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Metaprogramming

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This article is about the computer programming technique. For the management technique, see Charles Simonyi.


Metaprogramming is a computer programming technique in which computer programs possess the capability to treat other programs as data. This enables a program to be designed in such a way that it can read, generate, analyze, or transform other programs, and even modify its own behavior during execution.[1][2] In many instances, this approach allows developers to reduce the amount of code required to implement a solution, thereby decreasing development time.[3] Furthermore, metaprogramming provides programs with the flexibility to adapt to new situations without the need for recompilation.

Metaprogramming techniques can be employed to shift computations from runtime to compile time, facilitating code generation through compile-time computations, and enabling the creation of self-modifying code. A key aspect of metaprogramming is a programming language's ability to serve as its own metalanguage, which enables reflective programming. This capability is known as reflection, and it is considered an important language feature that supports metaprogramming.[4]

Metaprogramming gained prominence during the 1970s and 1980s, particularly in conjunction with list-processing languages like Lisp. In the 1980s, specialized Lisp machine hardware garnered attention, enabling applications that could process and manipulate code. These machines were frequently utilized in artificial intelligence applications.


Approaches

Metaprogramming empowers developers to write programs within the generic programming paradigm. When a programming language itself is treated as a first-class data type—as seen in languages like Lisp, Prolog, SNOBOL, or Rebol—it is particularly beneficial, a concept referred to as homoiconicity. Generic programming leverages metaprogramming capabilities by allowing code to be written without explicit data type specifications, as data types can be supplied as parameters at the time of use.

Metaprogramming is typically achieved through one of three primary approaches.[5]

1. The first approach involves exposing the internals of the runtime system (engine) to the programming code through application programming interfaces (APIs), such as the .NET Common Intermediate Language (CIL) emitter.

2. The second approach is the dynamic execution of expressions that include programming commands, often composed from strings or other methods using arguments or context, as seen in languages like JavaScript.[6] In this context, "programs can write programs." Although both approaches may be employed within the same language, most programming languages tend to favor one approach over the other.

3. The third approach involves stepping outside the language altogether. General-purpose program transformation systems, such as compilers that accept language descriptions and perform arbitrary transformations, are direct implementations of metaprogramming. This method allows metaprogramming to be applied to almost any target language, regardless of whether that language has built-in metaprogramming capabilities. For instance, Scheme allows developers to overcome some limitations in C by utilizing constructs from Scheme to extend the functionality of C.[7]

Lisp is often considered the quintessential language for metaprogramming due to both its historical significance and the simplicity and power of its metaprogramming features. In Lisp, the unquote operator (typically a comma) introduces code that is evaluated at program definition time, rather than at runtime. This makes the metaprogramming language identical to the host programming language, allowing existing Lisp routines to be reused directly for metaprogramming if desired. Similar approaches have been implemented in other languages by incorporating an interpreter within the program to work directly with the program's data. Some high-level languages, such as RemObjects' Pascal Script for Object Pascal, have adopted this type of implementation.

Usages

Code generation

An elementary example of a metaprogram is this POSIX Shell script, illustrating the concept of generative programming: 

#!/bin/sh
# metaprogram
echo '#!/bin/sh' > program
for i in $(seq 992)
do
    echo "echo $i" >> program
done
chmod +x program

This script generates a new 993-line program that prints the numbers 1–992. While this example demonstrates how code can be written to produce more code, it is not the most efficient method for printing a sequence of numbers. However, the metaprogram can be written and executed in under a minute, resulting in the generation of over 1,000 lines of code within that timeframe.

A quine is a specific type of metaprogram that outputs its own source code. Quines are typically of recreational or theoretical interest.

Not all metaprogramming involves generative programming. In environments where programs can be modified at runtime or where incremental compiling is supported—such as in C#, Forth, Frink, Groovy, JavaScript, Lisp, Elixir, Lua, Nim, Perl, PHP, Python, Rebol, Ruby, Rust, R, SAS, Smalltalk, and Tcl—metaprogramming techniques can be employed without the need for generating new source code.[8]


One generative approach involves the use of domain-specific languages (DSLs). A common example of this is the use of DSLs in generative metaprogramming, such as the tools lex and yacc, which are employed to generate lexical analyzers and parsers. These tools allow users to describe a language using regular expressions and context-free grammars, while embedding the complex algorithms necessary to efficiently parse the language.

