Programming language

A programming language is an artificial language that can be used to control the behavior of a machine (often a computer). Like human languages, programming languages have syntactic and semantic rules used to define meaning. Programming languages are used to facilitate communication about the task of organizing and manipulating information, and to express algorithms precisely. Some authors restrict the term "programming language" to those languages that can express all possible algorithms;In mathematical terms, this means the programming language is Turing-complete sometimes the term "computer language" is used for more limited artificial languages.

Thousands of different programming languagesAs of May 2006 The Encyclopedia of Computer Languages by Murdoch University, Australia lists 8512 computer languages. have been created and new ones are created every year. Few languages ever become sufficiently popular that they are used by more than a few people, but professional programmers may use dozens of different languages during their careers.

Definitions

Authors disagree on the precise definition of programming language. Traits often considered important include:

Function: A programming language is a language used to write computer programs, which instruct a computer to perform some kind of computation, 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. and/or organize the flow of control between mechanical devices.
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. In some cases, programming languages are used by one program or machine to program another; Postscript source code, for example, is frequently generated programmatically to control a computer printer or display.
Constructs: Programming languages may contain constructs for defining and manipulating data structures or for controlling the flow of execution.
Expressive power: The theory of computation classifies languages by the computations they can express. All Turing complete languages can implement the same algorithms. ANSI/ISO SQL and Charity are examples of languages that are not Turing complete yet often called programming languages., Charity is a categorical programming language..., All Charity computations terminate.

Non-computational languages, such as markup languages like HTML or formal grammars like BNF, are usually not considered programming languages; however, informal usage sometimes includes them.

Purpose

A prominent purpose of programming languages is to provide instructions to a computer. As such, 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, computers do exactly what they are told to do, and cannot understand the code the programmer "intended" to write. The combination of the language definition, the program, and the program's inputs must fully specify the external behavior that occurs when the program is executed.

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 (e.g., PL/I), all of them have failed to be accepted in this role.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. (). Despite the initial high hopes, PL/I is rarely used today; and widespread languages have been only minimally influenced by PL/I ideas. Ada had similar early goals. 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 removed from simple translation into underlying hardware instructions. Because programmers are less tied to the needs of the computer, their programs can do more computing with less effort from the programmer. This lets them write more programs in the same amount of time. Frederick P. Brooks, Jr.: The Mythical Man-Month, Addison-Wesley, 1982, pp. 93-94

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."Dijkstra, Edsger W. On the foolishness of "natural language programming." EWD667. Alan Perlis was similarly dismissive of the idea.Perlis, Alan, Epigrams on Programming. SIGPLAN Notices Vol. 17, No. 9, September 1982, pp. 7-13

Elements

Syntax

A programming language's surface form is known as its syntax. Most programming languages are purely textual; they use sequences of text including words, numbers, and punctuation, much like written natural languages. On the other hand, there are some programming languages which are more graphical in nature, using spatial 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. 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 ::= ^*?^*+ symbol ::= ^*.* 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: '12345', '()', '(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);

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.

Internally, all data in modern digital computers are stored simply as zeros or ones (binary). The data typically represent information in the real world such as names, bank accounts and measurements, so the low-level binary data are organized by programming languages into these high-level concepts as data types. There are also more abstract types whose purpose is just to warn the programmer about semantically meaningless statements or verify safety properties of programs.

Languages can be classified with respect to their type systems.

Typed vs untyped languages

A language is typed if operations defined for one data type cannot be performed on values of another data type. For example, "word" 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.

By opposition, an untyped language, such as most assembly languages, allows any operation to be performed on any data type.. 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 vs 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 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++ 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.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 Dec 2001) and Sound Generic Java type inference (Martin Odersky, 15 Jan 2002). C#'s type system is similar to Java's, and uses a similar partial type inference scheme.

Dynamic typing, also called latent typing, determines the type-safety of operations at runtime; in other words, types are associated with runtime values rather than textual expressions. As with type-inferred languages, dynamically typed languages do not require the programmer to write explicit type annotations on expressions. Among other things, this may permit a single variable to refer to values of different types at different points in the program execution. However, type errors cannot be automatically detected until a piece of code is actually executed, making debugging more difficult. Lisp, JavaScript, and Python are dynamically typed.

Weak and strong

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 cause bugs; such languages are often termed unsafe. C, C++, and most assembly languages are often described as weakly typed.

Strong typing prevents the above. Attempting to mix types raises an error. Strongly-typed languages are often termed type-safe or safe, but they do not make bugs impossible. Ada, Python, and ML are strongly typed.

