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le, a null pointer constant can be written as 0, with or without explicit casting to a pointer type, as the NULL macro defined by several standard headers or, since C23 with the constant nullptr. In conditional contexts, null pointer values evaluate to false, while all other pointer values evaluate to true.
Void pointers (void *) point to objects of unspecified type, and can therefore be used as "generic" data pointers. Since the size and type of the pointed-to object is not known, void pointers cannot be dereferenced, nor is pointer arithmetic on them allowed, although they can easily be (and in many contexts implicitly are) converted to and from any other object pointer type.
Careless use of pointers is potentially dangerous. Because they are typically unchecked, a pointer variable can be made to point to any arbitrary location, which can cause undesirable effects. Although properly used pointers point to safe places, they can be made to point to unsafe places by using invalid poi
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ade to point to unsafe places by using invalid pointer arithmetic; the objects they point to may continue to be used after deallocation (dangling pointers); they may be used without having been initialized (wild pointers); or they may be directly assigned an unsafe value using a cast, union, or through another corrupt pointer. In general, C is permissive in allowing manipulation of and conversion between pointer types, although compilers typically provide options for various levels of checking. Some other programming languages address these problems by using more restrictive reference types.
=== Arrays ===
Array types in C are traditionally of a fixed, static size specified at compile time. The more recent C99 standard also allows a form of variable-length arrays. However, it is also possible to allocate a block of memory (of arbitrary size) at run-time, using the standard library's malloc function, and treat it as an array.
Since arrays are always accessed (in effect) via pointer
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arrays are always accessed (in effect) via pointers, array accesses are typically not checked against the underlying array size, although some compilers may provide bounds checking as an option. Array bounds violations are therefore possible and can lead to various repercussions, including illegal memory accesses, corruption of data, buffer overruns, and run-time exceptions.
C does not have a special provision for declaring multi-dimensional arrays, but rather relies on recursion within the type system to declare arrays of arrays, which effectively accomplishes the same thing. The index values of the resulting "multi-dimensional array" can be thought of as increasing in row-major order. Multi-dimensional arrays are commonly used in numerical algorithms (mainly from applied linear algebra) to store matrices. The structure of the C array is well suited to this particular task. However, in early versions of C the bounds of the array must be known fixed values or else explicitly passed t
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be known fixed values or else explicitly passed to any subroutine that requires them, and dynamically sized arrays of arrays cannot be accessed using double indexing. (A workaround for this was to allocate the array with an additional "row vector" of pointers to the columns.) C99 introduced "variable-length arrays" which address this issue.
The following example using modern C (C99 or later) shows allocation of a two-dimensional array on the heap and the use of multi-dimensional array indexing for accesses (which can use bounds-checking on many C compilers):
And here is a similar implementation using C99's Auto VLA feature:
=== Array–pointer interchangeability ===
The subscript notation x[i] (where x designates a pointer) is syntactic sugar for *(x+i). Taking advantage of the compiler's knowledge of the pointer type, the address that x + i points to is not the base address (pointed to by x) incremented by i bytes, but rather is defined to be the base address incremented by i multip
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ned to be the base address incremented by i multiplied by the size of an element that x points to. Thus, x[i] designates the i+1th element of the array.
Furthermore, in most expression contexts (a notable exception is as operand of sizeof), an expression of array type is automatically converted to a pointer to the array's first element. This implies that an array is never copied as a whole when named as an argument to a function, but rather only the address of its first element is passed. Therefore, although function calls in C use pass-by-value semantics, arrays are in effect passed by reference.
The total size of an array x can be determined by applying sizeof to an expression of array type. The size of an element can be determined by applying the operator sizeof to any dereferenced element of an array A, as in n = sizeof A[0]. Thus, the number of elements in a declared array A can be determined as sizeof A / sizeof A[0]. Note, that if only a pointer to the first element is availabl
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if only a pointer to the first element is available as it is often the case in C code because of the automatic conversion described above, the information about the full type of the array and its length are lost.
== Memory management ==
One of the most important functions of a programming language is to provide facilities for managing memory and the objects that are stored in memory. C provides three principal ways to allocate memory for objects:
Static memory allocation: space for the object is provided in the binary at compile-time; these objects have an extent (or lifetime) as long as the binary which contains them is loaded into memory.
Automatic memory allocation: temporary objects can be stored on the stack, and this space is automatically freed and reusable after the block in which they are declared is exited.
Dynamic memory allocation: blocks of memory of arbitrary size can be requested at run-time using library functions such as malloc from a region of memory called the hea
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h as malloc from a region of memory called the heap; these blocks persist until subsequently freed for reuse by calling the library function realloc or free.
These three approaches are appropriate in different situations and have various trade-offs. For example, static memory allocation has little allocation overhead, automatic allocation may involve slightly more overhead, and dynamic memory allocation can potentially have a great deal of overhead for both allocation and deallocation. The persistent nature of static objects is useful for maintaining state information across function calls, automatic allocation is easy to use but stack space is typically much more limited and transient than either static memory or heap space, and dynamic memory allocation allows convenient allocation of objects whose size is known only at run-time. Most C programs make extensive use of all three.
Where possible, automatic or static allocation is usually simplest because the storage is managed by the co
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simplest because the storage is managed by the compiler, freeing the programmer of the potentially error-prone chore of manually allocating and releasing storage. However, many data structures can change in size at runtime, and since static allocations (and automatic allocations before C99) must have a fixed size at compile-time, there are many situations in which dynamic allocation is necessary. Prior to the C99 standard, variable-sized arrays were a common example of this. (See the article on C dynamic memory allocation for an example of dynamically allocated arrays.) Unlike automatic allocation, which can fail at run time with uncontrolled consequences, the dynamic allocation functions return an indication (in the form of a null pointer value) when the required storage cannot be allocated. (Static allocation that is too large is usually detected by the linker or loader, before the program can even begin execution.)
Unless otherwise specified, static objects contain zero or null p
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e specified, static objects contain zero or null pointer values upon program startup. Automatically and dynamically allocated objects are initialized only if an initial value is explicitly specified; otherwise they initially have indeterminate values (typically, whatever bit pattern happens to be present in the storage, which might not even represent a valid value for that type). If the program attempts to access an uninitialized value, the results are undefined. Many modern compilers try to detect and warn about this problem, but both false positives and false negatives can occur.
Heap memory allocation has to be synchronized with its actual usage in any program to be reused as much as possible. For example, if the only pointer to a heap memory allocation goes out of scope or has its value overwritten before it is deallocated explicitly, then that memory cannot be recovered for later reuse and is essentially lost to the program, a phenomenon known as a memory leak. Conversely, it is p
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omenon known as a memory leak. Conversely, it is possible for memory to be freed, but is referenced subsequently, leading to unpredictable results. Typically, the failure symptoms appear in a portion of the program unrelated to the code that causes the error, making it difficult to diagnose the failure. Such issues are ameliorated in languages with automatic garbage collection.
== Libraries ==
The C programming language uses libraries as its primary method of extension. In C, a library is a set of functions contained within a single "archive" file. Each library typically has a header file, which contains the prototypes of the functions contained within the library that may be used by a program, and declarations of special data types and macro symbols used with these functions. For a program to use a library, it must include the library's header file, and the library must be linked with the program, which in many cases requires compiler flags (e.g., -lm, shorthand for "link the math
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er flags (e.g., -lm, shorthand for "link the math library").
The most common C library is the C standard library, which is specified by the ISO and ANSI C standards and comes with every C implementation (implementations which target limited environments such as embedded systems may provide only a subset of the standard library). This library supports stream input and output, memory allocation, mathematics, character strings, and time values. Several separate standard headers (for example, stdio.h) specify the interfaces for these and other standard library facilities.
Another common set of C library functions are those used by applications specifically targeted for Unix and Unix-like systems, especially functions which provide an interface to the kernel. These functions are detailed in various standards such as POSIX and the Single UNIX Specification.
Since many programs have been written in C, there are a wide variety of other libraries available. Libraries are often written in C bec
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es available. Libraries are often written in C because C compilers generate efficient object code; programmers then create interfaces to the library so that the routines can be used from higher-level languages like Java, Perl, and Python.
=== File handling and streams ===
File input and output (I/O) is not part of the C language itself but instead is handled by libraries (such as the C standard library) and their associated header files (e.g. stdio.h). File handling is generally implemented through high-level I/O which works through streams. A stream is from this perspective a data flow that is independent of devices, while a file is a concrete device. The high-level I/O is done through the association of a stream to a file. In the C standard library, a buffer (a memory area or queue) is temporarily used to store data before it is sent to the final destination. This reduces the time spent waiting for slower devices, for example a hard drive or solid-state drive. Low-level I/O functio
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drive or solid-state drive. Low-level I/O functions are not part of the standard C library but are generally part of "bare metal" programming (programming that is independent of any operating system such as most embedded programming). With few exceptions, implementations include low-level I/O.
== Language tools ==
A number of tools have been developed to help C programmers find and fix statements with undefined behavior or possibly erroneous expressions, with greater rigor than that provided by the compiler.
