Jump to content

Draft:AI code agents

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Laurelli7 (talk | contribs) at 19:49, 20 May 2025 (update reference). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Lead:

I created a new article about AI code agent.

AI Code Agent

AI code agents are artificial intelligence systems capable of automatically generating software code from natural language prompts or formal specifications. This field, also known as automatic programming or program synthesis, has evolved significantly from theoretical concepts in early computing to sophisticated practical applications powered by modern AI.

Historical Overview

Early Concepts (1940s–1960s)

The idea of machines writing programs dates back to Alan Turing's proposal of automatic computing engines in 1945. In the late 1950s, the FORTRAN compiler marked an early practical step by automating low-level assembly coding from higher-level languages (Backus, 1957)[1]. Concurrently, Alonzo Church's formulation of circuit synthesis ("Church's Problem," 1957) set theoretical foundations for program synthesis.

Foundational Developments (1960s–1980s)

The proofs-as-programs paradigm, pioneered by Cordell Green (1969)[2] and Zohar Manna and Richard Waldinger (1975)[3], established the deductive approach, where programs were systematically derived from logical specifications. Logic programming languages, notably Prolog (1972), enabled automatic execution from logical statements. Japan's Fifth Generation Computer Systems project (1982) significantly boosted international research and practical interest.

Expansion and Practical Systems (1980s–2000s)

Douglas Smith's KIDS system (1983) illustrated practical synthesis by integrating domain-specific knowledge to generate efficient algorithms. Genetic programming, introduced by John Koza (1992)[4], demonstrated evolutionary approaches, while Microsoft’s Flash Fill (Gulwani, 2011)[5] popularized programming-by-example (PBE) synthesis, significantly broadening accessibility.

Neural and Machine Learning Approaches (2010s)

The DeepCoder project (2017)[6] combined deep learning with traditional synthesis techniques, predicting code fragments needed to solve problems. Google's AutoML (2017) used reinforcement learning to automatically design machine learning architectures. The Bayou system (2018) leveraged neural sketch learning from GitHub repositories, marking practical neural-based code generation.

Modern Large Language Models (2020–Present)

OpenAI's Codex (2021)[7], powering GitHub Copilot, demonstrated large language models (LLMs) generating reliable, substantial code directly from natural language prompts. DeepMind's AlphaCode (2022)[8] further validated LLMs’ capability by achieving competitive human-level performance in algorithmic contests. Meta's Code Llama (2023)[9] provided powerful, open-source coding models, significantly democratizing AI-driven coding.

The future of AI code agents points toward greater autonomy, improved correctness via integration with formal verification, and more specialized domain-specific models. Significant challenges remain, including ensuring correctness, resolving intellectual property issues, and addressing ethical and security concerns associated with AI-generated code.

Key Milestones

  • 1945: Alan Turing introduces the concept of automatic programming.
  • 1957: Introduction of the FORTRAN compiler and Church's synthesis problem.
  • 1969: Cordell Green pioneers proofs-as-programs.
  • 1972: Development of Prolog.
  • 1983: KIDS synthesis system demonstrates practical application.
  • 1992: Genetic Programming proposed by John Koza.
  • 2011: Flash Fill by Microsoft brings programming-by-example mainstream.
  • 2017: DeepCoder and AutoML integrate machine learning with code synthesis.
  • 2021: OpenAI Codex and GitHub Copilot launch.
  • 2022: AlphaCode achieves competitive programming benchmarks.
  • 2023: Meta releases open-source Code Llama; AutoGPT introduces autonomous agents.

References

  1. ^ Backus, J. W.; Stern, H.; Ziller, I.; Hughes, R. A.; Nutt, R.; Beeber, R. J.; Best, S.; Goldberg, R.; Haibt, L. M.; Herrick, H. L.; Nelson, R. A.; Sayre, D.; Sheridan, P. B. (1957). "The FORTRAN automatic coding system". Papers presented at the February 26-28, 1957, western joint computer conference: Techniques for reliability on - IRE-AIEE-ACM '57 (Western). New York, New York, USA: ACM Press. doi:10.1145/1455567.1455599.
  2. ^ Green, Cordell (1969-03-01). Application of Theorem Proving to Problem Solving (Report). Fort Belvoir, VA: Defense Technical Information Center.
  3. ^ Manna, Zohar; Waldinger, Richard (1975-06). "Knowledge and reasoning in program synthesis". Artificial Intelligence. 6 (2): 175–208. doi:10.1016/0004-3702(75)90008-9. ISSN 0004-3702. {{cite journal}}: Check date values in: |date= (help)
  4. ^ Koza, JohnR. (1994-06). "Genetic programming as a means for programming computers by natural selection". Statistics and Computing. 4 (2). doi:10.1007/bf00175355. ISSN 0960-3174. {{cite journal}}: Check date values in: |date= (help)
  5. ^ Gulwani, Sumit (2011-01-26). "Automating string processing in spreadsheets using input-output examples". ACM SIGPLAN Notices. 46 (1): 317–330. doi:10.1145/1925844.1926423. ISSN 0362-1340.
  6. ^ "Write Alternate History in an Hour". Out of This World Library Programs: 116–118. 2017. doi:10.5040/9798400694615.0042.
  7. ^ Yadav, Devansh; Mondal, Shouvick (2025). "Evaluating Pre-Trained Large Language Models on Zero Shot Prompts for Parallelization of Source Code". doi.org. Retrieved 2025-05-19.
  8. ^ Li, Yujia; Choi, David; Chung, Junyoung; Kushman, Nate; Schrittwieser, Julian; Leblond, Rémi; Eccles, Tom; Keeling, James; Gimeno, Felix; Dal Lago, Agustin; Hubert, Thomas; Choy, Peter; de Masson d’Autume, Cyprien; Babuschkin, Igor; Chen, Xinyun (2022-12-09). "Competition-level code generation with AlphaCode". Science. 378 (6624): 1092–1097. doi:10.1126/science.abq1158. ISSN 0036-8075.
  9. ^ Senanayake, Janaka; Kalutarage, Harsha; Al-Kadri, Mhd Omar; Piras, Luca; Petrovski, Andrei (2023). "Labelled Vulnerability Dataset on Android Source Code (LVDAndro) to Develop AI-Based Code Vulnerability Detection Models". Proceedings of the 20th International Conference on Security and Cryptography. SCITEPRESS - Science and Technology Publications. doi:10.5220/0012060400003555.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Draft:AI_code_agents&oldid=1291369181"