An alternate definition for "weakly typed" refers to languages, such as Perl, Javascript, and C++ which permit a large number of implicit type conversions. The expression "123" + 45 is legal in Javascript; and produces the result 168; in evaluating the expression, a Javascript engine will note that the string "123" is convertable to a number, implicitly perform the conversion, and then add 123 and 45 to produce the final sum. However, note that the expression "123" + "45" is "12345" (and neither 168 nor "168"); the + operator when applied to strings in Javascript performs string concatenation. This capability is often useful, but occasionally dangerous; as it may permit enable operations which the programmer didn't intend to succeed, often producing unexpected results.

Strong and static are 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.

Standard library

Most programming languages have an associated standard library, which is conventionally made available in all implementations of the language. Standard libraries typically include definitions for commonly used algorithms, data structures, and mechanisms for input and output.

Often, a language's standard library is treated as part of the language by its designers and users. Most language specifications define a standard library that must be made available in all conforming implementations. The line between a language and its standard library is therefore porous. Indeed, some languages are designed so that the meanings of certain syntactic constructs cannot even be described without referring to the standard library. For example, in Java, a string literal is defined as an instance of the java.lang.String class from the standard library; similarly, in Smalltalk, an anonymous function expression (a "block") constructs an instance of the standard 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 the standard library.

Practice

A 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's specification and implementation.

Specification

The specification of a programming language is intended to provide a definition that language users and implementors can use to interpret the behavior of programs when reading their source code.

A programming language specification can take several forms, including the following:

An explicit definition of the syntax and 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). The syntax and semantics of the language has to be inferred from this description, which may be written in natural or a formal language.
A model implementation, sometimes written in the language being specified (e.g., the Prolog). The syntax and semantics of the language are explicit in the behavior of the model implementation.

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. Some implementations support both interpretation and compilation.

An interpreter parses a computer program and executes it directly. This can be imagined as following the instructions of the program line-by-line. By contrast, a compiler translates the program into another language, which may in turn be either interpreted or compiled. Some compilers translate a program into machine code (instructions that can be interpreted directly by a piece of computing hardware).

Programs compiled to machine code usually run faster than interpreted ones, because the work of parsing and translating the programming language has already been done. However, interpreters are frequently easier to write than compilers.

Many modern languages use a mixture of compilation and interpretation. The "compiler" for a bytecode-based language translates the source code into a partially compiled intermediate format, which is later run by a fast interpreter called a virtual machine. Some "interpreters" actually use a just-in-time compiler, which compiles the code to machine language immediately before running it. These techniques are often combined. An unusual case is Forth, which is described as incrementally compiled. Like other aspects of programming languages, "compiled" and "interpreted" may be best understood as opposite ends of a spectrum, rather than as discrete options.

History

Early developments

In some sense, the first programming languages predate the modern computer. At a practical level, punch cards were used by the beginning of the 20th Century to encode data and perform limited mechanical processing; even before that "programmable" looms and player piano scrolls implemented limited domain-specific programming languages. 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.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."

In the 1940s the first recognizably modern, electrically powered computers were created. Their limited speed and memory capacity required programmers to write hand tuned assembly language programs. During the 1950s, the first three modern programming languages were developed: FORTRAN, LISP and COBOL. Variants of all of these are still in general use, and importantly, 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 modern programming languages are, in many respects, descendents of Algol.

Refinement

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 programming languages:

APL introduced array programming, and influenced functional programming.Richard L. Wexelblat: History of Programming Languages, Academic Press, 1981, chapter XIV.
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.. 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.
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.

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 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. Module systems were often wedded to generic programming constructs.

The rapid growth of the Internet in the mid-1990's created an opportunity for new languages to be adopted. In particular, the Java programming language rose to popularity because of its early integration with web browsers, and various scripting languages achieved widespread use in developing customized applications for web servers. Neither of these developments represented much fundamental novelty in language design, merely refinements in existing languages and paradigms.

Programming language evolution continues, in both industry and research. Current directions include mechanisms for adding security and reliability verification, alternative mechanisms for modularity (mixins, delegates, aspects), integration with databases, and a focus on open source as a developmental philosophy.

Taxonomies

There is no overarching classification scheme for programming languages. Any 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.

References

External links

99 Bottles of Beer - a collection of implementations in many languages
Computer Languages History graphical chart
Dictionary of Programming Languages
History of Programming Languages (HOPL)
Open Directory - Computer Programming Languages
Programming Language Comparison
Syntax Patterns for Various Languages

Programming language topics | Computer languages

Definitions

Purpose

Elements

Syntax

Type system

Typed vs untyped languages

Static vs dynamic typing

Weak and strong

Execution semantics

Standard library

Practice

Specification

Implementation

History

Early developments

Refinement

Consolidation and growth

Taxonomies

See also

References

Further reading

External links

Gourt Home::Gourt :: Programming language

Definitions

Purpose

Elements

Syntax

Type system

Typed vs untyped languages

Static vs dynamic typing

Weak and strong

Execution semantics

Standard library

Practice

Specification

Implementation

History

Early developments

Refinement

Consolidation and growth

Taxonomies

See also

References

Further reading

External links