Automated source code checking and auditing tools exist, such as Lint. A common practice is to use Lint to detect questionable code when a program is first written. Once a program passes Lint, it is then compiled using the C compiler. Also, many compilers can optionally warn about syntactically valid constructs that are likely to actually be errors. MISRA C is a proprietary set of guidelines to avoid such questionable code, developed for embedded systems.
There are also compile
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loped for embedded systems.
There are also compilers, libraries, and operating system level mechanisms for performing actions that are not a standard part of C, such as bounds checking for arrays, detection of buffer overflow, serialization, dynamic memory tracking, and automatic garbage collection.
Memory management checking tools like Purify or Valgrind and linking with libraries containing special versions of the memory allocation functions can help uncover runtime errors in memory usage.
== Uses ==
=== Rationale for use in systems programming ===
C is widely used for systems programming in implementing operating systems and embedded system applications. This is for several reasons:
The C language permits platform hardware and memory to be accessed with pointers and type punning, so system-specific features (e.g. Control/Status Registers, I/O registers) can be configured and used with code written in C – it allows fullest control of the platform it is running on.
The code gene
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ol of the platform it is running on.
The code generated after compilation does not demand many system features, and can be invoked from some boot code in a straightforward manner – it is simple to execute.
The C language statements and expressions typically map well on to sequences of instructions for the target processor, and consequently there is a low run-time demand on system resources – it is fast to execute.
With its rich set of operators, the C language can use many of the features of target CPUs. Where a particular CPU has more esoteric instructions, a language variant can be constructed with perhaps intrinsic functions to exploit those instructions – it can use practically all the target CPU's features.
The language makes it easy to overlay structures onto blocks of binary data, allowing the data to be comprehended, navigated and modified – it can write data structures, even file systems.
The language supports a rich set of operators, including bit manipulation, for integer a
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erators, including bit manipulation, for integer arithmetic and logic, and perhaps different sizes of floating point numbers – it can process appropriately-structured data effectively.
C is a fairly small language, with only a handful of statements, and without too many features that generate extensive target code – it is comprehensible.
C has direct control over memory allocation and deallocation, which gives reasonable efficiency and predictable timing to memory-handling operations, without any concerns for sporadic stop-the-world garbage collection events – it has predictable performance.
C permits the use and implementation of different memory allocation schemes, including a typical malloc and free; a more sophisticated mechanism with arenas; or a version for an OS kernel that may suit DMA, use within interrupt handlers, or integrated with the virtual memory system.
Depending on the linker and environment, C code can also call libraries written in assembly language, and may be call
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ries written in assembly language, and may be called from assembly language – it interoperates well with other lower-level code.
C and its calling conventions and linker structures are commonly used in conjunction with other high-level languages, with calls both to C and from C supported – it interoperates well with other high-level code.
C has a very mature and broad ecosystem, including libraries, frameworks, open source compilers, debuggers and utilities, and is the de facto standard. It is likely the drivers already exist in C, or that there is a similar CPU architecture as a back-end of a C compiler, so there is reduced incentive to choose another language.
=== Used for computationally-intensive libraries ===
C enables programmers to create efficient implementations of algorithms and data structures, because the layer of abstraction from hardware is thin, and its overhead is low, an important criterion for computationally intensive programs. For example, the GNU Multiple Precisi
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ve programs. For example, the GNU Multiple Precision Arithmetic Library, the GNU Scientific Library, Mathematica, and MATLAB are completely or partially written in C. Many languages support calling library functions in C, for example, the Python-based framework NumPy uses C for the high-performance and hardware-interacting aspects.
=== Games ===
Computer games are often built from a combination of languages. C has featured significantly, especially for those games attempting to obtain best performance from computer platforms. Examples include Doom from 1993.
=== C as an intermediate language ===
C is sometimes used as an intermediate language by implementations of other languages. This approach may be used for portability or convenience; by using C as an intermediate language, additional machine-specific code generators are not necessary. C has some features, such as line-number preprocessor directives and optional superfluous commas at the end of initializer lists, that support
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mmas at the end of initializer lists, that support compilation of generated code. However, some of C's shortcomings have prompted the development of other C-based languages specifically designed for use as intermediate languages, such as C--. Also, contemporary major compilers GCC and LLVM both feature an intermediate representation that is not C, and those compilers support front ends for many languages including C.
=== Other languages written in C ===
A consequence of C's wide availability and efficiency is that compilers, libraries and interpreters of other programming languages are often implemented in C. For example, the reference implementations of Python, Perl, Ruby, and PHP are written in C.
=== Once used for web development ===
Historically, C was sometimes used for web development using the Common Gateway Interface (CGI) as a "gateway" for information between the web application, the server, and the browser. C may have been chosen over interpreted languages because of its
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n chosen over interpreted languages because of its speed, stability, and near-universal availability. It is no longer common practice for web development to be done in C, and many other web development languages are popular. Applications where C-based web development continues include the HTTP configuration pages on routers, IoT devices and similar, although even here some projects have parts in higher-level languages e.g. the use of Lua within OpenWRT.
=== Web servers ===
The two most popular web servers, Apache HTTP Server and Nginx, are both written in C. These web servers interact with the operating system, listen on TCP ports for HTTP requests, and then serve up static web content, or cause the execution of other languages handling to 'render' content such as PHP, which is itself primarily written in C. C's close-to-the-metal approach allows for the construction of these high-performance software systems.
=== End-user applications ===
C has also been widely used to impleme
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cations ===
C has also been widely used to implement end-user applications. However, such applications can also be written in newer, higher-level languages.
== Limitations ==
the power of assembly language and the convenience of ... assembly language
While C has been popular, influential and hugely successful, it has drawbacks, including:
The standard dynamic memory handling with malloc and free is error prone. Improper use can lead to memory leaks and dangling pointers.
The use of pointers and the direct manipulation of memory means corruption of memory is possible, perhaps due to programmer error, or insufficient checking of bad data.
There is some type checking, but it does not apply to areas like variadic functions, and the type checking can be trivially or inadvertently circumvented. It is weakly typed.
Since the code generated by the compiler contains few checks itself, there is a burden on the programmer to consider all possible outcomes, to protect against buffer overruns,
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ble outcomes, to protect against buffer overruns, array bounds checking, stack overflows, memory exhaustion, and consider race conditions, thread isolation, etc.
The use of pointers and the run-time manipulation of these means there may be two ways to access the same data (aliasing), which is not determinable at compile time. This means that some optimisations that may be available to other languages are not possible in C. FORTRAN is considered faster.
Some of the standard library functions, e.g. scanf or strncat, can lead to buffer overruns.
There is limited standardisation in support for low-level variants in generated code, for example: different function calling conventions and ABI; different structure packing conventions; different byte ordering within larger integers (including endianness). In many language implementations, some of these options may be handled with the preprocessor directive #pragma, and some with additional keywords e.g. use __cdecl calling convention. The d
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ywords e.g. use __cdecl calling convention. The directive and options are not consistently supported.
String handling using the standard library is code-intensive, with explicit memory management required.
The language does not directly support object orientation, introspection, run-time expression evaluation, generics, etc.
There are few guards against inappropriate use of language features, which may lead to unmaintainable code. In particular, the C preprocessor can hide troubling effects such as double evaluation and worse. This facility for tricky code has been celebrated with competitions such as the International Obfuscated C Code Contest and the Underhanded C Contest.
C lacks standard support for exception handling and only offers return codes for error checking. The setjmp and longjmp standard library functions have been used to implement a try-catch mechanism via macros.
For some purposes, restricted styles of C have been adopted, e.g. MISRA C or CERT C, in an attempt to re
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opted, e.g. MISRA C or CERT C, in an attempt to reduce the opportunity for bugs. Databases such as CWE attempt to count the ways C etc. has vulnerabilities, along with recommendations for mitigation.
There are tools that can mitigate against some of the drawbacks. Contemporary C compilers include checks which may generate warnings to help identify many potential bugs.
== Related languages ==
C has both directly and indirectly influenced many later languages such as C++ and Java. The most pervasive influence has been syntactical; all of the languages mentioned combine the statement and (more or less recognizably) expression syntax of C with type systems, data models or large-scale program structures that differ from those of C, sometimes radically.
Several C or near-C interpreters exist, including Ch and CINT, which can also be used for scripting.
When object-oriented programming languages became popular, C++ and Objective-C were two different extensions of C that provided object-o
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o different extensions of C that provided object-oriented capabilities. Both languages were originally implemented as source-to-source compilers; source code was translated into C, and then compiled with a C compiler.
The C++ programming language (originally named "C with Classes") was devised by Bjarne Stroustrup as an approach to providing object-oriented functionality with a C-like syntax. C++ adds greater typing strength, scoping, and other tools useful in object-oriented programming, and permits generic programming via templates. Nearly a superset of C, C++ now supports most of C, with a few exceptions.
