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Towards python program repair with generative pre-trained transformer (GPT-3.5)
- 1 University of Michigan
- 2 Dalian University of Technology
* Author to whom correspondence should be addressed.
Abstract
ChatGPT has a great potential using a simple prompt design, which means further studies can be done to investigate the effect of different prompt designs. Complex software often contains hidden bugs in its source code. Recent study suggests that OpenAI’s ChatGPT can perform numerous operations including code-to-code operations like code completion, translation, repair, and summarization, along with language-to-code operations such as code explanation and search. ChatGPT’s dialogue capability can assist in generating more accurate bug fixes. However, it sometimes offers solutions without seeking further information, which can mislead users. To address this issue and enhance user experience, we conducted three design iterations to develop “D-bugger” — a system enabling programmers to fix bugs more effectively. We conducted a survey to gauge the need for refinement, designed a low-fidelity prototype with key features, and then created a high-fidelity prototype evaluated by Python users. Our work aims to enhance the debugging process and user engagement.
Keywords
Program repair, ChatGPT, Misleading rate
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Cite this article
Cao,Z.;Ren,Q. (2024). Towards python program repair with generative pre-trained transformer (GPT-3.5). Theoretical and Natural Science,43,102-112.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
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