[Fortran]] (FORmula TRANslation) was invented. Often considered the first [[compiler|compiled]] high-level programming language,{{sfn|Gabbrielli|Martini|2023|p=521}}{{sfn|Sebesta|2012|p=42}} Fortran has remained in use into the twenty-first century.{{sfn|Gabbrielli|Martini|2023|p=524}}

===1960s and 1970s===
[[File:IBM Electronic Data Processing Machine - GPN-2000-001881.jpg|thumb|Two people using an [[IBM 704]] [[mainframe]]—the first hardware to support [[floating-point arithmetic]]—in 1957. [[Fortran]] was designed for this machine.{{sfn|Sebesta|2012|pp=42–44}}{{sfn|Gabbrielli|Martini|2023|p=524}}]]
Around 1960, the first [[mainframes]]—general purpose computers—were developed, although they could only be operated by professionals and the cost was extreme. The data and instructions were input by [[punch cards]], meaning that no input could be added while the program was running. The languages developed at this time therefore are designed for minimal interaction.{{sfn|Gabbrielli|Martini|2023|pp=523–524}} After the invention of the [[microprocessor]], computers in the 1970s became dramatically cheaper.{{sfn|Gabbrielli|Martini|2023|p=527}} New computers also allowed more user interaction, which was supported by newer programming languages.{{sfn|Gabbrielli|Martini|2023|p=528}}

[[Lisp (programming language)|Lisp]], implemented in 1958, was the first [[functional programming]] language.<ref>{{Cite web|url=https://twobithistory.org/2018/10/14/lisp.html|title=How Lisp Became God's Own Programming Language|website=twobithistory.org|access-date=10 April 2024|archive-date=10 April 2024|archive-url=https://web.archive.org/web/20240410060444/https://twobithistory.org/2018/10/14/lisp.html|url-status=live}}</ref> Unlike Fortran, it supported [[recursion]] and [[conditional expression]]s,{{sfn|Sebesta|2012|pp=47–48}} and it also introduced [[dynamic memory management]] on a [[heap (computer science)|heap]] and automatic [[Garbage collection (computer science)|garbage collection]].{{sfn|Gabbrielli|Martini|2023|p=526}} For the next decades, Lisp dominated [[artificial intelligence]] applications.{{sfn|Sebesta|2012|p=50}} In 1978, another functional language, [[ML (programming language)|ML]], introduced [[type inference|inferred types]] and polymorphic [[Parameter (computer programming)|parameter]]s.{{sfn|Gabbrielli|Martini|2023|p=528}}{{sfn|Sebesta|2012|pp=701–703}}

After [[ALGOL]] (ALGOrithmic Language) was released in 1958 and 1960,{{sfn|Gabbrielli|Martini|2023|pp=524–525}} it became the standard in computing literature for describing [[algorithm]]s. Although its commercial success was limited, most popular imperative languages—including [[C (programming language)|C]], [[Pascal (programming language)|Pascal]], [[Ada (programming language)|Ada]], [[C++]], [[Java (programming language)|Java]], and [[C Sharp (programming language)|C#]]—are directly or indirectly descended from ALGOL 60.{{sfn|Sebesta|2012|pp=56–57}}{{sfn|Gabbrielli|Martini|2023|p=524}} Among its innovations adopted by later programming languages included greater portability and the first use of [[context-free grammar|context-free]], [[Backus–Naur form|BNF]] grammar.{{sfn|Gabbrielli|Martini|2023|p=525}} [[Simula]], the first language to support [[object-oriented programming]] (including [[subtypes]], [[dynamic dispatch]], and [[inheritance (computer science)|inheritance]]), also descends from ALGOL and achieved commercial success.{{sfn|Gabbrielli|Martini|2023|pp=526–527}} C, another ALGOL descendant, has sustained popularity into the twenty-first century. C allows access to lower-level machine operations more than other contemporary languages. Its power and efficiency, generated in part with flexible [[Pointer (computer programming)|pointer]] operations, comes at the cost of making it more difficult to write correct code.{{sfn|Gabbrielli|Martini|2023|p=528}}

[[Prolog]], designed in 1972, was the first [[logic programming]] language, communicating with a computer using formal logic notation.{{sfn|Gabbrielli|Martini|2023|p=531}}{{sfn|Sebesta|2012|p=79}} With logic programming, the programmer specifies a desired result and allows the [[interpreter (computer science)|interpreter]] to decide how to achieve it.{{sfn|Gabbrielli|Martini|2023|p=530}}{{sfn|Sebesta|2012|p=79}}

===1980s to 2000s===
[[File:Bangalore India Tech books for sale IMG 5261.jpg|thumb|right|A small selection of programming language textbooks]]

During the 1980s, the invention of the [[personal computer]] transformed the roles for which programming languages were used.{{sfn|Gabbrielli|Martini|2023|pp=532–533}} New languages introduced in the 1980s included C++, a [[superset]] of C that can compile C programs but also supports [[Class (computer programming)|classes]] and [[Inheritance (object-oriented programming)|inheritance]].{{sfn|Gabbrielli|Martini|2023|p=534}} [[Ada (programming language)|Ada]] and other new languages introduced support for [[Concurrency (computer science)|concurrency]].{{sfn|Gabbrielli|Martini|2023|pp=534–535}} The Japanese government invested heavily into the so-called [[Fifth-generation programming language|fifth-generation languages]] that added support for concurrency to logic programming constructs, but these languages were outperformed by other concurrency-supporting languages.{{sfn|Gabbrielli|Martini|2023|p=535}}{{sfn|Sebesta|2012|p=736}}

Due to the rapid growth of the [[Internet]] and the [[World Wide Web]] in the 1990s, new programming languages were introduced to support [[Web pages]] and [[Computer network |networking]].{{sfn|Gabbrielli|Martini|2023|p=536}} [[Java (programming language)|Java]], based on C++ and designed for increased portability across systems and security, enjoyed large-scale success because these features are essential for many Internet applications.{{sfn|Gabbrielli|Martini|2023|pp=536–537}}{{sfn|Sebesta|2012|pp=91–92}} Another development was that of [[type system|dynamically typed]] [[scripting languages]]—[[Python (programming language)|Python]], [[JavaScript]], [[PHP]], and [[Ruby (programming language)|Ruby]]—designed to quickly produce small programs that coordinate existing [[Application software|application]]s. Due to their integration with [[HTML]], they have also been used for building web pages hosted on [[Server (computing)|server]]s.{{sfn|Gabbrielli|Martini|2023|pp=538–539}}{{sfn|Sebesta|2012|pp=97–99}}