Objective-C was originally a very "thin" layer on top of C, and remains a strict superset of C that permits object-oriented programming using a hybrid dynamic/static typing paradigm. Objective-C derives its syntax from both C and Smalltalk: syntax that involves preprocessing, expressions, function declarations, and function calls is inherited from C, while the syntax for object-ori
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inherited from C, while the syntax for object-oriented features was originally taken from Smalltalk.
In addition to C++ and Objective-C, Ch, Cilk, and Unified Parallel C are nearly supersets of C.
== See also ==
Compatibility of C and C++
Comparison of Pascal and C
Comparison of programming languages
International Obfuscated C Code Contest
List of C-family programming languages
List of C compilers
== Notes ==
== References ==
== Sources ==
Ritchie, Dennis M. (March 1993). "The Development of the C Language". ACM SIGPLAN Notices. 28 (3). ACM: 201–208. doi:10.1145/155360.155580.
By courtesy of the author, also at Ritchie, Dennis M. "Chistory". www.bell-labs.com. Retrieved March 29, 2022.
Ritchie, Dennis M. (1993). "The Development of the C Language". The Second ACM SIGPLAN Conference on History of Programming Languages (HOPL-II). ACM. pp. 201–208. doi:10.1145/154766.155580. ISBN 0-89791-570-4. Archived from the original on April 11, 2019. Retrieved November 4, 2014.
Kernighan,
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11, 2019. Retrieved November 4, 2014.
Kernighan, Brian W.; Ritchie, Dennis M. (1988). The C Programming Language (2nd ed.). Prentice Hall. ISBN 0-13-110362-8.
== Further reading ==
Plauger, P.J. (1992). The Standard C Library (1 ed.). Prentice Hall. ISBN 978-0131315099. (source)
Banahan, M.; Brady, D.; Doran, M. (1991). The C Book: Featuring the ANSI C Standard (2 ed.). Addison-Wesley. ISBN 978-0201544336. (free)
Feuer, Alan R. (1985). The C Puzzle Book (1 ed.). Prentice Hall. ISBN 0131099345.
Harbison, Samuel; Steele, Guy Jr. (2002). C: A Reference Manual (5 ed.). Pearson. ISBN 978-0130895929. (archive)
King, K.N. (2008). C Programming: A Modern Approach (2 ed.). W. W. Norton. ISBN 978-0393979503. (archive)
Griffiths, David; Griffiths, Dawn (2012). Head First C (1 ed.). O'Reilly. ISBN 978-1449399917.
Perry, Greg; Miller, Dean (2013). C Programming: Absolute Beginner's Guide (3 ed.). Que. ISBN 978-0789751980.
Deitel, Paul; Deitel, Harvey (2015). C: How to Program (8 ed.). Pearson. I
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rvey (2015). C: How to Program (8 ed.). Pearson. ISBN 978-0133976892.
Gustedt, Jens (2019). Modern C (2 ed.). Manning. ISBN 978-1617295812. (free)
== External links ==
ISO C Working Group official website
ISO/IEC 9899, publicly available official C documents, including the C99 Rationale
"C99 with Technical corrigenda TC1, TC2, and TC3 included" (PDF). Archived (PDF) from the original on October 25, 2007. (3.61 MB)
comp.lang.c Frequently Asked Questions
A History of C, by Dennis Ritchie
C Library Reference and Examples
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In computer programming, a programming idiom, code idiom or simply idiom is a code fragment having a semantic role which recurs frequently across software projects. It often expresses a special feature of a recurring construct in one or more programming languages, frameworks or libraries. This definition is rooted in the linguistic definition of "idiom".
The idiom can be seen by developers as an action on a programming concept underlying a pattern in code, which is represented in implementation by contiguous or scattered code snippets. Generally speaking, a programming idiom's semantic role is a natural language expression of a simple task, algorithm, or data structure that is not a built-in feature in the programming language being used, or, conversely, the use of an unusual or notable feature that is built into a programming language.
Knowing the idioms associated with a programming language and how to use them is an important part of gaining fluency in that language. It also helps
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f gaining fluency in that language. It also helps to transfer knowledge in the form of analogies from one language or framework to another. Such idiomatic knowledge is widely used in crowdsourced repositories to help developers overcome programming barriers.
Mapping code idioms to idiosyncrasies can be a helpful way to navigate the tradeoffs between generalization and specificity. By identifying common patterns and idioms, developers can create mental models and schemata that help them quickly understand and navigate new code. Furthermore, by mapping these idioms to idiosyncrasies and specific use cases, developers can ensure that they are applying the correct approach and not overgeneralizing it.
One way to do this is by creating a reference or documentation that maps common idioms to specific use cases, highlighting where they may need to be adapted or modified to fit a particular project or development team. This can help ensure that developers are working with a shared understand
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at developers are working with a shared understanding of best practices and can make informed decisions about when to use established idioms and when to adapt them to fit their specific needs.
A common misconception is to use the adverbial or adjectival form of the term as "using a programming language in a typical way", which really refers to a idiosyncrasy. An idiom implies the semantics of some code in a programming language has similarities to other languages or frameworks. For example, an idiosyncratic way to manage dynamic memory in C would be to use the C standard library functions malloc and free, whereas idiomatic refers to manual memory management as recurring semantic role that can be achieved with code fragments malloc in C, or pointer = new type [number_of_elements] in C++. In both cases, the semantics of the code are intelligible to developers familiar with C or C++, once the idiomatic or idiosyncratic rationale is exposed to them. However, while idiomatic rationale is of
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to them. However, while idiomatic rationale is often general to the programming domain, idiosyncratic rationale is frequently tied to specific API terminology.
== Examples ==
=== Printing Hello World ===
One of the most common starting points to learn to program or notice the syntax differences between a known language and a new one.
It has several implementations, among them the code fragments for C++:
For Java:
=== Inserting an element in an array ===
This idiom helps developers understand how to manipulate collections in a given language, particularly inserting an element x at a position i in a list s and moving the elements to its right.
Code fragments:
For Python:
For JavaScript:
For Perl:
== See also ==
Algorithmic skeleton
Embedded SQL
Idiom
== References ==
== External links ==
programming-idioms.org shows short idioms implementations in most mainstream languages.
C++ programming idioms from Wikibooks.
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In computer science, reflective programming or reflection is the ability of a process to examine, introspect, and modify its own structure and behavior.
== Historical background ==
The earliest computers were programmed in their native assembly languages, which were inherently reflective, as these original architectures could be programmed by defining instructions as data and using self-modifying code. As the bulk of programming moved to higher-level compiled languages such as ALGOL, COBOL, Fortran, Pascal, and C, this reflective ability largely disappeared until new programming languages with reflection built into their type systems appeared.
Brian Cantwell Smith's 1982 doctoral dissertation introduced the notion of computational reflection in procedural programming languages and the notion of the meta-circular interpreter as a component of 3-Lisp.
== Uses ==
Reflection helps programmers make generic software libraries to display data, process different formats of data, perform se
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ata, process different formats of data, perform serialization and deserialization of data for communication, or do bundling and unbundling of data for containers or bursts of communication.
Effective use of reflection almost always requires a plan: A design framework, encoding description, object library, a map of a database or entity relations.
Reflection makes a language more suited to network-oriented code. For example, it assists languages such as Java to operate well in networks by enabling libraries for serialization, bundling and varying data formats. Languages without reflection such as C are required to use auxiliary compilers for tasks like Abstract Syntax Notation to produce code for serialization and bundling.
Reflection can be used for observing and modifying program execution at runtime. A reflection-oriented program component can monitor the execution of an enclosure of code and can modify itself according to a desired goal of that enclosure. This is typically accomplish
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al of that enclosure. This is typically accomplished by dynamically assigning program code at runtime.
In object-oriented programming languages such as Java, reflection allows inspection of classes, interfaces, fields and methods at runtime without knowing the names of the interfaces, fields, methods at compile time. It also allows instantiation of new objects and invocation of methods.
Reflection is often used as part of software testing, such as for the runtime creation/instantiation of mock objects.
Reflection is also a key strategy for metaprogramming.
In some object-oriented programming languages such as C# and Java, reflection can be used to bypass member accessibility rules. For C#-properties this can be achieved by writing directly onto the (usually invisible) backing field of a non-public property. It is also possible to find non-public methods of classes and types and manually invoke them. This works for project-internal files as well as external libraries such as .NET's asse
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as well as external libraries such as .NET's assemblies and Java's archives.
== Implementation ==
A language that supports reflection provides a number of features available at runtime that would otherwise be difficult to accomplish in a lower-level language. Some of these features are the abilities to:
Discover and modify source-code constructions (such as code blocks, classes, methods, protocols, etc.) as first-class objects at runtime.
Convert a string matching the symbolic name of a class or function into a reference to or invocation of that class or function.
Evaluate a string as if it were a source-code statement at runtime.
Create a new interpreter for the language's bytecode to give a new meaning or purpose for a programming construct.
