In computer science, automatic programming[1] is a type of computer programming in which some mechanism generates a computer program,[circular definition] to allow human programmers to write the code at a higher abstraction level.
There has been little agreement on the precise definition of automatic programming, mostly because its meaning has changed over time. David Parnas, tracing the history of "automatic programming" in published research, noted that in the 1940s it described automation of the manual process of punching paper tape. Later it referred to translation of high-level programming languages like Fortran and ALGOL. One of the earliest programs identifiable as a compiler is named Autocode. Parnas concluded that "automatic programming has always been a euphemism for programming in a higher-level language than was then available to the programmer."[2]
Program synthesis is one type of automatic programming where a procedure is created from scratch, based on mathematical requirements.
Origin
Mildred Koss, an early UNIVAC programmer, explains: "Writing machine code involved several tedious steps—breaking down a process into discrete instructions, assigning specific memory locations to all the commands, and managing the I/O buffers. After following these steps to implement mathematical routines, a sub-routine library, and sorting programs, our task was to look at the larger programming process. We needed to understand how we might reuse tested code and have the machine help in programming. As we programmed, we examined the process and tried to think of ways to abstract these steps to incorporate them into higher-level language. This led to the development of interpreters, assemblers, compilers, and generators—programs designed to operate on or produce other programs, that is, automatic programming."[3]
Generative programming
Generative programming and the related term metaprogramming[4] are concepts whereby programs can be written "to manufacture software components in an automated way"[5] just as automation has improved "production of traditional commodities such as garments, automobiles, chemicals, and electronics."[6][7]
The goal is to improve programmer productivity.[8] It is often related to code-reuse topics such as component-based software engineering.
Source-code generation
Source code generation is the process of generating source code based on a description of the problem[9] or an ontological model such as a template and is accomplished with a programming tool such as a template processor or an integrated development environment (IDE). These tools allow generating source code via any of various means.
Modern programming languages are well supported by tools like Json4Swift[10] (Swift) and Json2Kotlin[11] (Kotlin).
Programs that could generate COBOL code include:
- the DYL250/DYL260/DYL270/DYL280 series[12]
- Business Controls Corporation's SB-5
- Peat Marwick Mitchell's PMM2170 application-program-generator package
These application generators supported COBOL inserts and overrides.
A macro processor, such as the C preprocessor, which replaces patterns in source code according to relatively simple rules, is a simple form of source-code generator. Source-to-source code generation tools also exist.[13][14]
Large language models such as ChatGPT are capable of generating a program's source code from a description of the program given in a natural language.[15]
Many relational database management systems (RDBMS) provide a function that will export the content of the database as SQL data definition queries, which may then be executed to re-import the tables and their data, or migrate them to another RDBMS.
Some languages use annotations to generate source code and inject it. For example, this is done in Java and Kotlin using annotations, for example the Project Lombok library which runs at compile time with an annotation processor. There has been a C++ proposal to add token sequence injection using compile-time reflective programming (reflection).[16]
Low-code applications
A low-code development platform (LCDP) is software that provides an environment programmers use to create application software through graphical user interfaces and configuration instead of traditional computer programming.
Vibe coding
Vibe coding is software development assisted by artificial intelligence (AI) where the software developer describes a project or task in a prompt to a large language model (LLM) which generates source code automatically. Vibe coding may involve accepting AI-generated code without thorough review of the output, instead relying on results and follow-up prompts to guide changes.[17][18]
The term was coined in February 2025 by computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla. In March 2025, Merriam-Webster listed the term as a "slang & trending" expression.[19] It was also named the Collins English Dictionary Word of the Year for 2025.[20][21]
Advocates of vibe coding say it allows amateur programmers to produce software without the extensive training and skills required for software engineering.[22][23] Critics say there is a lack of accountability, maintainability, and an increased risk of introducing security vulnerabilities in the resulting software.[17][23]
See also
- Automatic bug fixing
- Automated machine learning
- Comparison of code generation tools
- Feature-oriented programming
- GitHub Copilot
- AI-assisted software development
- Language-oriented programming
- Modeling language
- Program transformation
- Semantic translation
- Vocabulary-based transformation
- Fourth-generation programming language
- Low-code development platform
- Emergent Coding
Notes
- ↑ Ricardo Aler Mur, "Automatic Inductive Programming Archived 2016-03-04 at the Wayback Machine", ICML 2006 Tutorial. June 2006.
- ↑ D. L. Parnas. "Software Aspects of Strategic Defense Systems." American Scientist. November 1985.
- ↑ Chun, Wendy. "On Software, or the Persistence of Visual Knowledge." Grey Room 18. Boston: 2004, pg. 30.
- ↑ "About Generative Programming".
Generative programming, as a subdomain of meta-programming, describes the practice of writing programs that generate other programs as part of their execution.
- ↑ P. Cointe (2005). "Towards Generative Programming". Unconventional Programming Paradigms. Lecture Notes in Computer Science. Vol. 3566. pp. 315–325. doi:10.1007/11527800_24. ISBN 978-3-540-27884-9.
