mlsanityData to 14 Oct 2026

Optimizing AI-Assisted Code Generation

arXiv 2412.10953 ↗First posted 14 Dec 2024cs.SE cs.AI cs.LGcited by 2

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Abstract (arXiv)

In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaCode, take advantage of advances in machine learning (ML) and natural language processing (NLP) enabled by large language models (LLMs). However, it must be borne in mind that these models work probabilistically, which means that although they can generate complex code from natural language input, there is no guarantee for the functionality and security of the generated code. However, to fully exploit the considerable potential of this technology, the security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them. In addition, we explore how these systems can be optimized to create safe, high-performance, and executable artificial intelligence (AI) models, and consider how to improve their accessibility to make AI development more inclusive and equitable.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 92 references that are in the map. First posted on arXiv 14 Dec 2024.

Builds on

39 of 92 references.

References · 39

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27 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Studycited by 0
  2. 2026Bespoke Visual Assistance: What and How do Blind and Low-Vision People Create with Agentic Programming?cited by 0