These features can be implemented in different ways. In MOO, reflection forms a natural part of everyday programming idiom. When verbs (methods) are called, various variables such as verb (the name of the verb being called) and this (the obj
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e name of the verb being called) and this (the object on which the verb is called) are populated to give the context of the call. Security is typically managed by accessing the caller stack programmatically: Since callers() is a list of the methods by which the current verb was eventually called, performing tests on callers()[0] (the command invoked by the original user) allows the verb to protect itself against unauthorised use.
Compiled languages rely on their runtime system to provide information about the source code. A compiled Objective-C executable, for example, records the names of all methods in a block of the executable, providing a table to correspond these with the underlying methods (or selectors for these methods) compiled into the program. In a compiled language that supports runtime creation of functions, such as Common Lisp, the runtime environment must include a compiler or an interpreter.
Reflection can be implemented for languages without built-in reflection by usin
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for languages without built-in reflection by using a program transformation system to define automated source-code changes.
== Security considerations ==
Reflection may allow a user to create unexpected control flow paths through an application, potentially bypassing security measures. This may be exploited by attackers. Historical vulnerabilities in Java caused by unsafe reflection allowed code retrieved from potentially untrusted remote machines to break out of the Java sandbox security mechanism. A large scale study of 120 Java vulnerabilities in 2013 concluded that unsafe reflection is the most common vulnerability in Java, though not the most exploited.
== Examples ==
The following code snippets create an instance foo of class Foo and invoke its method PrintHello. For each programming language, normal and reflection-based call sequences are shown.
=== Common Lisp ===
The following is an example in Common Lisp using the Common Lisp Object System:
=== C# ===
The following i
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n Lisp Object System:
=== C# ===
The following is an example in C#:
=== Delphi, Object Pascal ===
This Delphi and Object Pascal example assumes that a TFoo class has been declared in a unit called Unit1:
=== eC ===
The following is an example in eC:
=== Go ===
The following is an example in Go:
=== Java ===
The following is an example in Java:
=== JavaScript ===
The following is an example in JavaScript:
=== Julia ===
The following is an example in Julia:
=== Objective-C ===
The following is an example in Objective-C, implying either the OpenStep or Foundation Kit framework is used:
=== Perl ===
The following is an example in Perl:
=== PHP ===
The following is an example in PHP:
=== Python ===
The following is an example in Python:
=== R ===
The following is an example in R:
=== Ruby ===
The following is an example in Ruby:
=== Xojo ===
The following is an example using Xojo:
== See also ==
List of reflective programming languages and platforms
Mirror (pro
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ve programming languages and platforms
Mirror (programming)
Programming paradigms
Self-hosting (compilers)
Self-modifying code
Type introspection
typeof
== References ==
=== Citations ===
=== Sources ===
== Further reading ==
Ira R. Forman and Nate Forman, Java Reflection in Action (2005), ISBN 1-932394-18-4
Ira R. Forman and Scott Danforth, Putting Metaclasses to Work (1999), ISBN 0-201-43305-2
== External links ==
Reflection in logic, functional and object-oriented programming: a short comparative study
An Introduction to Reflection-Oriented Programming
Brian Foote's pages on Reflection in Smalltalk
Java Reflection API Tutorial from Oracle
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In computer science, extensible programming is a style of computer programming that focuses on mechanisms to extend the programming language, compiler, and runtime system (environment). Extensible programming languages, supporting this style of programming, were an active area of work in the 1960s, but the movement was marginalized in the 1970s. Extensible programming has become a topic of renewed interest in the 21st century.
== Historical movement ==
The first paper usually associated with the extensible programming language movement is M. Douglas McIlroy's 1960 paper on macros for high-level programming languages. Another early description of the principle of extensibility occurs in Brooker and Morris's 1960 paper on the compiler-compiler. The peak of the movement was marked by two academic symposia, in 1969 and 1971. By 1975, a survey article on the movement by Thomas A. Standish was essentially a post mortem. The Forth was an exception, but it went essentially unnoticed.
=== C
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eption, but it went essentially unnoticed.
=== Character of the historical movement ===
As typically envisioned, an extensible language consisted of a base language providing elementary computing facilities, and a metalanguage able to modify the base language. A program then consisted of metalanguage modifications and code in the modified base language.
The most prominent language-extension technique used in the movement was macro definition. Grammar modification was also closely associated with the movement, resulting in the eventual development of adaptive grammar formalisms. The Lisp language community remained separate from the extensible language community, apparently because, as one researcher observed,
any programming language in which programs and data are essentially interchangeable can be regarded as an extendible [sic] language. ... this can be seen very easily from the fact that Lisp has been used as an extendible language for years.
At the 1969 conference, Simula was pr
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e for years.
At the 1969 conference, Simula was presented as an extensible language.
Standish described three classes of language extension, which he named paraphrase, orthophrase, and metaphrase (otherwise paraphrase and metaphrase being translation terms).
Paraphrase defines a facility by showing how to exchange it for something formerly defined (or to be defined). As examples, he mentions macro definitions, ordinary procedure definitions, grammatical extensions, data definitions, operator definitions, and control structure extensions.
Orthophrase adds features to a language that could not be achieved using the base language, such as adding an input/output (I/O) system to a base language formerly with no I/O primitives. Extensions must be understood as orthophrase relative to some given base language, since a feature not defined in terms of the base language must be defined in terms of some other language. This corresponds to the modern notion of plug-ins.
Metaphrase modifies the in
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ern notion of plug-ins.
Metaphrase modifies the interpretation rules used for pre-existing expressions. This corresponds to the modern notion of reflective programming (reflection).
=== Death of the historical movement ===
Standish attributed the failure of the extensibility movement to the difficulty of programming successive extensions. A programmer might build a first shell of macros around a base language. Then, if a second shell of macros is built around that, any subsequent programmer must be intimately familiar with both the base language, and the first shell. A third shell would require familiarity with the base and both the first and second shells, and so on. Shielding a programmer from lower-level details is the intent of the abstraction movement that supplanted the extensibility movement.
Despite the earlier presentation of Simula as extensible, by 1975, Standish's survey does not seem in practice to have included the newer abstraction-based technologies (though he used a
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abstraction-based technologies (though he used a very general definition of extensibility that technically could have included them). A 1978 history of programming abstraction from the invention of the computer until then, made no mention of macros, and gave no hint that the extensible languages movement had ever occurred. Macros were tentatively admitted into the abstraction movement by the late 1980s (perhaps due to the advent of hygienic macros), by being granted the pseudonym syntactic abstractions.
== Modern movement ==
In the modern sense, a system that supports extensible programming will provide all of the features described below.
=== Extensible syntax ===
This simply means that the source language(s) to be compiled must not be closed, fixed, or static. It must be possible to add new keywords, concepts, and structures to the source language(s). Languages which allow the addition of constructs with user defined syntax include Coq, Racket, Camlp4, OpenC++, Seed7, Red, Rebo
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ude Coq, Racket, Camlp4, OpenC++, Seed7, Red, Rebol, and Felix. While it is acceptable for some fundamental and intrinsic language features to be immutable, the system must not rely solely on those language features. It must be possible to add new ones.
=== Extensible compiler ===
In extensible programming, a compiler is not a monolithic program that converts source code input into binary executable output. The compiler itself must be extensible to the point that it is really a collection of plugins that assist with the translation of source language input into anything. For example, an extensible compiler will support the generation of object code, code documentation, re-formatted source code, or any other desired output. The architecture of the compiler must permit its users to "get inside" the compilation process and provide alternative processing tasks at every reasonable step in the compilation process.
For just the task of translating source code into something that can be exec
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lating source code into something that can be executed on a computer, an extensible compiler should:
use a plug-in or component architecture for nearly every aspect of its function
determine which language or language variant is being compiled and locate the appropriate plug-in to recognize and validate that language
use formal language specifications to syntactically and structurally validate arbitrary source languages
assist with the semantic validation of arbitrary source languages by invoking an appropriate validation plug-in
allow users to select from different kinds of code generators so that the resulting executable can be targeted for different processors, operating systems, virtual machines, or other execution environment.
provide facilities for error generation and extensions to it
allow new kinds of nodes in the abstract syntax tree (AST),
allow new values in nodes of the AST,
allow new kinds of edges between nodes,
support the transformation of the input AST, or portions t
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the transformation of the input AST, or portions thereof, by some external "pass"
support the translation of the input AST, or portions thereof, into another form by some external "pass"
assist with the flow of information between internal and external passes as they both transform and translate the AST into new ASTs or other representations
=== Extensible runtime ===
At runtime, extensible programming systems must permit languages to extend the set of operations that it permits. For example, if the system uses a byte-code interpreter, it must allow new byte-code values to be defined. As with extensible syntax, it is acceptable for there to be some (smallish) set of fundamental or intrinsic operations that are immutable. However, it must be possible to overload or augment those intrinsic operations so that new or additional behavior can be supported.