===2000s to present===

During the 2000s, there was a slowdown in the development of new programming languages that achieved widespread popularity.{{sfn|Gabbrielli|Martini|2023|p=542}} One innovation was [[service-oriented programming]], designed to exploit [[distributed systems]] whose components are connected by a network. Services are similar to objects in object-oriented programming, but run on a separate process.{{sfn|Gabbrielli|Martini|2023|pp=474–475, 477, 542}} [[C Sharp (programming language)|C#]] and [[F Sharp (programming language)|F#]] cross-pollinated ideas between imperative and functional programming.{{sfn|Gabbrielli|Martini|2023|pp=542–543}} After 2010, several new languages—[[Rust (programming language)|Rust]], [[Go (programming language)|Go]], [[Swift (programming language)|Swift]], [[Zig (programming language)|Zig]] and [[Carbon (programming language)|Carbon]] —competed for the performance-critical software for which C had historically been used.{{sfn|Gabbrielli|Martini|2023|p=544}} Most of the new programming languages use [[Type system|static typing]] while a few numbers of new languages use [[Type system|dynamic typing]] like [[Ring (programming language)|Ring]] and [[Julia_(programming_language)|Julia]].<ref>{{cite arXiv | eprint=1209.5145 | last1=Bezanson | first1=Jeff | last2=Karpinski | first2=Stefan | last3=Shah | first3=Viral B. | last4=Edelman | first4=Alan | title=Julia: A Fast Dynamic Language for Technical Computing | date=2012 | class=cs.PL }}</ref><ref>Ayouni, M. and Ayouni, M., 2020. Data Types in Ring. Beginning Ring Programming: From Novice to Professional, pp.51-98.</ref>

Some of the new programming languages are classified as [[visual programming languages]] like [[Scratch_(programming_language)|Scratch]], [[LabVIEW]] and [[PWCT]]. Also, some of these languages mix between textual and visual programming usage like [[Ballerina (programming language)|Ballerina]].<ref>Sáez-López, J.M., Román-González, M. and Vázquez-Cano, E., 2016. Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, pp.129-141.</ref><ref>Fayed, M.S., Al-Qurishi, M., Alamri, A. and Al-Daraiseh, A.A., 2017, March. PWCT: visual language for IoT and cloud computing applications and systems. In Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing (pp. 1-5).</ref><ref>Kodosky, J., 2020. LabVIEW. Proceedings of the ACM on Programming Languages, 4(HOPL), pp.1-54.</ref><ref>Fernando, A. and Warusawithana, L., 2020. Beginning Ballerina Programming: From Novice to Professional. Apress.</ref> Also, this trend lead to developing projects that help in developing new VPLs like [[Blockly]] by [[Google]].<ref>Baluprithviraj, K.N., Bharathi, K.R., Chendhuran, S. and Lokeshwaran, P., 2021, March. Artificial intelligence based smart door with face mask detection. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (pp. 543-548). IEEE.</ref> Many game engines like [[Unreal Engine|Unreal]] and [[Unity (game engine)|Unity]] added support for visual scripting too.<ref>Sewell, B., 2015. Blueprints visual scripting for unreal engine. Packt Publishing Ltd.</ref><ref>Bertolini, L., 2018. Hands-On Game Development without Coding: Create 2D and 3D games with Visual Scripting in Unity. Packt Publishing Ltd.</ref>

==Elements==
Every programming language includes fundamental elements for describing data and the operations or transformations applied to them, such as adding two numbers or selecting an item from a collection. These elements are governed by syntactic and semantic rules that define their structure and meaning, respectively.

===Syntax===
{{Main|Syntax (programming languages)}}
[[File:Python add5 parse.png|thumb|367px|[[Parse tree]] of [[Python (programming language)|Python code]] with inset tokenization]]
[[File:Python add5 syntax.svg|thumb|292px|[[Syntax highlighting]] is often used to aid programmers in recognizing elements of source code. The language above is [[Python (programming language)|Python]].]]
A programming language's surface form is known as its [[syntax (programming languages)|syntax]]. Most programming languages are purely textual; they use sequences of text including words, numbers, and punctuation, much like written natural languages. On the other hand, some programming languages are [[visual programming language|graphical]], using visual relationships between symbols to specify a program.

The syntax of a language describes the possible combinations of symbols that form a syntactically correct program. The meaning given to a combination of symbols is handled by semantics (either [[Formal semantics of programming languages|formal]] or hard-coded in a [[Reference implementation (computing)|reference implementation]]). Since most languages are textual, this article discusses textual syntax.

The programming language syntax is usually defined using a combination of [[regular expression]]s (for [[lexical analysis|lexical]] structure) and [[Backus–Naur form]] (for [[context-free grammar|grammatical]] structure). Below is a simple grammar, based on [[Lisp (programming language)|Lisp]]:
<syntaxhighlight lang="bnf">
expression ::= atom | list
atom       ::= number | symbol
number     ::= [+-]?['0'-'9']+
symbol     ::= ['A'-'Z''a'-'z'].*
list       ::= '(' expression* ')'
</syntaxhighlight>

This grammar specifies the following:
* an ''expression'' is either an ''atom'' or a ''list'';
* an ''atom'' is either a ''number'' or a ''symbol'';
* a ''number'' is an unbroken sequence of one or more decimal digits, optionally preceded by a plus or minus sign;
* a ''symbol'' is a letter followed by zero or more of any alphabetical characters (excluding whitespace); and
* a ''list'' is a matched pair of parentheses, with zero or more ''expressions'' inside it.

The following are examples of well-formed token sequences in this grammar: <code>12345</code>, <code>()</code> and <code>(a b c232 (1))</code>.

Not all syntactically correct programs are semantically correct. Many syntactically correct programs are nonetheless ill-formed, per the language's rules; and may (depending on the language specification and the soundness of the implementation) result in an error on translation or execution. In some cases, such programs may exhibit [[undefined behavior]]. Even when a program is well-defined within a language, it may still have a meaning that is not intended by the person who wrote it.

Using [[natural language]] as an example, it may not be possible to assign a meaning to a grammatically correct sentence or the sentence may be false:
* "[[Colorless green ideas sleep furiously]]." is grammatically well-formed but has no generally accepted meaning.
* "John is a married bachelor." is grammatically [[well-formedness|well-formed]] but expresses a meaning that cannot be true.

The following [[C (programming language)|C language]] fragment is syntactically correct, but performs operations that are not semantically defined (the operation <code>*p >> 4</code> has no meaning for a value having a complex type and <code>p->im</code> is not defined because the value of <code>p</code> is the [[null pointer]]):

<syntaxhighlight lang="c">
complex *p = NULL;
complex abs_p = sqrt(*p >> 4 + p->im);
</syntaxhighlight>

If the [[type declaration]] on the first line were omitted, the program would trigger an error on the undefined variable <code>p</code> during compilation. However, the program would still be syntactically correct since type declarations provide only semantic information.

