Chenglong Li , Zheng Zheng , Xiaoting Du , Xiangyue Ma , Zhengqi Wang , Xinheng Li
{"title":"KnowBug:利用错误报告知识增强大型语言模型,用于深度学习框架的错误预测","authors":"Chenglong Li , Zheng Zheng , Xiaoting Du , Xiangyue Ma , Zhengqi Wang , Xinheng Li","doi":"10.1016/j.knosys.2024.112588","DOIUrl":null,"url":null,"abstract":"<div><div>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 <em>KnowBug</em>, 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 <em>KnowBug</em>’s proficiency in understanding bug reports and predicting different bug types.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction\",\"authors\":\"Chenglong Li , Zheng Zheng , Xiaoting Du , Xiangyue Ma , Zhengqi Wang , Xinheng Li\",\"doi\":\"10.1016/j.knosys.2024.112588\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 <em>KnowBug</em>, 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 <em>KnowBug</em>’s proficiency in understanding bug reports and predicting different bug types.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S095070512401222X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095070512401222X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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 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.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.