From Wikipedia, the free encyclopedia
A programming language is a machine-readable artificial language designed to express computations that can be performed by a machine, particularly a computer. Programming languages can be used to create programs that specify the behavior of a machine, to express algorithms precisely, or as a mode of human communication.
Many programming languages have some form of written specification of their syntax and semantics, since computers require precisely defined instructions. Some (such as C) are defined by a specification document (for example, an ISO Standard), while others (such as Perl) have a dominant implementation.
The earliest programming languages predate the invention of the computer, and were used to direct the behavior of machines such as Jacquard looms and player pianos. Thousands of different programming languages have been created, mainly in the computer field, with many more being created every year.
Traits often considered important for constituting a programming language:
- Function: A programming language is a language used to write computer programs, which involve a computer performing some kind of computation or algorithm and possibly control external devices such as printers, robots, and so on.
- Target: Programming languages differ from natural languages in that natural languages are only used for interaction between people, while programming languages also allow humans to communicate instructions to machines. Some programming languages are used by one device to control another. For example PostScript programs are frequently created by another program to control a computer printer or display.
- Constructs: Programming languages may contain constructs for defining and manipulating data structures or controlling the flow of execution.
- Expressive power: The theory of computation classifies languages by the computations they are capable of expressing. All Turing complete languages can implement the same set of algorithms. ANSI/ISO SQL and Charity are examples of languages that are not Turing complete, yet often called programming languages.
Some authors restrict the term “programming language” to those languages that can express all possible algorithms; sometimes the term “computer language” is used for more limited artificial languages.
Non-computational languages, such as markup languages like HTML or formal grammars like BNF, are usually not considered programming languages. A programming language (which may or may not be Turing complete) may be embedded in these non-computational (host) languages.
A programming language provides a structured mechanism for defining pieces of data, and the operations or transformations that may be carried out automatically on that data. A programmer uses the abstractions present in the language to represent the concepts involved in a computation. These concepts are represented as a collection of the simplest elements available (called primitives). 
Programming 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.
Programs for a computer might be executed in a batch process without human interaction, or a user might type commands in an interactive session of an interpreter. In this case the “commands” are simply programs, whose execution is chained together. When a language is used to give commands to a software application (such as a shell) it is called a scripting language.
Many languages have been designed from scratch, altered to meet new needs, combined with other languages, and eventually fallen into disuse. Although there have been attempts to design one “universal” computer language that serves all purposes, all of them have failed to be generally accepted as filling this role. The need for diverse computer languages arises from the diversity of contexts in which languages are used:
- Programs range from tiny scripts written by individual hobbyists to huge systems written by hundreds of programmers.
- Programmers range in expertise from novices who need simplicity above all else, to experts who may be comfortable with considerable complexity.
- Programs must balance speed, size, and simplicity on systems ranging from microcontrollers to supercomputers.
- Programs may be written once and not change for generations, or they may undergo nearly constant modification.
- Finally, programmers may simply differ in their tastes: they may be accustomed to discussing problems and expressing them in a particular language.
One common trend in the development of programming languages has been to add more ability to solve problems using a higher level of abstraction. The earliest programming languages were tied very closely to the underlying hardware of the computer. As new programming languages have developed, features have been added that let 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. This lets them write more functionality per time unit.
Natural language processors have 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 Dijkstra took the position that the use of a formal language is essential to prevent the introduction of meaningless constructs, and dismissed natural……… language programming as “foolish”. Alan Perlis was similarly dismissive of the idea.
All programming languages have some primitive building blocks for the description of data and the processes or transformations applied to them (like the addition of two numbers or the selection of an item from a collection). These primitives are defined by syntactic and semantic rules which describe their structure and meaning respectively.
A programming language’s surface form is known as its syntax. Most programming languages are purely textual; they use sequences of text including words, numbers, and punctuation, much like written natural languages. On the other hand, there are some programming languages which are more graphical in nature, using visual relationships between symbols to specify a program.
The syntax of a language describes the possible combinations of symbols that form a syntactically correct program. The meaning given to a combination of symbols is handled by semantics (either formal or hard-coded in a reference implementation). Since most languages are textual, this article discusses textual syntax.