The grammar needed to specify a programming language can be classified by its position in the [[Chomsky hierarchy]]. The syntax of most programming languages can be specified using a Type-2 grammar, i.e., they are [[context-free grammar]]s.<ref>{{cite book|author=Michael Sipser|year=1996|title=Introduction to the Theory of Computation|publisher=PWS Publishing|isbn=978-0-534-94728-6 |author-link=Michael Sipser|title-link=Introduction to the Theory of Computation}} Section 2.2: Pushdown Automata, pp.101–114.</ref> Some languages, including Perl and Lisp, contain constructs that allow execution during the parsing phase. Languages that have constructs that allow the programmer to alter the behavior of the parser make syntax analysis an [[undecidable problem]], and generally blur the distinction between parsing and execution.<ref>Jeffrey Kegler, "[http://www.jeffreykegler.com/Home/perl-and-undecidability Perl and Undecidability] {{webarchive|url=https://web.archive.org/web/20090817183115/http://www.jeffreykegler.com/Home/perl-and-undecidability |date=17 August 2009 }}", ''The Perl Review''. Papers 2 and 3 prove, using respectively [[Rice's theorem]] and direct reduction to the [[halting problem]], that the parsing of Perl programs is in general undecidable.</ref> In contrast to [[Lisp macro|Lisp's macro system]] and Perl's <code>BEGIN</code> blocks, which may contain general computations, C macros are merely string replacements and do not require code execution.<ref>Marty Hall, 1995, [http://www.apl.jhu.edu/~hall/Lisp-Notes/Macros.html Lecture Notes: Macros] {{webarchive|url=https://web.archive.org/web/20130806054148/http://www.apl.jhu.edu/~hall/Lisp-Notes/Macros.html |date=6 August 2013 }}, [[PostScript]] [http://www.apl.jhu.edu/~hall/Lisp-Notes/Macros.ps version] {{webarchive|url=https://web.archive.org/web/20000817211709/http://www.apl.jhu.edu/~hall/Lisp-Notes/Macros.ps |date=17 August 2000 }}</ref>

===Semantics===
{{Logical connectives sidebar}}
The term [[Semantics#Computer science|''semantics'']] refers to the meaning of languages, as opposed to their form ([[#Syntax|syntax]]).

====Static semantics====
Static semantics defines restrictions on the structure of valid texts that are hard or impossible to express in standard syntactic formalisms.<ref name="Aaby 2004"/>{{Failed verification|date=January 2023|reason=This site says nothing about "static semantics" or any connection between semantics and "structure" or "restrictions".}} For compiled languages, static semantics essentially include those semantic rules that can be checked at compile time. Examples include checking that every [[identifier]] is declared before it is used (in languages that require such declarations) or that the labels on the arms of a [[case statement]] are distinct.<ref>Michael Lee Scott, ''Programming language pragmatics'', Edition 2, Morgan Kaufmann, 2006, {{ISBN|0-12-633951-1}}, p. 18–19</ref> Many important restrictions of this type, like checking that identifiers are used in the appropriate context (e.g. not adding an integer to a function name), or that [[subroutine]] calls have the appropriate number and type of arguments, can be enforced by defining them as rules in a [[logic]] called a [[type system]]. Other forms of [[static code analysis|static analyses]] like [[data flow analysis]] may also be part of static semantics. Programming languages such as [[Java (programming language)|Java]] and [[C Sharp (programming language)|C#]] have [[definite assignment analysis]], a form of data flow analysis, as part of their respective static semantics.<ref name=":1">{{Cite book |last=Winskel |first=Glynn |url=https://books.google.com/books?id=JzUNn6uUxm0C |title=The Formal Semantics of Programming Languages: An Introduction |date=5 February 1993 |publisher=MIT Press |isbn=978-0-262-73103-4 |language=en}}</ref>

====Dynamic semantics====
{{Main|Semantics of programming languages}}
{{unreferenced|section|date=April 2024}}
Once data has been specified, the machine must be instructed to perform operations on the data. For example, the semantics may define the [[evaluation strategy|strategy]] by which expressions are evaluated to values, or the manner in which [[control flow|control structures]] conditionally execute [[Statement (computer science)|statements]]. The ''dynamic semantics'' (also known as ''execution semantics'') of a language defines how and when the various constructs of a language should produce a program behavior. There are many ways of defining execution semantics. Natural language is often used to specify the execution semantics of languages commonly used in practice. A significant amount of academic research goes into [[formal semantics of programming languages]], which allows execution semantics to be specified in a formal manner. Results from this field of research have seen limited application to programming language design and implementation outside academia.<ref name=":1" />

===Type system===
{{Main|Data type|Type system|Type safety}}

A [[data type]] is a set of allowable values and operations that can be performed on these values.{{sfn|Sebesta|2012|p=244}} Each programming language's [[type system]] defines which data types exist, the type of an [[Expression (mathematics)|expression]], and how [[type equivalence]] and [[type compatibility]] function in the language.{{sfn|Sebesta|2012|p=245}}

According to [[type theory]], a language is fully typed if the specification of every operation defines types of data to which the operation is applicable.<ref name="typing">{{cite web|url=http://www.acooke.org/comp-lang.html|author=Andrew Cooke|title=Introduction To Computer Languages|access-date=13 July 2012|url-status=live|archive-url=https://web.archive.org/web/20120815140215/http://www.acooke.org/comp-lang.html|archive-date=15 August 2012}}</ref> In contrast, an untyped language, such as most [[assembly language]]s, allows any operation to be performed on any data, generally sequences of bits of various lengths.<ref name="typing"/> In practice, while few languages are fully typed, most offer a degree of typing.<ref name="typing"/> 

Because different types (such as [[integer]]s and [[floating point|floats]]) represent values differently, unexpected results will occur if one type is used when another is expected. [[Type checking]] will flag this error, usually at [[compile time]] (runtime type checking is more costly).{{sfn|Sebesta|2012|pp=15, 408–409}} With [[Strongly-typed programming language|strong typing]], [[type error]]s can always be detected unless variables are explicitly [[type conversion|cast]] to a different type. [[Weak typing]] occurs when languages allow implicit casting—for example, to enable operations between variables of different types without the programmer making an explicit type conversion. The more cases in which this [[type coercion]] is allowed, the fewer type errors can be detected.{{sfn|Sebesta|2012|pp=303–304}}  
====Commonly supported types====
{{See also|Primitive data type}}
Early programming languages often supported only built-in, numeric types such as the [[integer]] (signed and unsigned) and [[floating point]] (to support operations on [[real number]]s that are not integers). Most programming languages support multiple sizes of floats (often called [[Single-precision floating-point format|float]] and [[Double-precision floating-point format|double]]) and integers depending on the size and precision required by the programmer. Storing an integer in a type that is too small to represent it leads to [[integer overflow]]. The most common way of representing negative numbers with signed types is [[twos complement]], although [[ones complement]] is also used.{{sfn|Sebesta|2012|pp=246–247}} Other common types include [[Boolean data type|Boolean]]—which is either true or false—and [[Character (computing) |character]]—traditionally one [[byte]], sufficient to represent all [[ASCII]] characters.{{sfn|Sebesta|2012|p=249}} 

