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KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction
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AI
DOI：10.1016/j.knosys.2024.112588.png)
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摘要
En 中文
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.
Keyword:
Bug report
Bug prediction
Deep learning framework
Large language model
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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