Programming language syntax is usually defined using a combination of regular expressions (for lexical structure) and Backus-Naur Form (for grammatical structure). Below is a simple grammar, based on Lisp:
expression ::= atom | list atom ::= number | symbol number ::= [+-]?['0'-'9']+ symbol ::= ['A'-'Z''a'-'z'].* list ::= '(' expression* ')'
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 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: ‘
(a b c232 (1))‘
Not all syntactically correct programs are semantically correct. Many syntactically correct programs are nonetheless ill-formed, per the language’s rules; and may (depending on the language specification and the soundness of the implementation) result in an error on translation or execution. In some cases, such programs may exhibit undefined behavior. Even when a program is well-defined within a language, it may still have a meaning that is not intended by the person who wrote it.
Using natural language as an example, it may not be possible to assign a meaning to a grammatically correct sentence or the sentence may be false:
- “Colorless green ideas sleep furiously.” is grammatically well-formed but has no generally accepted meaning.
- “John is a married bachelor.” is grammatically well-formed but expresses a meaning that cannot be true.
The following C language fragment is syntactically correct, but performs an operation that is not semantically defined (because p is a null pointer, the operations p->real and p->im have no meaning):
complex *p = NULL; complex abs_p = sqrt (p->real * p->real + p->im * p->im);
The grammar needed to specify a programming language can be classified by its position in the Chomsky hierarchy. The syntax of most programming languages can be specified using a Type-2 grammar, i.e., they are context-free grammars.
 Static semantics
The static semantics defines restrictions on the structure of valid texts that are hard or impossible to express in standard syntactic formalisms. The most important of these restrictions are covered by type systems.
 Type system
A type system defines how a programming language classifies values and expressions into types, how it can manipulate those types and how they interact. This generally includes a description of the data structures that can be constructed in the language. The design and study of type systems using formal mathematics is known as type theory.
 Typed versus untyped languages
A language is typed if the specification of every operation defines types of data to which the operation is applicable, with the implication that it is not applicable to other types. For example, “
this text between the quotes” is a string. In most programming languages, dividing a number by a string has no meaning. Most modern programming languages will therefore reject any program attempting to perform such an operation. In some languages, the meaningless operation will be detected when the program is compiled (“static” type checking), and rejected by the compiler, while in others, it will be detected when the program is run (“dynamic” type checking), resulting in a runtime exception.
A special case of typed languages are the single-type languages. These are often scripting or markup languages, such as Rexx or SGML, and have only one data type—most commonly character strings which are used for both symbolic and numeric data.
In contrast, an untyped language, such as most assembly languages, allows any operation to be performed on any data, which are generally considered to be sequences of bits of various lengths. High-level languages which are untyped include BCPL and some varieties of Forth.
In practice, while few languages are considered typed from the point of view of type theory (verifying or rejecting all operations), most modern languages offer a degree of typing. Many production languages provide means to bypass or subvert the type system.
 Static versus dynamic typing
In static typing all expressions have their types determined prior to the program being run (typically at compile-time). For example, 1 and (2+2) are integer expressions; they cannot be passed to a function that expects a string, or stored in a variable that is defined to hold dates.
Statically typed languages can be either manifestly typed or type-inferred. In the first case, the programmer must explicitly write types at certain textual positions (for example, at variable declarations). In the second case, the compiler infers the types of expressions and declarations based on context. Most mainstream statically typed languages, such as C++, C# and Java, are manifestly typed. Complete type inference has traditionally been associated with less mainstream languages, such as Haskell and ML. However, many manifestly typed languages support partial type inference; for example, Java and C# both infer types in certain limited cases.
 Weak and strong typing
Weak typing allows a value of one type to be treated as another, for example treating a string as a number. This can occasionally be useful, but it can also allow some kinds of program faults to go undetected at compile time and even at run time.
2 * x implicitly converts
x to a number, and this conversion succeeds even if
Array, or a string of letters. Such implicit conversions are often useful, but they can mask programming errors.
Strong and static are now generally considered orthogonal concepts, but usage in the literature differs. Some use the term strongly typed to mean strongly, statically typed, or, even more confusingly, to mean simply statically typed. Thus C has been called both strongly typed and weakly, statically typed.
 Execution semantics
Once data has been specified, the machine must be instructed to perform operations on the data. The execution semantics of a language defines how and when the various constructs of a language should produce a program behavior.