=== Content separated from form ===
Extensible programming systems should regard programs as data to be processed. Those programs sho
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ograms as data to be processed. Those programs should be completely devoid of any kind of formatting information. The visual display and editing of programs to users should be a translation function, supported by the extensible compiler, that translates the program data into forms more suitable for viewing or editing. Naturally, this should be a two-way translation. This is important because it must be possible to easily process extensible programs in a variety of ways. It is unacceptable for the only uses of source language input to be editing, viewing and translation to machine code. The arbitrary processing of programs is facilitated by de-coupling the source input from specifications of how it should be processed (formatted, stored, displayed, edited, etc.).
=== Source language debugging support ===
Extensible programming systems must support the debugging of programs using the constructs of the original source language regardless of the extensions or transformation the program h
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of the extensions or transformation the program has undergone in order to make it executable. Most notably, it cannot be assumed that the only way to display runtime data is in structures or arrays. The debugger, or more correctly 'program inspector', must permit the display of runtime data in forms suitable to the source language. For example, if the language supports a data structure for a business process or work flow, it must be possible for the debugger to display that data structure as a fishbone chart or other form provided by a plugin.
== Examples ==
Camlp4
Felix
Nemerle
Seed7
Rebol
Red
Ruby (metaprogramming)
IMP
OpenC++
XL
XML
Forth
Lisp
Racket
Scheme
Lua
PL/I
Smalltalk
== See also ==
Adaptive grammar
Concept programming
Dialecting
Grammar-oriented programming
Language-oriented programming
Homoiconicity
== References ==
== External links ==
=== General ===
Greg Wilson's Article in ACM Queue
Slashdot Discussion
Modern Extensible Languages Archived 2011-06-12 at the W
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Extensible Languages Archived 2011-06-12 at the Wayback Machine – A paper from Daniel Zingaro
=== Tools ===
MetaL – an extensible programming compiler engine implementation
XPS – eXtensible Programming System (in development)
MPS – JetBrains Metaprogramming system
=== Languages with extensible syntax ===
OpenZz
xtc – eXTensible C
English-script
Nemerle Macros
Boo Syntactic Macros
Stanford University Intermediate Format compiler
Seed7 – The extensible programming language
Katahdin – a language with syntax and semantics that are mutable at runtime
π – a language with extensible syntax, implemented using an Earley parser
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In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that map values to other values, rather than a sequence of imperative statements which update the running state of the program.
In functional programming, functions are treated as first-class citizens, meaning that they can be bound to names (including local identifiers), passed as arguments, and returned from other functions, just as any other data type can. This allows programs to be written in a declarative and composable style, where small functions are combined in a modular manner.
Functional programming is sometimes treated as synonymous with purely functional programming, a subset of functional programming that treats all functions as deterministic mathematical functions, or pure functions. When a pure function is called with some given arguments, i
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re function is called with some given arguments, it will always return the same result, and cannot be affected by any mutable state or other side effects. This is in contrast with impure procedures, common in imperative programming, which can have side effects (such as modifying the program's state or taking input from a user). Proponents of purely functional programming claim that by restricting side effects, programs can have fewer bugs, be easier to debug and test, and be more suited to formal verification.
Functional programming has its roots in academia, evolving from the lambda calculus, a formal system of computation based only on functions. Functional programming has historically been less popular than imperative programming, but many functional languages are seeing use today in industry and education, including Common Lisp, Scheme, Clojure, Wolfram Language, Racket, Erlang, Elixir, OCaml, Haskell, and F#. Lean is a functional programming language commonly used for verifying ma
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rogramming language commonly used for verifying mathematical theorems. Functional programming is also key to some languages that have found success in specific domains, like JavaScript in the Web, R in statistics, J, K and Q in financial analysis, and XQuery/XSLT for XML. Domain-specific declarative languages like SQL and Lex/Yacc use some elements of functional programming, such as not allowing mutable values. In addition, many other programming languages support programming in a functional style or have implemented features from functional programming, such as C++11, C#, Kotlin, Perl, PHP, Python, Go, Rust, Raku, Scala, and Java (since Java 8).
== History ==
The lambda calculus, developed in the 1930s by Alonzo Church, is a formal system of computation built from function application. In 1937 Alan Turing proved that the lambda calculus and Turing machines are equivalent models of computation, showing that the lambda calculus is Turing complete. Lambda calculus forms the basis of al
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ng complete. Lambda calculus forms the basis of all functional programming languages. An equivalent theoretical formulation, combinatory logic, was developed by Moses Schönfinkel and Haskell Curry in the 1920s and 1930s.
Church later developed a weaker system, the simply typed lambda calculus, which extended the lambda calculus by assigning a data type to all terms. This forms the basis for statically typed functional programming.
The first high-level functional programming language, Lisp, was developed in the late 1950s for the IBM 700/7000 series of scientific computers by John McCarthy while at Massachusetts Institute of Technology (MIT). Lisp functions were defined using Church's lambda notation, extended with a label construct to allow recursive functions. Lisp first introduced many paradigmatic features of functional programming, though early Lisps were multi-paradigm languages, and incorporated support for numerous programming styles as new paradigms evolved. Later dialects, suc
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yles as new paradigms evolved. Later dialects, such as Scheme and Clojure, and offshoots such as Dylan and Julia, sought to simplify and rationalise Lisp around a cleanly functional core, while Common Lisp was designed to preserve and update the paradigmatic features of the numerous older dialects it replaced.
Information Processing Language (IPL), 1956, is sometimes cited as the first computer-based functional programming language. It is an assembly-style language for manipulating lists of symbols. It does have a notion of generator, which amounts to a function that accepts a function as an argument, and, since it is an assembly-level language, code can be data, so IPL can be regarded as having higher-order functions. However, it relies heavily on the mutating list structure and similar imperative features.
Kenneth E. Iverson developed APL in the early 1960s, described in his 1962 book A Programming Language (ISBN 9780471430148). APL was the primary influence on John Backus's FP. In t
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as the primary influence on John Backus's FP. In the early 1990s, Iverson and Roger Hui created J. In the mid-1990s, Arthur Whitney, who had previously worked with Iverson, created K, which is used commercially in financial industries along with its descendant Q.
In the mid-1960s, Peter Landin invented SECD machine, the first abstract machine for a functional programming language, described a correspondence between ALGOL 60 and the lambda calculus, and proposed the ISWIM programming language.
John Backus presented FP in his 1977 Turing Award lecture "Can Programming Be Liberated From the von Neumann Style? A Functional Style and its Algebra of Programs". He defines functional programs as being built up in a hierarchical way by means of "combining forms" that allow an "algebra of programs"; in modern language, this means that functional programs follow the principle of compositionality. Backus's paper popularized research into functional programming, though it emphasized function-level
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programming, though it emphasized function-level programming rather than the lambda-calculus style now associated with functional programming.
The 1973 language ML was created by Robin Milner at the University of Edinburgh, and David Turner developed the language SASL at the University of St Andrews. Also in Edinburgh in the 1970s, Burstall and Darlington developed the functional language NPL. NPL was based on Kleene Recursion Equations and was first introduced in their work on program transformation. Burstall, MacQueen and Sannella then incorporated the polymorphic type checking from ML to produce the language Hope. ML eventually developed into several dialects, the most common of which are now OCaml and Standard ML.
In the 1970s, Guy L. Steele and Gerald Jay Sussman developed Scheme, as described in the Lambda Papers and the 1985 textbook Structure and Interpretation of Computer Programs. Scheme was the first dialect of lisp to use lexical scoping and to require tail-call optimizati
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exical scoping and to require tail-call optimization, features that encourage functional programming.
In the 1980s, Per Martin-Löf developed intuitionistic type theory (also called constructive type theory), which associated functional programs with constructive proofs expressed as dependent types. This led to new approaches to interactive theorem proving and has influenced the development of subsequent functional programming languages.
The lazy functional language, Miranda, developed by David Turner, initially appeared in 1985 and had a strong influence on Haskell. With Miranda being proprietary, Haskell began with a consensus in 1987 to form an open standard for functional programming research; implementation releases have been ongoing as of 1990.
More recently it has found use in niches such as parametric CAD in the OpenSCAD language built on the CGAL framework, although its restriction on reassigning values (all values are treated as constants) has led to confusion among users who
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s constants) has led to confusion among users who are unfamiliar with functional programming as a concept.
Functional programming continues to be used in commercial settings.
== Concepts ==
A number of concepts and paradigms are specific to functional programming, and generally foreign to imperative programming (including object-oriented programming). However, programming languages often cater to several programming paradigms, so programmers using "mostly imperative" languages may have utilized some of these concepts.
=== First-class and higher-order functions ===
Higher-order functions are functions that can either take other functions as arguments or return them as results. In calculus, an example of a higher-order function is the differential operator
d
/
d
x
{\displaystyle d/dx}
, which returns the derivative of a function
f
{\displaystyle f}
.
High
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f
{\displaystyle f}
.
Higher-order functions are closely related to first-class functions in that higher-order functions and first-class functions both allow functions as arguments and results of other functions. The distinction between the two is subtle: "higher-order" describes a mathematical concept of functions that operate on other functions, while "first-class" is a computer science term for programming language entities that have no restriction on their use (thus first-class functions can appear anywhere in the program that other first-class entities like numbers can, including as arguments to other functions and as their return values).
