Programming language

A programming language is an artificial language that can be used to control the behavior of a machine, particularly a computer. Programming languages, like human languages, are defined through the use of syntactic and semantic rules, to determine structure and meaning respectively.

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; sometimes the term "computer language" is used for more limited artificial languages.

Thousands of different programming languages have been created, and new ones are created every year.

Definitions

Authors disagree on the precise definition, but traits often considered important requirements and objectives of the language to be characterized as a programming language:

Function: A programming language is a language used to write computer programs, which instruct a computer to perform some kind of computation, and/or organize the flow of control between external devices (such as a printer, a robot, or any peripheral).
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 (see Chomsky hierarchy). 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.
Non-computational languages, such as markup languages like HTML or formal grammars like BNF, are usually not considered programming languages. It is a usual approach to embed a programming language into the non-computational (host) language, to express templates for the host language.

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, all of them have failed to be accepted in 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 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.

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.

Elements

Syntax
Parse tree of Python code with inset tokenization Syntax highlighting is often used to aid programmers in the recognition of elements of source code. The language you see here is PythonA 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 ::= [+-]?['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: '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
For more details on this topic, see 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, "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.

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.

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 alternative definition for "weakly typed" refers to languages, such as Perl, Javascript, and C++ which permit a large number of implicit type conversions; Perl in particular can be characterized as a dynamically typed programming language in which type checking can take place at runtime. See type system. This capability is often useful, but occasionally dangerous; as it would permit operations whose objects can change type on demand.

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.


Core library
For more details on this topic, see Standard 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.