For example, the semantics may define the strategy by which expressions are evaluated to values, or the manner in which control structures conditionally execute statements.
 Core library
Most programming languages have an associated core library (sometimes known as the ‘Standard library’, especially if it is included as part of the published language standard), which is conventionally made available by all implementations of the language. Core libraries typically include definitions for commonly used algorithms, data structures, and mechanisms for input and output.
A language’s core library is often treated as part of the language by its users, although the designers may have treated it as a separate entity. Many language specifications define a core that must be made available in all implementations, and in the case of standardized languages this core library may be required. The line between a language and its core library therefore differs from language to language. Indeed, some languages are designed so that the meanings of certain syntactic constructs cannot even be described without referring to the core library. For example, in Java, a string literal is defined as an instance of the java.lang.String class; similarly, in Smalltalk, an anonymous function expression (a “block”) constructs an instance of the library’s BlockContext class. Conversely, Scheme contains multiple coherent subsets that suffice to construct the rest of the language as library macros, and so the language designers do not even bother to say which portions of the language must be implemented as language constructs, and which must be implemented as parts of a library.
A Jovan language’s designers and users must construct a number of artifacts that govern and enable the practice of programming. The most important of these artifacts are the language specification and implementation.
The specification of a programming language is intended to provide a definition that the language users and the implementors can use to determine whether the behavior of a program is correct, given its source code.
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., the C language), or a formal semantics (e.g., the Standard ML and Scheme specifications).
- A description of the behavior of a translator for the language (e.g., the C++ and Fortran specifications). The syntax and semantics of the language have to be inferred from this description, which may be written in natural or a formal language.
- A reference or model implementation, sometimes written in the language being specified (e.g., Prolog or ANSI REXX). The syntax and semantics of the language are explicit in the behavior of the reference implementation.
An implementation of a programming language provides a way to execute that program on one or more configurations of hardware and software. There are, broadly, two approaches to programming language implementation: compilation and interpretation. It is generally possible to implement a language using either technique.
The output of a compiler may be executed by hardware or a program called an interpreter. In some implementations that make use of the interpreter approach there is no distinct boundary between compiling and interpreting. For instance, some implementations of the BASIC programming language compile and then execute the source a line at a time.
Programs that are executed directly on the hardware usually run several orders of magnitude faster than those that are interpreted in software.
One technique for improving the performance of interpreted programs is just-in-time compilation. Here the virtual machine, just before execution, translates the blocks of bytecode which are going to be used to machine code, for direct execution on the hardware.
 Early developments
The first programming languages predate the modern computer. The 19th century had “programmable” looms and player piano scrolls which implemented what are today recognized as examples of domain-specific programming languages. By the beginning of the twentieth century, punch cards encoded data and directed mechanical processing. In the 1930s and 1940s, the formalisms of Alonzo Church‘s lambda calculus and Alan Turing‘s Turing machines provided mathematical abstractions for expressing algorithms; the lambda calculus remains influential in language design.
In the 1940s, the first electrically powered digital computers were created. The first high-level programming language to be designed for a computer was Plankalkül, developed for the German Z3 by Konrad Zuse between 1943 and 1945. However, it was not implemented until much later because of wartime damage.
Programmers of early 1950s computers, notably UNIVAC I and IBM 701, used machine language programs, that is, the first generation language (1GL). 1GL programming was quickly superseded by similarly machine-specific, but mnemonic, second generation languages (2GL) known as assembly languages or “assembler”. Later in the 1950s, assembly language programming, which had evolved to include the use of macro instructions, was followed by the development of “third generation” programming languages (3GL), such as FORTRAN, LISP, and COBOL. 3GLs are more abstract and are “portable”, or at least implemented similar on computers that do not support the same native machine code. Updated versions of all of these 3GLs are still in general use, and each has strongly influenced the development of later languages. At the end of the 1950s, the language formalized as Algol 60 was introduced, and most later programming languages are, in many respects, descendants of Algol. The format and use of the early programming languages was heavily influenced by the constraints of the interface.
The period from the 1960s to the late 1970s brought the development of the major language paradigms now in use, though many aspects were refinements of ideas in the very first Third-generation programming languages:
- APL introduced array programming and influenced functional programming.