[[array (data type)|Arrays]] are a data type whose elements, in many languages, must consist of a single type of fixed length. Other languages define arrays as references to data stored elsewhere and support elements of varying types.{{sfn|Sebesta|2012|p=260}} Depending on the programming language, sequences of multiple characters, called [[string (computing)|strings]], may be supported as arrays of characters or their own [[primitive type]].{{sfn|Sebesta|2012|p=250}} Strings may be of fixed or variable length, which enables greater flexibility at the cost of increased storage space and more complexity.{{sfn|Sebesta|2012|p=254}} Other data types that may be supported include [[list (computing)|lists]],{{sfn|Sebesta|2012|pp=281–282}} [[associative arrays|associative (unordered) arrays]] accessed via keys,{{sfn|Sebesta|2012|pp=272–273}} [[record (computer science)|record]]s in which data is mapped to names in an ordered structure,{{sfn|Sebesta|2012|pp=276–277}} and [[tuple]]s—similar to records but without names for data fields.{{sfn|Sebesta|2012|p=280}} [[Pointer (computer programming)|Pointer]]s store memory addresses, typically referencing locations on the [[Heap (programming)|heap]] where other data is stored.{{sfn|Sebesta|2012|pp=289–290}}

The simplest [[user-defined type]] is an [[Ordinal data type|ordinal type]], often called an [[enumeration]], whose values can be mapped onto the set of positive integers.{{sfn|Sebesta|2012|p=255}} Since the mid-1980s, most programming languages also support [[abstract data types]], in which the representation of the data and operations are [[information hiding|hidden from the user]], who can only access an [[Interface (computing)|interface]].{{sfn|Sebesta|2012|pp=244–245}} The benefits of [[data abstraction]] can include increased reliability, reduced complexity, less potential for [[name collision]], and allowing the underlying [[data structure]] to be changed without the client needing to alter its code.{{sfn|Sebesta|2012|p=477}}

====Static and dynamic typing====
In [[static typing]], all expressions have their types determined before a program executes, typically at compile-time.<ref name="typing"/> Most widely used, statically typed programming languages require the types of variables to be specified explicitly. In some languages, types are implicit; one form of this is when the compiler can [[type inference|infer]] types based on context. The downside of [[implicit typing]] is the potential for errors to go undetected.{{sfn|Sebesta|2012|p=211}} Complete type inference has traditionally been associated with functional languages such as [[Haskell]] and [[ML (programming language)|ML]].<ref>{{Cite conference |last=Leivant |first=Daniel |date=1983 |title=Polymorphic type inference |conference=ACM SIGACT-SIGPLAN symposium on Principles of programming languages |language=en |location=Austin, Texas |publisher=ACM Press |pages=88–98 |doi=10.1145/567067.567077 |isbn=978-0-89791-090-3|doi-access=free }}</ref> 

With dynamic typing, the type is not attached to the variable but only the value encoded in it. A single variable can be reused for a value of a different type. Although this provides more flexibility to the programmer, it is at the cost of lower reliability and less ability for the programming language to check for errors.{{sfn|Sebesta|2012|pp=212–213}} Some languages allow variables of a [[union type]] to which any type of value can be assigned, in an exception to their usual static typing rules.{{sfn|Sebesta|2012|pp=284–285}}

===Concurrency===
{{see also|Concurrent computing}}
In computing, multiple instructions can be executed simultaneously. Many programming languages support instruction-level and subprogram-level concurrency.{{sfn|Sebesta|2012|p=576}} By the twenty-first century, additional processing power on computers was increasingly coming from the use of additional processors, which requires programmers to design software that makes use of multiple processors simultaneously to achieve improved performance.{{sfn|Sebesta|2012|p=579}} [[Interpreted language]]s such as [[Python (programming language)|Python]] and [[Ruby (programming language)|Ruby]] do not support the concurrent use of multiple processors.{{sfn|Sebesta|2012|p=585}} Other programming languages do support managing data shared between different threads by controlling the order of execution of key instructions via the use of [[Semaphore (programming)|semaphore]]s, controlling access to shared data via [[monitor (synchronization)|monitor]], or enabling [[message passing]] between threads.{{sfn|Sebesta|2012|pp=585–586}}
===Exception handling===
{{main|Exception handling}}
Many programming languages include exception handlers, a section of code triggered by [[runtime error]]s that can deal with them in two main ways:{{sfn|Sebesta|2012|pp=630, 634}}
*Termination: shutting down and handing over control to the [[operating system]]. This option is considered the simplest.
*Resumption: resuming the program near where the exception occurred. This can trigger a repeat of the exception, unless the exception handler is able to modify values to prevent the exception from reoccurring.
Some programming languages support dedicating a block of code to run regardless of whether an exception occurs before the code is reached; this is called finalization.{{sfn|Sebesta|2012|p=635}}

There is a tradeoff between increased ability to handle exceptions and reduced performance.{{sfn|Sebesta|2012|p=631}} For example, even though array index errors are common{{sfn|Sebesta|2012|p=261}} C does not check them for performance reasons.{{sfn|Sebesta|2012|p=631}} Although programmers can write code to catch user-defined exceptions, this can clutter a program. Standard libraries in some languages, such as C, use their return values to indicate an exception.{{sfn|Sebesta|2012|p=632}} Some languages and their compilers have the option of turning on and off error handling capability, either temporarily or permanently.{{sfn|Sebesta|2012|pp=631, 635–636}}

==Design and implementation==
{{Main|Programming language design and implementation}}
One of the most important influences on programming language design has been [[computer architecture]]. [[Imperative languages]], the most commonly used type, were designed to perform well on [[von Neumann architecture]], the most common computer architecture.{{sfn|Sebesta|2012|p=18}} In von Neumann architecture, the [[Computer memory|memory]] stores both data and instructions, while the [[CPU]] that performs instructions on data is separate, and data must be piped back and forth to the CPU. The central elements in these languages are variables, [[Assignment (computer science)|assignment]], and [[iteration]], which is more efficient than [[Recursion (computer science)|recursion]] on these machines.{{sfn|Sebesta|2012|p=19}}  