Higher-order functions enable partial application or currying, a technique that applies a function to its arguments one at a time, with each application returning a new function that accepts the next argument. This lets a programmer succinctly express, for example, the successor function as the addition operator partially
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cessor function as the addition operator partially applied to the natural number one.
=== Pure functions ===
Pure functions (or expressions) have no side effects (memory or I/O). This means that pure functions have several useful properties, many of which can be used to optimize the code:
If the result of a pure expression is not used, it can be removed without affecting other expressions.
If a pure function is called with arguments that cause no side-effects, the result is constant with respect to that argument list (sometimes called referential transparency or idempotence), i.e., calling the pure function again with the same arguments returns the same result. (This can enable caching optimizations such as memoization.)
If there is no data dependency between two pure expressions, their order can be reversed, or they can be performed in parallel and they cannot interfere with one another (in other terms, the evaluation of any pure expression is thread-safe).
If the entire language
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xpression is thread-safe).
If the entire language does not allow side-effects, then any evaluation strategy can be used; this gives the compiler freedom to reorder or combine the evaluation of expressions in a program (for example, using deforestation).
While most compilers for imperative programming languages detect pure functions and perform common-subexpression elimination for pure function calls, they cannot always do this for pre-compiled libraries, which generally do not expose this information, thus preventing optimizations that involve those external functions. Some compilers, such as gcc, add extra keywords for a programmer to explicitly mark external functions as pure, to enable such optimizations. Fortran 95 also lets functions be designated pure. C++11 added constexpr keyword with similar semantics.
=== Recursion ===
Iteration (looping) in functional languages is usually accomplished via recursion. Recursive functions invoke themselves, letting an operation be repeated u
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oke themselves, letting an operation be repeated until it reaches the base case. In general, recursion requires maintaining a stack, which consumes space in a linear amount to the depth of recursion. This could make recursion prohibitively expensive to use instead of imperative loops. However, a special form of recursion known as tail recursion can be recognized and optimized by a compiler into the same code used to implement iteration in imperative languages. Tail recursion optimization can be implemented by transforming the program into continuation passing style during compiling, among other approaches.
The Scheme language standard requires implementations to support proper tail recursion, meaning they must allow an unbounded number of active tail calls. Proper tail recursion is not simply an optimization; it is a language feature that assures users that they can use recursion to express a loop and doing so would be safe-for-space. Moreover, contrary to its name, it accounts for all
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oreover, contrary to its name, it accounts for all tail calls, not just tail recursion. While proper tail recursion is usually implemented by turning code into imperative loops, implementations might implement it in other ways. For example, Chicken intentionally maintains a stack and lets the stack overflow. However, when this happens, its garbage collector will claim space back, allowing an unbounded number of active tail calls even though it does not turn tail recursion into a loop.
Common patterns of recursion can be abstracted away using higher-order functions, with catamorphisms and anamorphisms (or "folds" and "unfolds") being the most obvious examples. Such recursion schemes play a role analogous to built-in control structures such as loops in imperative languages.
Most general purpose functional programming languages allow unrestricted recursion and are Turing complete, which makes the halting problem undecidable, can cause unsoundness of equational reasoning, and generally req
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undness of equational reasoning, and generally requires the introduction of inconsistency into the logic expressed by the language's type system. Some special purpose languages such as Coq allow only well-founded recursion and are strongly normalizing (nonterminating computations can be expressed only with infinite streams of values called codata). As a consequence, these languages fail to be Turing complete and expressing certain functions in them is impossible, but they can still express a wide class of interesting computations while avoiding the problems introduced by unrestricted recursion. Functional programming limited to well-founded recursion with a few other constraints is called total functional programming.
=== Strict versus non-strict evaluation ===
Functional languages can be categorized by whether they use strict (eager) or non-strict (lazy) evaluation, concepts that refer to how function arguments are processed when an expression is being evaluated. The technical diff
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expression is being evaluated. The technical difference is in the denotational semantics of expressions containing failing or divergent computations. Under strict evaluation, the evaluation of any term containing a failing subterm fails. For example, the expression:
print length([2+1, 3*2, 1/0, 5-4])
fails under strict evaluation because of the division by zero in the third element of the list. Under lazy evaluation, the length function returns the value 4 (i.e., the number of items in the list), since evaluating it does not attempt to evaluate the terms making up the list. In brief, strict evaluation always fully evaluates function arguments before invoking the function. Lazy evaluation does not evaluate function arguments unless their values are required to evaluate the function call itself.
The usual implementation strategy for lazy evaluation in functional languages is graph reduction. Lazy evaluation is used by default in several pure functional languages, including Miranda, Cl
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l pure functional languages, including Miranda, Clean, and Haskell.
Hughes 1984 argues for lazy evaluation as a mechanism for improving program modularity through separation of concerns, by easing independent implementation of producers and consumers of data streams. Launchbury 1993 describes some difficulties that lazy evaluation introduces, particularly in analyzing a program's storage requirements, and proposes an operational semantics to aid in such analysis. Harper 2009 proposes including both strict and lazy evaluation in the same language, using the language's type system to distinguish them.
=== Type systems ===
Especially since the development of Hindley–Milner type inference in the 1970s, functional programming languages have tended to use typed lambda calculus, rejecting all invalid programs at compilation time and risking false positive errors, as opposed to the untyped lambda calculus, that accepts all valid programs at compilation time and risks false negative errors,
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compilation time and risks false negative errors, used in Lisp and its variants (such as Scheme), as they reject all invalid programs at runtime when the information is enough to not reject valid programs. The use of algebraic data types makes manipulation of complex data structures convenient; the presence of strong compile-time type checking makes programs more reliable in absence of other reliability techniques like test-driven development, while type inference frees the programmer from the need to manually declare types to the compiler in most cases.
Some research-oriented functional languages such as Coq, Agda, Cayenne, and Epigram are based on intuitionistic type theory, which lets types depend on terms. Such types are called dependent types. These type systems do not have decidable type inference and are difficult to understand and program with. But dependent types can express arbitrary propositions in higher-order logic. Through the Curry–Howard isomorphism, then, well-typed p
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h the Curry–Howard isomorphism, then, well-typed programs in these languages become a means of writing formal mathematical proofs from which a compiler can generate certified code. While these languages are mainly of interest in academic research (including in formalized mathematics), they have begun to be used in engineering as well. Compcert is a compiler for a subset of the language C that is written in Coq and formally verified.
A limited form of dependent types called generalized algebraic data types (GADT's) can be implemented in a way that provides some of the benefits of dependently typed programming while avoiding most of its inconvenience. GADT's are available in the Glasgow Haskell Compiler, in OCaml and in Scala, and have been proposed as additions to other languages including Java and C#.
=== Referential transparency ===
Functional programs do not have assignment statements, that is, the value of a variable in a functional program never changes once defined. This elimin
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al program never changes once defined. This eliminates any chances of side effects because any variable can be replaced with its actual value at any point of execution. So, functional programs are referentially transparent.
Consider C assignment statement x=x*10, this changes the value assigned to the variable x. Let us say that the initial value of x was 1, then two consecutive evaluations of the variable x yields 10 and 100 respectively. Clearly, replacing x=x*10 with either 10 or 100 gives a program a different meaning, and so the expression is not referentially transparent. In fact, assignment statements are never referentially transparent.
Now, consider another function such as int plusone(int x) {return x+1;} is transparent, as it does not implicitly change the input x and thus has no such side effects.
Functional programs exclusively use this type of function and are therefore referentially transparent.
=== Data structures ===
Purely functional data structures are often repre
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Purely functional data structures are often represented in a different way to their imperative counterparts. For example, the array with constant access and update times is a basic component of most imperative languages, and many imperative data-structures, such as the hash table and binary heap, are based on arrays. Arrays can be replaced by maps or random access lists, which admit purely functional implementation, but have logarithmic access and update times. Purely functional data structures have persistence, a property of keeping previous versions of the data structure unmodified. In Clojure, persistent data structures are used as functional alternatives to their imperative counterparts. Persistent vectors, for example, use trees for partial updating. Calling the insert method will result in some but not all nodes being created.
== Comparison to imperative programming ==
Functional programming is very different from imperative programming. The most significant differences stem
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rogramming. The most significant differences stem from the fact that functional programming avoids side effects, which are used in imperative programming to implement state and I/O. Pure functional programming completely prevents side-effects and provides referential transparency.
Higher-order functions are rarely used in older imperative programming. A traditional imperative program might use a loop to traverse and modify a list. A functional program, on the other hand, would probably use a higher-order "map" function that takes a function and a list, generating and returning a new list by applying the function to each list item.
=== Imperative vs. functional programming ===
The following two examples (written in JavaScript) achieve the same effect: they multiply all even numbers in an array by 10 and add them all, storing the final sum in the variable result.