- PL/I (NPL) was designed in the early 1960s to incorporate the best ideas from FORTRAN and COBOL.
- In the 1960s, Simula was the first language designed to support object-oriented programming; in the mid-1970s, Smalltalk followed with the first “purely” object-oriented language.
- C was developed between 1969 and 1973 as a systems programming language, and remains popular.
- Prolog, designed in 1972, was the first logic programming language.
- In 1978, ML built a polymorphic type system on top of Lisp, pioneering statically typed functional programming languages.
Each of these languages spawned an entire family of descendants, and most modern languages count at least one of them in their ancestry.
The 1960s and 1970s also saw considerable debate over the merits of structured programming, and whether programming languages should be designed to support it. Edsger Dijkstra, in a famous 1968 letter published in the Communications of the ACM, argued that GOTO statements should be eliminated from all “higher level” programming languages.
The 1960s and 1970s also saw expansion of techniques that reduced the footprint of a program as well as improved productivity of the programmer and user. The card deck for an early 4GL was a lot smaller for the same functionality expressed in a 3GL deck.
 Consolidation and growth
The 1980s were years of relative consolidation. C++ combined object-oriented and systems programming. The United States government standardized Ada, a systems programming language derived from Pascal and intended for use by defense contractors. In Japan and elsewhere, vast sums were spent investigating so-called “fifth generation” languages that incorporated logic programming constructs. The functional languages community moved to standardize ML and Lisp. Rather than inventing new paradigms, all of these movements elaborated upon the ideas invented in the previous decade.
One important trend in language design during the 1980s was an increased focus on programming for large-scale systems through the use of modules, or large-scale organizational units of code. Modula-2, Ada, and ML all developed notable module systems in the 1980s, although other languages, such as PL/I, already had extensive support for modular programming. Module systems were often wedded to generic programming constructs.
The rapid growth of the Internet in the mid-1990s created opportunities for new languages. Perl, originally a Unix scripting tool first released in 1987, became common in dynamic Web sites. Java came to be used for server-side programming. These developments were not fundamentally novel, rather they were refinements to existing languages and paradigms, and largely based on the C family of programming languages.
Programming language evolution continues, in both industry and research. Current directions include security and reliability verification, new kinds of modularity (mixins, delegates, aspects), and database integration such as Microsoft’s LINQ.
 Measuring language usage
It is difficult to determine which programming languages are most widely used, and what usage means varies by context. One language may occupy the greater number of programmer hours, a different one have more lines of code, and a third utilize the most CPU time. Some languages are very popular for particular kinds of applications. For example, COBOL is still strong in the corporate data center, often on large mainframes; FORTRAN in engineering applications; C in embedded applications and operating systems; and other languages are regularly used to write many different kinds of applications.
Various methods of measuring language popularity, each subject to a different bias over what is measured, have been proposed:
- counting the number of job advertisements that mention the language
- the number of books sold that teach or describe the language
- estimates of the number of existing lines of code written in the language—which may underestimate languages not often found in public searches
- counts of language references (i.e., to the name of the language) found using a web search engine.
There is no overarching classification scheme for programming languages. A given programming language does not usually have a single ancestor language. Languages commonly arise by combining the elements of several predecessor languages with new ideas in circulation at the time. Ideas that originate in one language will diffuse throughout a family of related languages, and then leap suddenly across familial gaps to appear in an entirely different family.
The task is further complicated by the fact that languages can be classified along multiple axes. For example, Java is both an object-oriented language (because it encourages object-oriented organization) and a concurrent language (because it contains built-in constructs for running multiple threads in parallel). Python is an object-oriented scripting language.
In broad strokes, programming languages divide into programming paradigms and a classification by intended domain of use. Paradigms include procedural programming, object-oriented programming, functional programming, and logic programming; some languages are hybrids of paradigms or multi-paradigmatic. An assembly language is not so much a paradigm as a direct model of an underlying machine architecture. By purpose, programming languages might be considered general purpose, system programming languages, scripting languages, domain-specific languages, or concurrent/distributed languages (or a combination of these). Some general purpose languages were designed largely with educational goals.
A programming language may also be classified by factors unrelated to programming paradigm. For instance, most programming languages use English language keywords, while a minority do not. Other languages may be classified as being esoteric or not.