Many programming languages have been designed from scratch, altered to meet new needs, and combined with other languages. Many have eventually fallen into disuse.{{cn|date=August 2024}} The birth of programming languages in the 1950s was stimulated by the desire to make a universal programming language suitable for all machines and uses, avoiding the need to write code for different computers.{{sfn|Nofre|Priestley|Alberts|2014|p=55}} By the early 1960s, the idea of a universal language was rejected due to the differing requirements of the variety of purposes for which code was written.{{sfn|Nofre|Priestley|Alberts|2014|p=60}} 

===Tradeoffs===
Desirable qualities of programming languages include readability, writability, and reliability.{{sfn|Sebesta|2012|p=8}} These features can reduce the cost of training programmers in a language, the amount of time needed to write and maintain programs in the language, the cost of compiling the code, and increase runtime performance.{{sfn|Sebesta|2012|pp=16–17}} 
*Although early programming languages often prioritized efficiency over readability, the latter has grown in importance since the 1970s. Having multiple operations to achieve the same result can be detrimental to readability, as is [[operator overload|overloading operators]], so that the same operator can have multiple meanings.{{sfn|Sebesta|2012|pp=8–9}} Another feature important to readability is [[orthogonality]], limiting the number of constructs that a programmer has to learn.{{sfn|Sebesta|2012|pp=9–10}} A syntax structure that is easily understood and [[reserved word|special word]]s that are immediately obvious also supports readability.{{sfn|Sebesta|2012|pp=12–13}}
*Writability is the ease of use for writing code to solve the desired problem. Along with the same features essential for readability,{{sfn|Sebesta|2012|p=13}} [[abstraction (computer science)|abstraction]]—interfaces that enable hiding details from the client—and [[Expressive power (computer science)|expressivity]]—enabling more concise programs—additionally help the programmer write code.{{sfn|Sebesta|2012|pp=14–15}} The earliest programming languages were tied very closely to the underlying hardware of the computer, but over time support for abstraction has increased, allowing programmers express ideas that are more remote from simple translation into underlying hardware instructions. Because programmers are less tied to the complexity of the computer, their programs can do more computing with less effort from the programmer.<ref>Frederick P. Brooks, Jr.: ''The Mythical Man-Month'', Addison-Wesley, 1982, pp. 93–94</ref> Most programming languages come with a [[standard library]] of commonly used functions.<ref>{{cite journal |last1=Busbee |first1=Kenneth Leroy |last2=Braunschweig |first2=Dave |title=Standard Libraries |url=https://press.rebus.community/programmingfundamentals/chapter/standard-libraries/ |website=Programming Fundamentals – A Modular Structured Approach |access-date=27 January 2024 |language=en |date=15 December 2018}}</ref>
*Reliability means that a program performs as specified in a wide range of circumstances.{{sfn|Sebesta|2012|p=15}} [[Type checking]], [[exception handling]], and restricted [[aliasing (computing)|aliasing]] (multiple variable names accessing the same region of memory) all can improve a program's reliability.{{sfn|Sebesta|2012|pp=8, 16}}
Programming language design often involves tradeoffs.{{sfn|Sebesta|2012|pp=18, 23}} For example, features to improve reliability typically come at the cost of performance.{{sfn|Sebesta|2012|p=23}} Increased expressivity due to a large number of operators makes writing code easier but comes at the cost of readability.{{sfn|Sebesta|2012|p=23}}

{{anchor|English-like programming languages}}
[[Natural-language programming]] has been proposed as a way to eliminate the need for a specialized language for programming. However, this goal remains distant and its benefits are open to debate. [[Edsger W. Dijkstra]] took the position that the use of a formal language is essential to prevent the introduction of meaningless constructs.<ref>Dijkstra, Edsger W. [http://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/EWD667.html On the foolishness of "natural language programming."] {{webarchive|url=https://web.archive.org/web/20080120201526/http://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/EWD667.html |date=20 January 2008 }} EWD667.</ref> [[Alan Perlis]] was similarly dismissive of the idea.<ref>{{cite web|last=Perlis|first=Alan|url=http://www-pu.informatik.uni-tuebingen.de/users/klaeren/epigrams.html|title=Epigrams on Programming|work=SIGPLAN Notices Vol. 17, No. 9|date=September 1982|pages=7–13|url-status=live|archive-url=https://web.archive.org/web/19990117034445/http://www-pu.informatik.uni-tuebingen.de/users/klaeren/epigrams.html|archive-date=17 January 1999}}</ref> 
===Specification===
{{Main|Programming language specification}}

The specification of a programming language is an artifact that the language [[programmer|users]] and the [[programming language implementation|implementors]] can use to agree upon whether a piece of [[source code]] is a valid [[computer program|program]] in that language, and if so what its behavior shall be.

A programming language specification can take several forms, including the following:
* An explicit definition of the syntax, static semantics, and execution semantics of the language. While syntax is commonly specified using a formal grammar, semantic definitions may be written in [[natural language]] (e.g., as in the [[C (programming language)|C language]]), or a [[formal semantics of programming languages|formal semantics]] (e.g., as in [[Standard ML]]<ref>{{cite book|last=Milner|first=R.|author-link=Robin Milner |author2=[[Mads Tofte|M. Tofte]] |author3=[[Robert Harper (computer scientist)|R. Harper]] |author4=D. MacQueen |title=The Definition of Standard ML (Revised)|publisher=MIT Press|year=1997|isbn=978-0-262-63181-5}}</ref> and [[Scheme (programming language)|Scheme]]<ref>{{cite web|first=Richard|last=Kelsey|author2=William Clinger|author3=Jonathan Rees|title=Section 7.2 Formal semantics|work=Revised<sup>5</sup> Report on the Algorithmic Language Scheme|url=http://www.schemers.org/Documents/Standards/R5RS/HTML/r5rs-Z-H-10.html#%_sec_7.2|date=February 1998|url-status=live|archive-url=https://web.archive.org/web/20060706081110/http://www.schemers.org/Documents/Standards/R5RS/HTML/r5rs-Z-H-10.html#%_sec_7.2|archive-date=6 July 2006}}</ref> specifications).
* A description of the behavior of a [[compiler|translator]] for the language (e.g., the [[C++]] and [[Fortran]] specifications). The syntax and semantics of the language have to be inferred from this description, which may be written in natural or formal language.
* A [[reference implementation|''reference'' or ''model'' implementation]], sometimes [[Meta-circular evaluator|written in the language being specified]] (e.g., [[Prolog]] or [[REXX|ANSI REXX]]<ref>[[American National Standards Institute|ANSI]] – Programming Language Rexx, X3-274.1996</ref>). The syntax and semantics of the language are explicit in the behavior of the reference implementation.