Traditional imperative loop:
Functional programming with higher-order functions:
Sometimes the abstractions offered by fun
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ctions:
Sometimes the abstractions offered by functional programming might lead to development of more robust code that avoids certain issues that might arise when building upon large amount of complex, imperative code, such as off-by-one errors (see Greenspun's tenth rule).
=== Simulating state ===
There are tasks (for example, maintaining a bank account balance) that often seem most naturally implemented with state. Pure functional programming performs these tasks, and I/O tasks such as accepting user input and printing to the screen, in a different way.
The pure functional programming language Haskell implements them using monads, derived from category theory. Monads offer a way to abstract certain types of computational patterns, including (but not limited to) modeling of computations with mutable state (and other side effects such as I/O) in an imperative manner without losing purity. While existing monads may be easy to apply in a program, given appropriate templates and examp
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n a program, given appropriate templates and examples, many students find them difficult to understand conceptually, e.g., when asked to define new monads (which is sometimes needed for certain types of libraries).
Functional languages also simulate states by passing around immutable states. This can be done by making a function accept the state as one of its parameters, and return a new state together with the result, leaving the old state unchanged.
Impure functional languages usually include a more direct method of managing mutable state. Clojure, for example, uses managed references that can be updated by applying pure functions to the current state. This kind of approach enables mutability while still promoting the use of pure functions as the preferred way to express computations.
Alternative methods such as Hoare logic and uniqueness have been developed to track side effects in programs. Some modern research languages use effect systems to make the presence of side effects expli
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systems to make the presence of side effects explicit.
=== Efficiency issues ===
Functional programming languages are typically less efficient in their use of CPU and memory than imperative languages such as C and Pascal. This is related to the fact that some mutable data structures like arrays have a very straightforward implementation using present hardware. Flat arrays may be accessed very efficiently with deeply pipelined CPUs, prefetched efficiently through caches (with no complex pointer chasing), or handled with SIMD instructions. It is also not easy to create their equally efficient general-purpose immutable counterparts. For purely functional languages, the worst-case slowdown is logarithmic in the number of memory cells used, because mutable memory can be represented by a purely functional data structure with logarithmic access time (such as a balanced tree). However, such slowdowns are not universal. For programs that perform intensive numerical computations, functional
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form intensive numerical computations, functional languages such as OCaml and Clean are only slightly slower than C according to The Computer Language Benchmarks Game. For programs that handle large matrices and multidimensional databases, array functional languages (such as J and K) were designed with speed optimizations.
Immutability of data can in many cases lead to execution efficiency by allowing the compiler to make assumptions that are unsafe in an imperative language, thus increasing opportunities for inline expansion. Even if the involved copying that may seem implicit when dealing with persistent immutable data structures might seem computationally costly, some functional programming languages, like Clojure solve this issue by implementing mechanisms for safe memory sharing between formally immutable data. Rust distinguishes itself by its approach to data immutability which involves immutable references and a concept called lifetimes.
Immutable data with separation of identit
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fetimes.
Immutable data with separation of identity and state and shared-nothing schemes can also potentially be more well-suited for concurrent and parallel programming by the virtue of reducing or eliminating the risk of certain concurrency hazards, since concurrent operations are usually atomic and this allows eliminating the need for locks. This is how for example java.util.concurrent classes are implemented, where some of them are immutable variants of the corresponding classes that are not suitable for concurrent use. Functional programming languages often have a concurrency model that instead of shared state and synchronization, leverages message passing mechanisms (such as the actor model, where each actor is a container for state, behavior, child actors and a message queue). This approach is common in Erlang/Elixir or Akka.
Lazy evaluation may also speed up the program, even asymptotically, whereas it may slow it down at most by a constant factor (however, it may introduce mem
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y a constant factor (however, it may introduce memory leaks if used improperly). Launchbury 1993 discusses theoretical issues related to memory leaks from lazy evaluation, and O'Sullivan et al. 2008 give some practical advice for analyzing and fixing them.
However, the most general implementations of lazy evaluation making extensive use of dereferenced code and data perform poorly on modern processors with deep pipelines and multi-level caches (where a cache miss may cost hundreds of cycles) .
==== Abstraction cost ====
Some functional programming languages might not optimize abstractions such as higher order functions like "map" or "filter" as efficiently as the underlying imperative operations. Consider, as an example, the following two ways to check if 5 is an even number in Clojure:
When benchmarked using the Criterium tool on a Ryzen 7900X GNU/Linux PC in a Leiningen REPL 2.11.2, running on Java VM version 22 and Clojure version 1.11.1, the first implementation, which is implem
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1.11.1, the first implementation, which is implemented as:
has the mean execution time of 4.76 ms, while the second one, in which .equals is a direct invocation of the underlying Java method, has a mean execution time of 2.8 μs – roughly 1700 times faster. Part of that can be attributed to the type checking and exception handling involved in the implementation of even?. For instance the lo library for Go, which implements various higher-order functions common in functional programming languages using generics. In a benchmark provided by the library's author, calling map is 4% slower than an equivalent for loop and has the same allocation profile, which can be attributed to various compiler optimizations, such as inlining.
One distinguishing feature of Rust are zero-cost abstractions. This means that using them imposes no additional runtime overhead. This is achieved thanks to the compiler using loop unrolling, where each iteration of a loop, be it imperative or using iterators, is co
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a loop, be it imperative or using iterators, is converted into a standalone Assembly instruction, without the overhead of the loop controlling code. If an iterative operation writes to an array, the resulting array's elements will be stored in specific CPU registers, allowing for constant-time access at runtime.
=== Functional programming in non-functional languages ===
It is possible to use a functional style of programming in languages that are not traditionally considered functional languages. For example, both D and Fortran 95 explicitly support pure functions.
JavaScript, Lua, Python and Go had first class functions from their inception. Python had support for "lambda", "map", "reduce", and "filter" in 1994, as well as closures in Python 2.2, though Python 3 relegated "reduce" to the functools standard library module. First-class functions have been introduced into other mainstream languages such as Perl 5.0 in 1994, PHP 5.3, Visual Basic 9, C# 3.0, C++11, and Kotlin.
In Perl,
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sual Basic 9, C# 3.0, C++11, and Kotlin.
In Perl, lambda, map, reduce, filter, and closures are fully supported and frequently used. The book Higher-Order Perl, released in 2005, was written to provide an expansive guide on using Perl for functional programming.
In PHP, anonymous classes, closures and lambdas are fully supported. Libraries and language extensions for immutable data structures are being developed to aid programming in the functional style.
In Java, anonymous classes can sometimes be used to simulate closures; however, anonymous classes are not always proper replacements to closures because they have more limited capabilities. Java 8 supports lambda expressions as a replacement for some anonymous classes.
In C#, anonymous classes are not necessary, because closures and lambdas are fully supported. Libraries and language extensions for immutable data structures are being developed to aid programming in the functional style in C#.
Many object-oriented design patterns are
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e in C#.
Many object-oriented design patterns are expressible in functional programming terms: for example, the strategy pattern simply dictates use of a higher-order function, and the visitor pattern roughly corresponds to a catamorphism, or fold.
Similarly, the idea of immutable data from functional programming is often included in imperative programming languages, for example the tuple in Python, which is an immutable array, and Object.freeze() in JavaScript.
== Comparison to logic programming ==
Logic programming can be viewed as a generalisation of functional programming, in which functions are a special case of relations.
For example, the function, mother(X) = Y, (every X has only one mother Y) can be represented by the relation mother(X, Y). Whereas functions have a strict input-output pattern of arguments, relations can be queried with any pattern of inputs and outputs. Consider the following logic program:
The program can be queried, like a functional program, to generate m
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queried, like a functional program, to generate mothers from children:
But it can also be queried backwards, to generate children:
It can even be used to generate all instances of the mother relation:
Compared with relational syntax, functional syntax is a more compact notation for nested functions. For example, the definition of maternal grandmother in functional syntax can be written in the nested form:
The same definition in relational notation needs to be written in the unnested form:
Here :- means if and , means and.
However, the difference between the two representations is simply syntactic. In Ciao Prolog, relations can be nested, like functions in functional programming:
Ciao transforms the function-like notation into relational form and executes the resulting logic program using the standard Prolog execution strategy.
== Applications ==
=== Text editors ===
Emacs, a highly extensible text editor family uses its own Lisp dialect for writing plugins. The original aut
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Lisp dialect for writing plugins. The original author of the most popular Emacs implementation, GNU Emacs and Emacs Lisp, Richard Stallman considers Lisp one of his favorite programming languages.
Helix, since version 24.03 supports previewing AST as S-expressions, which are also the core feature of the Lisp programming language family.
=== Spreadsheets ===
Spreadsheets can be considered a form of pure, zeroth-order, strict-evaluation functional programming system. However, spreadsheets generally lack higher-order functions as well as code reuse, and in some implementations, also lack recursion. Several extensions have been developed for spreadsheet programs to enable higher-order and reusable functions, but so far remain primarily academic in nature.
=== Microservices ===
Due to their composability, functional programming paradigms can be suitable for microservices-based architectures.
