KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction

Understanding and predicting the bug type is crucial for developers striving to enhance testing efficiency and reduce software release problems. Bug reports, although semi-structured, contain valuable semantic information, making their comprehension critical for accurate bug prediction. Recent advan...

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Bibliographic Details
Published in:Knowledge-based systems 2024-12, Vol.305, p.112588, Article 112588
Main Authors: Li, Chenglong, Zheng, Zheng, Du, Xiaoting, Ma, Xiangyue, Wang, Zhengqi, Li, Xinheng
Format: Article
Language:English
Subjects:
Bug prediction
Bug report
Deep learning framework
Large language model
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Summary:Understanding and predicting the bug type is crucial for developers striving to enhance testing efficiency and reduce software release problems. Bug reports, although semi-structured, contain valuable semantic information, making their comprehension critical for accurate bug prediction. Recent advances in large language models (LLMs), especially generative LLMs, have demonstrated their power in natural language processing. Many studies have utilized these models to understand various forms of textual data. However, the capability of LLMs to fully understand bug reports remains uncertain. To tackle this challenge, we propose KnowBug, a framework designed to augment LLMs with knowledge from bug reports to improve their ability to predict bug types. In this framework, we utilize bug reports from open-source deep learning frameworks, design specialized prompts, and fine-tune LLMs to assess KnowBug’s proficiency in understanding bug reports and predicting different bug types.
ISSN:0950-7051
DOI:10.1016/j.knosys.2024.112588