Version history, similar report, and structure: Putting them together for improved bug localization
During the evolution of a software system, a large number of bug reports are submitted. Locating the source code files that need to be fixed to resolve the bugs is a challenging problem. Thus, there is a need for a technique that can automatically figure out these buggy files. A number of bug locali...
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sg-smu-ink.sis_research-34192015-11-15T14:37:57Z Version history, similar report, and structure: Putting them together for improved bug localization Wang, Shaowei LO, David During the evolution of a software system, a large number of bug reports are submitted. Locating the source code files that need to be fixed to resolve the bugs is a challenging problem. Thus, there is a need for a technique that can automatically figure out these buggy files. A number of bug localization solutions that take in a bug report and output a ranked list of files sorted based on their likelihood to be buggy have been proposed in the literature. However, the accuracy of these tools still need to be improved. In this paper, to address this need, we propose AmaLgam, a new method for locating relevant buggy files that puts together version history, similar reports, and structure. To do this, AmaLgam integrates a bug prediction technique used in Google which analyzes version history, with a bug localization technique named BugLocator which analyzes similar reports from bug report system, and the state-ofthe-art bug localization technique BLUiR which considers structure. We perform a large-scale experiment on four open source projects, namely AspectJ, Eclipse, SWT and ZXing to localize more than 3,000 bugs. Compared with a historyaware bug localization solution of Sisman and Kak, our approach achieves a 46.1% improvement in terms of mean average precision (MAP). Compared with BugLocator, our approach achieves a 24.4% improvement in terms of MAP. Compared with BLUiR, our approach achieves a 16.4% improvement in terms of MAP. 2014-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2419 info:doi/10.1145/2597008.2597148 https://ink.library.smu.edu.sg/context/sis_research/article/3419/viewcontent/p53_wang.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Version History Similar Report Structure Bug Localization Software Engineering |
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Version History Similar Report Structure Bug Localization Software Engineering Wang, Shaowei LO, David Version history, similar report, and structure: Putting them together for improved bug localization |
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During the evolution of a software system, a large number of bug reports are submitted. Locating the source code files that need to be fixed to resolve the bugs is a challenging problem. Thus, there is a need for a technique that can automatically figure out these buggy files. A number of bug localization solutions that take in a bug report and output a ranked list of files sorted based on their likelihood to be buggy have been proposed in the literature. However, the accuracy of these tools still need to be improved. In this paper, to address this need, we propose AmaLgam, a new method for locating relevant buggy files that puts together version history, similar reports, and structure. To do this, AmaLgam integrates a bug prediction technique used in Google which analyzes version history, with a bug localization technique named BugLocator which analyzes similar reports from bug report system, and the state-ofthe-art bug localization technique BLUiR which considers structure. We perform a large-scale experiment on four open source projects, namely AspectJ, Eclipse, SWT and ZXing to localize more than 3,000 bugs. Compared with a historyaware bug localization solution of Sisman and Kak, our approach achieves a 46.1% improvement in terms of mean average precision (MAP). Compared with BugLocator, our approach achieves a 24.4% improvement in terms of MAP. Compared with BLUiR, our approach achieves a 16.4% improvement in terms of MAP. |
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Wang, Shaowei LO, David |
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Wang, Shaowei LO, David |
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Wang, Shaowei |
title |
Version history, similar report, and structure: Putting them together for improved bug localization |
title_short |
Version history, similar report, and structure: Putting them together for improved bug localization |
title_full |
Version history, similar report, and structure: Putting them together for improved bug localization |
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Version history, similar report, and structure: Putting them together for improved bug localization |
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Version history, similar report, and structure: Putting them together for improved bug localization |
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version history, similar report, and structure: putting them together for improved bug localization |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/sis_research/2419 https://ink.library.smu.edu.sg/context/sis_research/article/3419/viewcontent/p53_wang.pdf |
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