 See also
- Comparison of basic instructions of programming languages
- Comparison of programming languages
- Computer programming
- Computer science and List of basic computer science topics
- Educational programming language
- Invariant based programming
- Lists of programming languages
- Literate programming
- Programming language dialect
- Programming language theory
- Software engineering and List of software engineering topics
- ^ “HOPL: an interactive Roster of Programming Languages”. Australia: Murdoch University. http://hopl.murdoch.edu.au/. Retrieved on 2009-06-01. “This site lists 8512 languages.”
- ^ ACM SIGPLAN (2003). “Bylaws of the Special Interest Group on Programming Languages of the Association for Computing Machinery”. http://www.acm.org/sigs/sigplan/sigplan_bylaws.htm. Retrieved on 2006-06-19., The scope of SIGPLAN is the theory, design, implementation, description, and application of computer programming languages – languages that permit the specification of a variety of different computations, thereby providing the user with significant control (immediate or delayed) over the computer’s operation.
- ^ Dean, Tom (2002). “Programming Robots”. Building Intelligent Robots. Brown University Department of Computer Science. http://www.cs.brown.edu/people/tld/courses/cs148/02/programming.html. Retrieved on 2006-09-23.
- ^ Digital Equipment Corporation. “Information Technology – Database Language SQL (Proposed revised text of DIS 9075)”. ISO/IEC 9075:1992, Database Language SQL. http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt. Retrieved on June 29 2006.
- ^ The Charity Development Group (December 1996). “The CHARITY Home Page”. http://pll.cpsc.ucalgary.ca/charity1/www/home.html. Retrieved on 2006-06-29., Charity is a categorical programming language…, All Charity computations terminate.
- ^ In mathematical terms, this means the programming language is Turing-complete MacLennan, Bruce J. (1987). Principles of Programming Languages. Oxford University Press. p. 1. ISBN 0-19-511306-3.
- ^ Abelson, Sussman, and Sussman. “Structure and Interpretation of Computer Programs”. http://mitpress.mit.edu/sicp/full-text/book/book-Z-H-10.html. Retrieved on 2009-03-03.
- ^ IBM in first publishing PL/I, for example, rather ambitiously titled its manual The universal programming language PL/I (IBM Library; 1966). The title reflected IBM’s goals for unlimited subsetting capability: PL/I is designed in such a way that one can isolate subsets from it satisfying the requirements of particular applications. (“Encyclopaedia of Mathematics » P » PL/I”. SpringerLink. http://eom.springer.de/P/p072885.htm. Retrieved on June 29 2006.). Ada and UNCOL had similar early goals.
- ^ Frederick P. Brooks, Jr.: The Mythical Man-Month, Addison-Wesley, 1982, pp. 93-94
- ^ Dijkstra, Edsger W. On the foolishness of “natural language programming.” EWD667.
- ^ Perlis, Alan, Epigrams on Programming. SIGPLAN Notices Vol. 17, No. 9, September 1982, pp. 7-13
- ^ Michael Sipser (1997). Introduction to the Theory of Computation. PWS Publishing. ISBN 0-534-94728-X. Section 2.2: Pushdown Automata, pp.101–114.
- ^ Aaby, Anthony (2004). Introduction to Programming Languages. http://web.archive.org/web/20040407162301/cs.wwc.edu/~aabyan/PLBook/HTML/index.html.
- ^ a b c d e f g Andrew Cooke. “An Introduction to Programming Languages”. http://www.acooke.org/andrew/writing/lang.html#sec-types. Retrieved on June 30 2006.[dead link]
- ^ Specifically, instantiations of generic types are inferred for certain expression forms. Type inference in Generic Java—the research language that provided the basis for Java 1.5’s bounded parametric polymorphism extensions—is discussed in two informal manuscripts from the Types mailing list: Generic Java type inference is unsound (Alan Jeffrey, 17 December 2001) and Sound Generic Java type inference (Martin Odersky, 15 January 2002). C#’s type system is similar to Java’s, and uses a similar partial type inference scheme.
- ^ “Revised Report on the Algorithmic Language Scheme (February 20, 1998)”. http://www.schemers.org/Documents/Standards/R5RS/HTML/r5rs-Z-H-4.html. Retrieved on June 9 2006.
