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Analysis of ChatGPT on Source Code

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arxiv 2306.00597 v2 pith:LL626YSZ submitted 2023-06-01 cs.SE cs.AIcs.PL

Ahmed R. Sadik , Antonello Ceravola , Frank Joublin , Jibesh Patra This is my paper · ORCID
classification cs.SEcs.AIcs.PL
keywords chatgptcodellmstheyanalysisbenefitsmodelsoffer
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper explores the use of Large Language Models (LLMs) and in particular ChatGPT in programming, source code analysis, and code generation. LLMs and ChatGPT are built using machine learning and artificial intelligence techniques, and they offer several benefits to developers and programmers. While these models can save time and provide highly accurate results, they are not yet advanced enough to replace human programmers entirely. The paper investigates the potential applications of LLMs and ChatGPT in various areas, such as code creation, code documentation, bug detection, refactoring, and more. The paper also suggests that the usage of LLMs and ChatGPT is expected to increase in the future as they offer unparalleled benefits to the programming community.

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Forward citations

Cited by 4 Pith papers

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

  1. Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models

    cs.CV 2025-09 reject novelty 4.0 of 10

    Quantitative geometry scores across four input types show semantic richness improves LLM-generated CAD fidelity, with code-based prompts reaching perfect scores only after human code edits.

  2. LLM-Ehnanced Holonic Architecture for Ad-Hoc Scalable SoS

    cs.AI 2025-01 reject novelty 4.0 of 10

    A new holonic system-of-systems architecture gives every component an LLM-powered reasoning layer and four management roles, but it is proposed and sketched, never implemented or measured.

  3. HyperGraphOS: A Meta Operating System for Science and Engineering

    cs.AI 2024-12 conditional novelty 4.0 of 10

    HyperGraphOS is an open-source, browser-based graph-modeling workspace that uses domain-specific languages to build, execute, and generate code for science and engineering models.

  4. SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment

    cs.SE 2025-01 reject novelty 2.0 of 10

    A survey-based review concluding that AI coding assistants introduce security vulnerabilities, hallucinated code, and data leak risks requiring developer vigilance.

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