Code instrumentation

Another application of metaprogramming is in the instrumentation of programs to perform dynamic program analysis.[9]


Challenges

Some argue that there is a sharp learning curve to make complete use of metaprogramming features.[10] Since metaprogramming gives more flexibility and configurability at runtime, misuse or incorrect use of metaprogramming can result in unwarranted and unexpected errors that can be extremely difficult to debug to an average developer. It can introduce risks in the system and make it more vulnerable if not used with care. Some of the common problems, which can occur due to wrong use of metaprogramming are inability of the compiler to identify missing configuration parameters, invalid or incorrect data can result in unknown exception or different results.[11] Due to this, some believe[10] that only high-skilled developers should work on developing features which exercise metaprogramming in a language or platform and average developers must learn how to use these features as part of convention.

Uses in programming languages

Macro systems

Main article: Macro (computer science)

Macro assemblers

Main article: Macro assembler

The IBM/360 and derivatives had powerful macro assembler facilities that were often used to generate complete assembly language programs [citation needed] or sections of programs (for different operating systems for instance). Macros provided with CICS transaction processing system had assembler macros that generated COBOL statements as a pre-processing step.

Other assemblers, such as MASM, also support macros.

Metaclasses

Main article: Metaclass

Metaclasses are provided by the following programming languages:

Template metaprogramming

Main article: Template metaprogramming

Staged metaprogramming

Main article: Multi-stage programming

Dependent types

Main article: Dependent type

Use of dependent types allows proving that generated code is never invalid.[17] However, this approach is leading-edge and rarely found outside of research programming languages.

Implementations

The list of notable metaprogramming systems is maintained at List of program transformation systems.

See also

References

  1. ^ Sondergaard, Harald (2013). "Course on Program Analysis and Transformation". Retrieved 18 September 2014.
  2. ^ Czarnecki, Krzysztof; Eisenecker, Ulrich W. (2000). Generative Programming. ISBN 0-201-30977-7.
  3. ^ Walker, Max. "The Art of Metaprogramming in Java". New Circle. Retrieved 28 January 2014.
  4. ^ Krauss, Aaron. "Programming Concepts: Type Introspection and Reflection". The Societea. Retrieved 14 September 2014.
  5. ^ Joshi, Prateek (5 April 2014). "What Is Metaprogramming? – Part 2/2". Perpetual Enigma. Retrieved 14 August 2014.
  6. ^ For example, instance_eval in Ruby takes a string or an anonymous function. "Rdoc for Class: BasicObject (Ruby 1.9.3) - instance_eval". Retrieved 30 December 2011.
  7. ^ "Art of Metaprogramming". IBM.
  8. ^ Scott, Michael L. (2016). Programming Language Pragmatics. Morgan Kaufmann. ISBN 978-0124104099.
  9. ^ Zeller, Andreas (2005). "Dynamic Program Analysis". ACM SIGSOFT Software Engineering Notes. doi:10.1145/1082983.1083156.
  10. ^ a b Bicking, Ian. "The challenge of metaprogramming". IanBicking.org. Retrieved 21 September 2016.
  11. ^ Terry, Matt (21 August 2013). "Beware of Metaprogramming". Medium.com. Medium Corporation. Retrieved 21 August 2014.
  12. ^ Through Common Lisp Object System's "Meta Object Protocol"
  13. ^ "C++ Template Metaprogramming". aszt.inf.elte.hu. Retrieved 2022-07-23.
  14. ^ Lisp (programming language) "Self-evaluating forms and quoting", quasi-quote operator.
  15. ^ "LMS: Program Generation and Embedded Compilers in Scala". scala-lms.github.io. Retrieved 2017-12-06.
  16. ^ Rompf, Tiark; Odersky, Martin (June 2012). "Lightweight Modular Staging: A Pragmatic Approach to Runtime Code Generation and Compiled DSLs". Communications of the ACM. 55 (6): 121–130. doi:10.1145/2184319.2184345. ISSN 0001-0782. S2CID 52898203.
  17. ^ Chlipala, Adam (June 2010). "Ur: statically-typed metaprogramming with type-level record computation" (PDF). ACM SIGPLAN Notices. PLDI '10. 45 (6): 122–133. doi:10.1145/1809028.1806612. Retrieved 29 August 2012.
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