- ^ Luca Cardelli and Peter Wegner. “On Understanding Types, Data Abstraction, and Polymorphism”. Manuscript (1985). http://citeseer.ist.psu.edu/cardelli85understanding.html. Retrieved on June 9 2006.
- ^ Milner, R.; M. Tofte, R. Harper and D. MacQueen. (1997). The Definition of Standard ML (Revised). MIT Press. ISBN 0-262-63181-4.
- ^ Kelsey, Richard; William Clinger and Jonathan Rees (February 1998). “Section 7.2 Formal semantics”. Revised5 Report on the Algorithmic Language Scheme. http://www.schemers.org/Documents/Standards/R5RS/HTML/r5rs-Z-H-10.html#%_sec_7.2. Retrieved on 2006-06-09.
- ^ ANSI — Programming Language Rexx, X3-274.1996
- ^ Benjamin C. Pierce writes:
- “… the lambda calculus has seen widespread use in the specification of programming language features, in language design and implementation, and in the study of type systems.”
- ^ a b O’Reilly Media. “History of programming languages” (PDF). http://www.oreilly.com/news/graphics/prog_lang_poster.pdf. Retrieved on October 5 2006.
- ^ Frank da Cruz. IBM Punch Cards Columbia University Computing History.
- ^ Richard L. Wexelblat: History of Programming Languages, Academic Press, 1981, chapter XIV.
- ^ François Labelle. “Programming Language Usage Graph”. Sourceforge. http://www.cs.berkeley.edu/~flab/languages.html. Retrieved on June 21 2006.. This comparison analyzes trends in number of projects hosted by a popular community programming repository. During most years of the comparison, C leads by a considerable margin; in 2006, Java overtakes C, but the combination of C/C++ still leads considerably.
- ^ Hayes, Brian (2006), “The Semicolon Wars”, American Scientist 94 (4): 299–303
- ^ Dijkstra, Edsger W. (March 1968). “Go To Statement Considered Harmful“. Communications of the ACM 11 (3): 147–148. doi:10.1145/362929.362947. http://www.acm.org/classics/oct95/. Retrieved on 2006-06-29.
- ^ Tetsuro Fujise, Takashi Chikayama Kazuaki Rokusawa, Akihiko Nakase (December 1994). “KLIC: A Portable Implementation of KL1” Proc. of FGCS ’94, ICOT Tokyo, December 1994. KLIC is a portable implementation of a concurrent logic programming language KL1.
- ^ Jim Bender (March 15, 2004). “Mini-Bibliography on Modules for Functional Programming Languages”. ReadScheme.org. http://readscheme.org/modules/. Retrieved on 2006-09-27.
- ^ Wall, Programming Perl ISBN 0-596-00027-8 p.66
- ^ Survey of Job advertisements mentioning a given language
- ^ Counting programming languages by book sales
- ^ 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
- ^ Programming Language Popularity
- ^ “TUNES: Programming Languages”. http://tunes.org/wiki/programming_20languages.html.
- ^ Wirth, Niklaus (1993). “Recollections about the development of Pascal“. Proc. 2nd ACM SIGPLAN conference on history of programming languages: 333–342. doi:10.1145/154766.155378. http://portal.acm.org/citation.cfm?id=155378. Retrieved on 2006-06-30.
 Further reading
- Daniel P. Friedman, Mitchell Wand, Christopher Thomas Haynes: Essentials of Programming Languages, The MIT Press 2001.
- David Gelernter, Suresh Jagannathan: Programming Linguistics, The MIT Press 1990.
- Shriram Krishnamurthi: Programming Languages: Application and Interpretation, online publication.
- 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.
- Benjamin C. Pierce: Types and Programming Languages, The MIT Press 2002.
- Ravi Sethi: Programming Languages: Concepts and Constructs, 2nd ed., Addison-Wesley 1996.
- Michael L. Scott: Programming Language Pragmatics, Morgan Kaufmann Publishers 2005.
- Richard L. Wexelblat (ed.): History of Programming Languages, Academic Press 1981.
 External links
- 99 Bottles of Beer A collection of implementations in many languages.
- Computer Programming Languages at the Open Directory Project
- Syntax Patterns for Various Languages