A study on prompt design, advantages and limitations of ChatGPT for deep learning program repair

Abstract

The emergence of large language models (LLMs) such as ChatGPT has revolutionized many fields. In particular, recent advances in LLMs have triggered various studies examining the use of these models for software development tasks, such as program repair, code understanding, and code generation. Prior studies have shown the capability of ChatGPT in repairing conventional programs. However, debugging deep learning (DL) programs poses unique challenges since the decision logic is not directly encoded in the source code. This requires LLMs to not only parse the source code syntactically but also understand the intention of DL programs. Therefore, ChatGPT’s capability in repairing DL programs remains unknown. To fill this gap, our study aims to answer three research questions: (1) Can ChatGPT debug DL programs effectively? (2) How can ChatGPT’s repair performance be improved by prompting? (3) In which way can dialogue help facilitate the repair? Our study analyzes the typical information that is useful for prompt design and suggests enhanced prompt templates that are more efficient for repairing DL programs. On top of them, we summarize the dual perspectives (i.e., advantages and disadvantages) of ChatGPT’s ability, such as its handling of API misuse and recommendation, and its shortcomings in identifying default parameters. Our findings indicate that ChatGPT has the potential to repair DL programs effectively and that prompt engineering and dialogue can further improve its performance by providing more code intention. We also identified the key intentions that can enhance ChatGPT’s program repairing capability.