===Implementation===
{{Main|Programming language implementation}}

An implementation of a programming language is the conversion of a program into [[machine code]] that can be executed by the hardware. The machine code then can be executed with the help of the [[operating system]].{{sfn|Sebesta|2012|pp=23–24}} The most common form of interpretation in [[Software release life cycle|production code]] is by a [[compiler]], which translates the source code via an intermediate-level language into machine code, known as an [[executable]]. Once the program is compiled, it will run more quickly than with other implementation methods.{{sfn|Sebesta|2012|pp=25–27}} Some compilers are able to provide further [[optimization]] to reduce memory or computation usage when the executable runs, but increasing compilation time.{{sfn|Sebesta|2012|p=27}}

Another implementation method is to run the program with an [[interpreter (computing)|interpreter]], which translates each line of software into machine code just before it executes. Although it can make debugging easier, the downside of interpretation is that it runs 10 to 100 times slower than a compiled executable.{{sfn|Sebesta|2012|p=28}} Hybrid interpretation methods provide some of the benefits of compilation and some of the benefits of interpretation via partial compilation. One form this takes is [[just-in-time compilation]], in which the software is compiled ahead of time into an intermediate language, and then into machine code immediately before execution.{{sfn|Sebesta|2012|pp=29–30}}

==Proprietary languages==
Although most of the most commonly used programming languages have fully open specifications and implementations, many programming languages exist only as proprietary programming languages with the implementation available only from a single vendor, which may claim that such a proprietary language is their intellectual property. Proprietary programming languages are commonly [[domain-specific language]]s or internal [[scripting language]]s for a single product; some proprietary languages are used only internally within a vendor, while others are available to external users.{{Citation needed|date=June 2023}}

Some programming languages exist on the border between proprietary and open; for example, [[Oracle Corporation]] asserts proprietary rights to some aspects of the [[Java programming language]],<ref>See: [[Oracle America, Inc. v. Google, Inc.]]{{User-generated source|date=June 2023}}</ref> and [[Microsoft]]'s [[C Sharp (programming language)|C#]] programming language, which has open implementations of most parts of the system, also has [[Common Language Runtime]] (CLR) as a closed environment.<ref>{{Cite news|url=https://www.computerscience.org/resources/computer-programming-languages/|title=Guide to Programming Languages {{!}} ComputerScience.org|work=ComputerScience.org|access-date=2018-05-13|language=en-US|archive-date=13 May 2018|archive-url=https://web.archive.org/web/20180513223729/https://www.computerscience.org/resources/computer-programming-languages/|url-status=live}}</ref>

Many proprietary languages are widely used, in spite of their proprietary nature; examples include [[MATLAB]], [[VBScript]], and [[Wolfram Language]]. Some languages may make the transition from closed to open; for example, [[Erlang (programming language)|Erlang]] was originally Ericsson's internal programming language.<ref>{{Cite web|url=https://www.ibm.com/developerworks/library/os-erlang1/index.html|title=The basics|date=2011-05-10|website=ibm.com|language=en|access-date=2018-05-13|archive-date=14 May 2018|archive-url=https://web.archive.org/web/20180514064903/https://www.ibm.com/developerworks/library/os-erlang1/index.html|url-status=live}}</ref>

[[List of open-source programming languages|Open source programming languages]] are particularly helpful for [[open science]] applications, enhancing the capacity for [[Replication crisis|replication]] and code sharing.<ref>{{cite journal |last1=Abdelaziz |first1=Abdullah I. |last2=Hanson |first2=Kent A. |last3=Gaber |first3=Charles E. |last4=Lee |first4=Todd A. |date=2023 |title=Optimizing large real-world data analysis with parquet files in R: A step-by-step tutorial |journal=Pharmacoepidemiology and Drug Safety |volume=33 |issue=3 |pages=e5728 |doi=10.1002/pds.5728|doi-access=free |pmid=37984998 }}</ref>

==Use==
Thousands of different programming languages have been created, mainly in the computing field.<ref>{{cite web|access-date=1 June 2009|url=http://hopl.murdoch.edu.au/|title=HOPL: an interactive Roster of Programming Languages|publisher=[[Murdoch University]]|location=Australia|quote=This site lists 8512 languages.|url-status=dead|archive-url=https://web.archive.org/web/20110220044217/http://hopl.murdoch.edu.au/|archive-date=20 February 2011}}</ref>
Individual software projects commonly use five programming languages or more.<ref>{{cite conference|first1=Philip|conference=Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering – EASE '15|last1=Mayer|first2=Alexander|last2=Bauer|title=Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering |publisher=ACM|year=2015|location=New York, NY, US|isbn=978-1-4503-3350-4|pages=4:1–4:10|doi=10.1145/2745802.2745805|quote=Results: We found (a) a mean number of 5 languages per project with a clearly dominant main general-purpose language and 5 often-used DSL types, (b) a significant influence of the size, number of commits, and the main language on the number of languages as well as no significant influence of age and number of contributors, and (c) three language ecosystems grouped around XML, Shell/Make, and HTML/CSS. Conclusions: Multi-language programming seems to be common in open-source projects and is a factor that must be dealt with in tooling and when assessing the development and maintenance of such software systems.|chapter=An empirical analysis of the utilization of multiple programming languages in open source projects|doi-access=free}}</ref>

Programming languages differ from most other forms of human expression in that they require a greater degree of precision and completeness. When using a natural language to communicate with other people, human authors and speakers can be ambiguous and make small errors, and still expect their intent to be understood. However, figuratively speaking, computers "do exactly what they are told to do", and cannot "understand" what code the programmer intended to write. The combination of the language definition, a program, and the program's inputs must fully specify the external behavior that occurs when the program is executed, within the domain of control of that program. On the other hand, ideas about an algorithm can be communicated to humans without the precision required for execution by using [[pseudocode]], which interleaves natural language with code written in a programming language.

A programming language provides a structured mechanism for defining pieces of data, and the operations or transformations that may be carried out automatically on that data. A [[programmer]] uses the [[Abstraction (computer science)|abstractions]] present in the language to represent the concepts involved in a computation. These concepts are represented as a collection of the simplest elements available (called [[language primitive|primitives]]).<ref>{{cite web|url=http://mitpress.mit.edu/sicp/full-text/book/book-Z-H-10.html|title=Structure and Interpretation of Computer Programs|author=Abelson, Sussman, and Sussman|access-date=3 March 2009|url-status=dead|archive-url=https://web.archive.org/web/20090226050622/http://mitpress.mit.edu/sicp/full-text/book/book-Z-H-10.html|archive-date=26 February 2009}}</ref> ''[[Computer Programming|Programming]]'' is the process by which programmers combine these primitives to compose new programs, or adapt existing ones to new uses or a changing environment.

