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Better Python Programming for all: With the focus on Maintainability

arXiv 2408.09134 ↗First posted 17 Aug 2024cs.SE cs.AIcited by 0

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Abstract (arXiv)

This study aims to enhance the maintainability of code generated by Large Language Models (LLMs), with a focus on the Python programming language. As the use of LLMs for coding assistance grows, so do concerns about the maintainability of the code they produce. Previous research has mainly concentrated on the functional accuracy and testing success of generated code, overlooking aspects of maintainability. Our approach involves the use of a specifically designed dataset for training and evaluating the model, ensuring a thorough assessment of code maintainability. At the heart of our work is the fine-tuning of an LLM for code refactoring, aimed at enhancing code readability, reducing complexity, and improving overall maintainability. After fine-tuning an LLM to prioritize code maintainability, our evaluations indicate that this model significantly improves code maintainability standards, suggesting a promising direction for the future of AI-assisted software development.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 17 Aug 2024.

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22 of 40 references.

References · 22

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
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  4. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
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  10. 2022A Systematic Evaluation of Large Language Models of Codecited by 360
  11. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
  12. 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 178
10 more
  1. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  2. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
  3. 2023CodeBERTScore: Evaluating Code Generation with Pretrained Models of Codecited by 101
  4. 2023AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuningcited by 49
  5. 2023Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMscited by 38
  6. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  7. 2022CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsingcited by 21
  8. 2023PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Modelscited by 17
  9. 2023Exploring the Robustness of Large Language Models for Solving Programming Problemscited by 16
  10. 2023The Program Testing Ability of Large Language Models for Codecited by 11

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