Generative Programming (GP) is an attempt to manufacture software components in an automated way by developing programs that synthesize other programs.
- ↑ "Generative Programming: Concepts and Experiences (GPCE)".
- ↑ A conference of SIGPLAN on this topic is planned for November 2018. Earlier/1970s attempts in this area included Yacc and the related Lex programs.
- ↑ James Wilcox, "Paying Too Much for Custom Application Development", March 2011.
- ↑ "Application generator". PCMag.com.
Software that generates application programs from descriptions of the problem rather than by traditional programming. It is at a higher level and easier to use than a high-level programming language such as ...
- ↑ Staff. "Online JSON to Swift Models Generator". Json4Swift.com. Retrieved 19 April 2026.
- ↑ Staff. "Online JSON to Kotlin Data Class Generator". Json2Kotlin.com. Retrieved 19 April 2026.
- ↑ "DYL-280 Command Syntax" (PDF). Archived from the original (PDF) on 2018-07-30. Retrieved 2018-09-03.
- ↑ Noaje, Gabriel, Christophe Jaillet, and Michaël Krajecki. "Source-to-source code translator: OpenMP C to CUDA". High Performance Computing and Communications (HPCC), 2011 IEEE 13th International Conference on. IEEE, 2011.
- ↑ Quinlan, Dan, and Chunhua Liao. "The ROSE source-to-source compiler infrastructure". Cetus users and compiler infrastructure workshop, in conjunction with PACT. Vol. 2011. 2011.
- ↑ Tung, Liam (January 26, 2023). "ChatGPT can write code. Now researchers say it's good at fixing bugs, too". ZDNET. Archived from the original on February 3, 2023. Retrieved June 22, 2023.
- ↑ Andrei Alexandrescu, Barry Rezvin, Daveed Vandevoorde (16 July 2024). "Code Injection with Token Sequences". open-std.org. WG21.
{{cite web}}: CS1 maint: multiple names: authors list (link) - 1 2 Edwards, Benj (5 March 2025). "Will the future of software development run on vibes?". Ars Technica. Archived from the original on 6 March 2025. Retrieved 3 June 2025.
The technique, enabled by large language models (LLMs) from companies like OpenAI and Anthropic, has attracted attention for potentially lowering the barrier to entry for software creation. But questions remain about whether the approach can reliably produce code suitable for real-world applications, even as tools like Cursor Composer, GitHub Copilot, and Replit Agent make the process increasingly accessible to non-programmers.
- ↑ "What is 'vibe code'? Former Tesla AI director Andrej Karpathy defines a new era in AI-driven development". The Times of India. 2 March 2025. Archived from the original on 4 March 2025. Retrieved 3 June 2025.
Karpathy's "vibe coding" is a recognition of how sophisticated AI systems have evolved. In describing on X (formerly Twitter), he added that LLMs, like the Cursor Composer with Sonnet, are advancing to a degree that nearly eliminates the use of traditional coding mechanisms. Describing his own experience, Karpathy explained how he converses with AI tools almost in a passive manner—merely talking to them and having the AI handle the rest. This method eliminates manually typing code as well as keeping track of all the minute information in the program.
- ↑ "vibe coding". Slang & Trending. Merriam-Webster. 8 March 2025. Retrieved 2 June 2025.
Vibe coding (also written as vibecoding) (Vibecode/Vibecoder) is a recently-coined term for the practice of writing code, making web pages, or creating apps, by just telling an AI program what you want, and letting it create the product for you. In vibe coding the coder does not need to understand how or why the code works, and often will have to accept that a certain number of bugs and glitches will be present. The verb form of the word is vibe code.
- ↑ Garnsworthy, Jenny (6 November 2025). "Collins dictionary crowns AI buzz term Word of the Year". The Independent. Retrieved 6 November 2025.
- ↑ "'Vibe coding' named word of the year by Collins Dictionary". BBC News. 6 November 2025. Retrieved 6 November 2025.
- ↑ Lanz, Jose Antonio (23 March 2025). "Vibe Coding: How Devs and Laymen Alike Are Using AI to Create Apps and Games". Decrypt.co.
- 1 2 Chowdhury, Hasan; Mann, Jyoti (13 February 2025). "Silicon Valley's next act: bringing 'vibe coding' to the world". Business Insider. Archived from the original on 26 February 2025. Retrieved 3 March 2025.
References
- Czarnecki, Krzysztof; Eisenecker, Ulrich W. (1 May 2000). Generative Programming: Methods, Tools, and Applications. Association for Computing Machinery (ACM Press), Addison Wesley. ISBN 978-0-201-30977-5. Retrieved 19 April 2026.
- Fowler, Matthew (Spring 2009). "Code Generation for Dummies". Software Development Magazine. Methods & Tools. Retrieved 19 April 2026 – via Martinig & Associates.
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