Programs for a computer might be [[Execution (computing)|executed]] in a [[Batch processing|batch process]] without any human interaction, or a user might type [[Command (computing)|commands]] in an [[Session (computer science)|interactive session]] of an [[Interpreter (computing)|interpreter]]. In this case the "commands" are simply programs, whose execution is chained together. When a language can run its commands through an interpreter (such as a [[Unix shell]] or other [[command-line interface]]), without compiling, it is called a [[scripting language]].<ref>{{cite web
 |url         = http://www.mactech.com/articles/mactech/Vol.15/15.09/ScriptingLanguages/index.html
 |title       = Scripting Languages
 |year        = 1999
 |first1= Brown
|last1=Vicki
|first2=Rich
|last2=Morin
 |website     = MacTech
 |url-status     = live
 |archive-url  = https://web.archive.org/web/20171202235828/http://www.mactech.com/articles/mactech/Vol.15/15.09/ScriptingLanguages/index.html
 |archive-date = 2 December 2017}}</ref>

===Measuring language usage===

Determining which is the most widely used programming language is difficult since the definition of usage varies by context. One language may occupy the greater number of programmer hours, a different one has more lines of code, and a third may consume the most CPU time. Some languages are very popular for particular kinds of applications. For example, [[COBOL]] is still strong in the corporate data center, often on large [[Mainframe computer|mainframes]];<ref>{{cite web
 |url         = http://www.computerworld.com.au/article/319269/cobol_turns_50/
 |title       = COBOL turns 50
 |date        = 2009-09-21
 |access-date  = 2013-10-19
 |author      = Georgina Swan
 |publisher   = Computerworld
 |url-status     = dead
 |archive-url  = https://web.archive.org/web/20131019181128/http://www.computerworld.com.au/article/319269/cobol_turns_50/
 |archive-date = 19 October 2013}}</ref><ref>{{cite web
 |url         = http://www.developer.com/lang/other/7-myths-of-cobol-debunked.html
 |title       = 7 Myths of COBOL Debunked
 |date        = 2012-05-03
 |access-date  = 2013-10-19
 |author      = Ed Airey
 |publisher   = developer.com
 |url-status     = live
 |archive-url  = https://web.archive.org/web/20131019171802/http://www.developer.com/lang/other/7-myths-of-cobol-debunked.html
 |archive-date = 19 October 2013}}</ref> [[Fortran]] in scientific and engineering applications; [[Ada (programming language)|Ada]] in aerospace, transportation, military, real-time, and embedded applications; and [[C (programming language)|C]] in embedded applications and operating systems. Other languages are regularly used to write many different kinds of applications.

Various methods of measuring language popularity, each subject to a different bias over what is measured, have been proposed:
* counting the number of job advertisements that mention the language<ref>{{cite web |author=Nicholas Enticknap |url=http://www.computerweekly.com/Articles/2007/09/11/226631/sslcomputer-weekly-it-salary-survey-finance-boom-drives-it-job.htm |title=SSL/Computer Weekly IT salary survey: finance boom drives IT job growth |work=Computer Weekly |access-date=2013-06-14 |url-status=live |archive-url=https://web.archive.org/web/20111026035734/http://www.computerweekly.com/Articles/2007/09/11/226631/SSLComputer-Weekly-IT-salary-survey-finance-boom-drives-IT-job.htm |archive-date=26 October 2011}}</ref>
* the number of books sold that teach or describe the language<ref>{{cite web|url=http://radar.oreilly.com/archives/2006/08/programming_language_trends_1.html|title=Counting programming languages by book sales|publisher=Radar.oreilly.com|date=2 August 2006|url-status=dead|archive-url=https://web.archive.org/web/20080517023127/http://radar.oreilly.com/archives/2006/08/programming_language_trends_1.html|archive-date=17 May 2008}}</ref>
* estimates of the number of existing lines of code written in the language{{spaced ndash}} which may underestimate languages not often found in public searches<ref>Bieman, J.M.; Murdock, V., Finding code on the World Wide Web: a preliminary investigation, Proceedings First IEEE International Workshop on Source Code Analysis and Manipulation, 2001</ref>
* counts of language references (i.e., to the name of the language) found using a web search engine.

Combining and averaging information from various internet sites, stackify.com reported the ten most popular programming languages (in descending order by overall popularity): [[Java (programming language)|Java]], [[C (programming language)|C]], [[C++]], [[Python (programming language)|Python]], [[C Sharp (programming language)|C#]], [[JavaScript]], [[Visual Basic .NET|VB .NET]], [[R (programming language)|R]], [[PHP]], and [[MATLAB]].<ref>{{cite web |url=https://stackify.com/popular-programming-languages-2018/ |title=Most Popular and Influential Programming Languages of 2018 |publisher=stackify.com |date=2017-12-18 |access-date=2018-08-29 |archive-date=30 August 2018 |archive-url=https://web.archive.org/web/20180830004924/https://stackify.com/popular-programming-languages-2018/ |url-status=live }}</ref>

As of June 2024, the top five programming languages as measured by [[TIOBE index]] are [[Python (programming language)|Python]], [[C++]], [[C (programming language)|C]], [[Java (programming language)|Java]] and [[C Sharp (programming language)|C#]]. TIOBE provides a list of top 100 programming languages according to popularity and update this list every month.<ref>{{cite web | url=https://www.tiobe.com/tiobe-index/ | title=TIOBE Index | access-date=24 June 2024 }}</ref>

==Dialects, flavors and implementations==
A '''dialect''' of a programming language or a [[data exchange language]] is a (relatively small) variation or extension of the language that does not change its intrinsic nature. With languages such as [[Scheme (programming language)|Scheme]] and [[Forth (programming language)|Forth]], standards may be considered insufficient, inadequate, or illegitimate by implementors, so often they will deviate from the standard, making a new [[dialect]]. In other cases, a dialect is created for use in a [[domain-specific language]], often a subset. In the [[Lisp (programming language)|Lisp]] world, most languages that use basic [[S-expression]] syntax and Lisp-like semantics are considered Lisp dialects, although they vary wildly as do, say, [[Racket (programming language)|Racket]] and [[Clojure]]. As it is common for one language to have several dialects, it can become quite difficult for an inexperienced programmer to find the right documentation. The [[BASIC]] language has [[List of BASIC dialects|many dialects]].