=== Academia ===
Functional programming is an active area of research in the field of programm
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n active area of research in the field of programming language theory. There are several peer-reviewed publication venues focusing on functional programming, including the International Conference on Functional Programming, the Journal of Functional Programming, and the Symposium on Trends in Functional Programming.
=== Industry ===
Functional programming has been employed in a wide range of industrial applications. For example, Erlang, which was developed by the Swedish company Ericsson in the late 1980s, was originally used to implement fault-tolerant telecommunications systems, but has since become popular for building a range of applications at companies such as Nortel, Facebook, Électricité de France and WhatsApp. Scheme, a dialect of Lisp, was used as the basis for several applications on early Apple Macintosh computers and has been applied to problems such as training-simulation software and telescope control. OCaml, which was introduced in the mid-1990s, has seen commercial u
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introduced in the mid-1990s, has seen commercial use in areas such as financial analysis, driver verification, industrial robot programming and static analysis of embedded software. Haskell, though initially intended as a research language, has also been applied in areas such as aerospace systems, hardware design and web programming.
Other functional programming languages that have seen use in industry include Scala, F#, Wolfram Language, Lisp, Standard ML and Clojure. Scala has been widely used in Data science, while ClojureScript, Elm or PureScript are some of the functional frontend programming languages used in production. Elixir's Phoenix framework is also used by some relatively popular commercial projects, such as Font Awesome or Allegro (one of the biggest e-commerce platforms in Poland)'s classified ads platform Allegro Lokalnie.
Functional "platforms" have been popular in finance for risk analytics (particularly with large investment banks). Risk factors are coded as function
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estment banks). Risk factors are coded as functions that form interdependent graphs (categories) to measure correlations in market shifts, similar in manner to Gröbner basis optimizations but also for regulatory frameworks such as Comprehensive Capital Analysis and Review. Given the use of OCaml and Caml variations in finance, these systems are sometimes considered related to a categorical abstract machine. Functional programming is heavily influenced by category theory.
=== Education ===
Many universities teach functional programming. Some treat it as an introductory programming concept while others first teach imperative programming methods.
Outside of computer science, functional programming is used to teach problem-solving, algebraic and geometric concepts. It has also been used to teach classical mechanics, as in the book Structure and Interpretation of Classical Mechanics.
In particular, Scheme has been a relatively popular choice for teaching programming for years.
== See al
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ce for teaching programming for years.
== See also ==
Eager evaluation
Functional reactive programming
Inductive functional programming
List of functional programming languages
List of functional programming topics
Nested function
Purely functional programming
== Notes and references ==
== Further reading ==
Abelson, Hal; Sussman, Gerald Jay (1985). Structure and Interpretation of Computer Programs. MIT Press. Bibcode:1985sicp.book.....A.
Cousineau, Guy and Michel Mauny. The Functional Approach to Programming. Cambridge, UK: Cambridge University Press, 1998.
Curry, Haskell Brooks and Feys, Robert and Craig, William. Combinatory Logic. Volume I. North-Holland Publishing Company, Amsterdam, 1958.
Curry, Haskell B.; Hindley, J. Roger; Seldin, Jonathan P. (1972). Combinatory Logic. Vol. II. Amsterdam: North Holland. ISBN 978-0-7204-2208-5.
Dominus, Mark Jason. Higher-Order Perl. Morgan Kaufmann. 2005.
Felleisen, Matthias; Findler, Robert; Flatt, Matthew; Krishnamurthi, Shriram (2018
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bert; Flatt, Matthew; Krishnamurthi, Shriram (2018). How to Design Programs. MIT Press.
Graham, Paul. ANSI Common LISP. Englewood Cliffs, New Jersey: Prentice Hall, 1996.
MacLennan, Bruce J. Functional Programming: Practice and Theory. Addison-Wesley, 1990.
Michaelson, Greg (10 April 2013). An Introduction to Functional Programming Through Lambda Calculus. Courier Corporation. ISBN 978-0-486-28029-5.
O'Sullivan, Brian; Stewart, Don; Goerzen, John (2008). Real World Haskell. O'Reilly.
Pratt, Terrence W. and Marvin Victor Zelkowitz. Programming Languages: Design and Implementation. 3rd ed. Englewood Cliffs, New Jersey: Prentice Hall, 1996.
Salus, Peter H. Functional and Logic Programming Languages. Vol. 4 of Handbook of Programming Languages. Indianapolis, Indiana: Macmillan Technical Publishing, 1998.
Thompson, Simon. Haskell: The Craft of Functional Programming. Harlow, England: Addison-Wesley Longman Limited, 1996.
== External links ==
Ford, Neal. "Functional thinking". Retrieved 2
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==
Ford, Neal. "Functional thinking". Retrieved 2021-11-10.
Akhmechet, Slava (2006-06-19). "defmacro – Functional Programming For The Rest of Us". Retrieved 2013-02-24. An introduction
Functional programming in Python (by David Mertz): part 1, part 2, part 3
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Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.
Depending on the programming language used, there are several kinds of inductive programming. Inductive functional programming, which uses functional programming languages such as Lisp or Haskell, and most especially inductive logic programming, which uses logic programming languages such as Prolog and other logical representations such as description logics, have been more prominent, but other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming.
== Definition ==
Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possi
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hms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases.
Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language.
In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration o
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cification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete.
In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples.
The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming,
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been used or suggested in inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action languages, agent languages and many types of imperative languages.
== History ==
Research on the inductive synthesis of recursive functional programs started in the early 1970s and was brought onto firm theoretical foundations with the seminal THESIS system of Summers and work of Biermann.
These approaches were split into two phases: first, input-output examples are transformed into non-recursive programs (traces) using a small set of basic operators; second, regularities in the traces are searched for and used to fold them into a recursive program. The main results until the mid-1980s are surveyed by Smith. Due to limited progress with respect to the range of programs that could be synthesized, research activities decreased significantly in the next decade.
The advent of logic programming brought
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t decade.
The advent of logic programming brought a new elan but also a new direction in the early 1980s, especially due to the MIS system of Shapiro eventually spawning the new field of inductive logic programming (ILP). The early works of Plotkin, and his "relative least general generalization (rlgg)", had an enormous impact in inductive logic programming. Most of ILP work addresses a wider class of problems, as the focus is not only on recursive logic programs but on machine learning of symbolic hypotheses from logical representations. However, there were some encouraging results on learning recursive Prolog programs such as quicksort from examples together with suitable background knowledge, for example with GOLEM. But again, after initial success, the community got disappointed by limited progress about the induction of recursive programs with ILP less and less focusing on recursive programs and leaning more and more towards a machine learning setting with applications in relation
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ine learning setting with applications in relational data mining and knowledge discovery.
In parallel to work in ILP, Koza proposed genetic programming in the early 1990s as a generate-and-test based approach to learning programs. The idea of genetic programming was further developed into the inductive programming system ADATE and the systematic-search-based system MagicHaskeller. Here again, functional programs are learned from sets of positive examples together with an output evaluation (fitness) function which specifies the desired input/output behavior of the program to be learned.
The early work in grammar induction (also known as grammatical inference) is related to inductive programming, as rewriting systems or logic programs can be used to represent production rules. In fact, early works in inductive inference considered grammar induction and Lisp program inference as basically the same problem. The results in terms of learnability were related to classical concepts, such as id
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ity were related to classical concepts, such as identification-in-the-limit, as introduced in the seminal work of Gold. More recently, the language learning problem was addressed by the inductive programming community.
In the recent years, the classical approaches have been resumed and advanced with great success. Therefore, the synthesis problem has been reformulated on the background of constructor-based term rewriting systems taking into account modern techniques of functional programming, as well as moderate use of search-based strategies and usage of background knowledge as well as automatic invention of subprograms. Many new and successful applications have recently appeared beyond program synthesis, most especially in the area of data manipulation, programming by example and cognitive modelling (see below).
Other ideas have also been explored with the common characteristic of using declarative languages for the representation of hypotheses. For instance, the use of higher-order
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hypotheses. For instance, the use of higher-order features, schemes or structured distances have been advocated for a better handling of recursive data types and structures; abstraction has also been explored as a more powerful approach to cumulative learning and function invention.
One powerful paradigm that has been recently used for the representation of hypotheses in inductive programming (generally in the form of generative models) is probabilistic programming (and related paradigms, such as stochastic logic programs and Bayesian logic programming).
== Application areas ==
The first workshop on Approaches and Applications of Inductive Programming (AAIP) held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end us
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routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations".
Since then, these and many other areas have shown to be successful application niches for inductive programming, such as end-user programming, the related areas of programming by example and programming by demonstration, and intelligent tutoring systems.
Other areas where inductive inference has been recently applied are knowledge acquisition, artificial general intelligence, reinforcement learning and theory evaluation, and cognitive science in general. There may also be prospective applications in intelligent agents, games, robotics, personalisation, ambient intelligence and human interfaces.
== See also ==
Evolutionary programming
Inductive reasoning
Test-driven development
== Referen
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