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Automatic Programming: Large Language Models and Beyond

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arxiv 2405.02213 v2 pith:SM6HVZHS submitted 2024-05-03 cs.SE cs.AIcs.LG

Michael R. Lyu , Baishakhi Ray , Abhik Roychoudhury , Shin Hwei Tan , Patanamon Thongtanunam This is my paper · ORCID
classification cs.SEcs.AIcs.LG
keywords programmingautomaticcodeautomaticallygeneratedllmsaroundassurance
verification ladder T0 review T1 audit T2 compute T3 formal
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Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair

    cs.SE 2025-09 conditional novelty 6.0 of 10

    Adversarial bug reports induced attacker-desired patches in 90% of trials, while the best tested pre-repair filter caught only 47%, exposing a structural weakness in LLM-based automated program repair.

  2. WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models

    cs.CL 2024-12 conditional novelty 6.0 of 10

    A code LLM fine-tuned on winner responses from pairwise expert battles, with instructions mined from chat templates, beats same-size baselines without proprietary LLMs.

  3. Automated Repair of C Programs Using Large Language Models

    cs.SE 2025-09 conditional novelty 5.0 of 10

    An agent that combines spectrum-based fault localization, test feedback, and chain-of-thought prompting repairs 44.93% of 3,902 Codeflaws C bugs, a 3.61-point gain over GPT-4 with CoT.

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