==Classifications==
{{details|Categorical list of programming languages}}
Programming languages are often placed into four main categories: [[Imperative programming|imperative]], [[functional programming|functional]], [[logic programming|logic]], and [[object oriented]].{{sfn|Sebesta|2012|p=21}}
*Imperative languages are designed to implement an algorithm in a specified order; they include [[visual programming languages]] such as [[.NET]] for generating [[graphical user interface]]s. [[Scripting languages]], which are partly or fully [[Interpreter (computing)|interpreted]] rather than [[compiler|compiled]], are sometimes considered a separate category but meet the definition of imperative languages.{{sfn|Sebesta|2012|pp=21–22}}
*Functional programming languages work by successively applying functions to the given parameters. Although appreciated by many researchers for their simplicity and elegance, problems with efficiency have prevented them from being widely adopted.{{sfn|Sebesta|2012|p=12}}
*Logic languages are designed so that the software, rather than the programmer, decides what order in which the instructions are executed.{{sfn|Sebesta|2012|p=22}}
*Object-oriented programming—whose characteristic features are [[data abstraction]], [[Inheritance (object-oriented programming)|inheritance]], and [[dynamic dispatch]]—is supported by most popular imperative languages and some functional languages.{{sfn|Sebesta|2012|pp=21–22}}
Although [[markup languages]] are not programming languages, some have extensions that support limited programming. Additionally, there are special-purpose languages that are not easily compared to other programming languages.{{sfn|Sebesta|2012|pp=22–23}}

==See also==
{{Portal|Computer programming}}

{{Div col}}
* [[Comparison of programming languages (basic instructions)]]
* [[Comparison of programming languages]]
* [[Computer programming]]
* [[Computer science]] and [[Outline of computer science]]
* [[Domain-specific language]]
* [[Domain-specific modeling]]
* [[Educational programming language]]
* [[Esoteric programming language]]
* [[Extensible programming]]
* [[:Category:Extensible syntax programming languages]]
* [[Invariant-based programming]]
* [[List of BASIC dialects]]
* [[List of open-source programming languages]]
* [[Lists of programming languages]]
* [[List of programming language researchers]]
* [[Programming languages used in most popular websites]]
* [[Language-oriented programming]]
* [[Logic programming]]
* [[Literate programming]]
* [[Metaprogramming]]
** {{Section link|Ruby (programming language)|Metaprogramming}}
* [[Modeling language]]
* [[Programming language theory]]
* [[Pseudocode]]
* {{Section link|Rebol|Dialects}}
* [[Reflective programming]]
* [[Scientific programming language]]
* [[Scripting language]]
* [[Software engineering]] and [[List of software engineering topics]]
{{Div col end}}

==References==
{{Reflist|30em}}

==Further reading==
{{see also|History of programming languages#Further reading}}
{{refbegin|30em}}
* {{cite book|last1=Abelson|first1=Harold|author-link1=Harold Abelson|last2=Sussman|first2=Gerald Jay|author-link2=Gerald Jay Sussman|title=Structure and Interpretation of Computer Programs|url=http://mitpress.mit.edu/sicp/full-text/book/book-Z-H-4.html|edition=2nd|year=1996|publisher=MIT Press|archive-url=https://web.archive.org/web/20180309173822/https://mitpress.mit.edu/sicp/full-text/book/book-Z-H-4.html|archive-date=9 March 2018|url-status=dead}}
* [[Raphael Finkel]]: ''[https://web.archive.org/web/20141022141742/http://www.nondot.org/sabre/Mirrored/AdvProgLangDesign/ Advanced Programming Language Design]'', Addison Wesley 1995.
* [[Daniel P. Friedman]], [[Mitchell Wand]], [[Christopher T. Haynes]]: ''[[Essentials of Programming Languages]]'', The MIT Press 2001.
* [[David Gelernter]], [[Suresh Jagannathan]]: ''Programming Linguistics'', [[The MIT Press]] 1990.
* [[Ellis Horowitz]] (ed.): ''Programming Languages, a Grand Tour'' (3rd ed.), 1987.
* Ellis Horowitz: ''Fundamentals of Programming Languages'', 1989.
* [[Shriram Krishnamurthi]]: ''[[Programming Languages: Application and Interpretation]]'', [http://www.cs.brown.edu/~sk/Publications/Books/ProgLangs/ online publication] {{Webarchive|url=https://web.archive.org/web/20210430210417/http://www.cs.brown.edu/~sk/Publications/Books/ProgLangs/ |date=30 April 2021 }}.
*{{cite book |last1=Gabbrielli |first1=Maurizio |last2=Martini |first2=Simone |title=Programming Languages: Principles and Paradigms |date=2023 |publisher=Springer |isbn=978-3-031-34144-1 |language=en|edition=2nd}}
* [[Bruce J. MacLennan]]: ''Principles of Programming Languages: Design, Evaluation, and Implementation'', [[Oxford University Press]] 1999.
* [[John C. Mitchell]]: ''Concepts in Programming Languages'', [[Cambridge University Press]] 2002.
*{{cite journal |last1=Nofre |first1=David |last2=Priestley |first2=Mark |last3=Alberts |first3=Gerard |title=When Technology Became Language: The Origins of the Linguistic Conception of Computer Programming, 1950–1960 |journal=Technology and Culture |date=2014 |volume=55 |issue=1 |pages=40–75 |doi=10.1353/tech.2014.0031 |jstor=24468397 |pmid=24988794 |url=https://www.jstor.org/stable/24468397 |issn=0040-165X}}
* [[Benjamin C. Pierce]]: ''[[Types and Programming Languages]]'', The MIT Press 2002.
* [[Terrence W. Pratt]] and [[Marvin Victor Zelkowitz]]: ''Programming Languages: Design and Implementation'' (4th ed.), Prentice Hall 2000.
* [[Peter H. Salus]]. ''Handbook of Programming Languages'' (4 vols.). Macmillan 1998.
* [[Ravi Sethi]]: ''Programming Languages: Concepts and Constructs'', 2nd ed., [[Addison-Wesley]] 1996.
* [[Michael L. Scott]] and Jonathan Aldrich: ''Programming Language Pragmatics'', 5th ed., [[Morgan Kaufmann Publishers]] 2025.
* {{cite book |last1=Sebesta |first1=Robert W. |title=Concepts of Programming Languages |date=2012 |publisher=Addison-Wesley |isbn=978-0-13-139531-2 |edition=10 |language=en}}
* Franklyn Turbak and David Gifford with Mark Sheldon: ''Design Concepts in Programming Languages'', The MIT Press 2009.
* [[Peter Van Roy]] and [[Seif Haridi]]. ''[[Concepts, Techniques, and Models of Computer Programming]]'', The MIT Press 2004.
* [[David A. Watt]]. ''Programming Language Concepts and Paradigms''. Prentice Hall 1990.
* David A. Watt and [[Muffy Thomas]]. ''Programming Language Syntax and Semantics''. Prentice Hall 1991.
* David A. Watt. ''Programming Language Processors''. Prentice Hall 1993.
* David A. Watt. ''Programming Language Design Concepts''. John Wiley & Sons 2004.
* {{cite book | last = Wilson | first = Leslie B. | title = Comparative Programming Languages, Third Edition | publisher = Addison-Wesley | year = 2001 | isbn = 0-201-71012-9}}
{{refend}}

{{Computer science}}
{{Types of programming languages}}
{{Programming languages}}
{{Computer language}}
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