Improved bug localization based on code change histories and bug reports

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• 作者：Klaus Changsun Youm ; ^a ; ^b ; ^{klausyoum@skku.edu} ; ^{klaus.youm@samsung.com} ; June Ahn^a ; ^{ahnjune@skku.edu} ; Eunseok Lee ; ^a ; ^{leees@skku.edu}
• 关键词：Bug localization ; Information retrieval ; Bug reports ; Stack traces ; Code change history ; Method analysis
• 刊名：Information and Software Technology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：82
• 期：Complete
• 页码：177-192
• 全文大小：3409 K
• 卷排序：82

文摘

Several issues or defects in released software during the maintenance phase are reported to the development team. It is costly and time-consuming for developers to precisely localize bugs. Bug reports and the code change history are frequently used and provide information for identifying fault locations during the software maintenance phase.ObjectiveIt is difficult to standardize the style of bug reports written in natural languages to improve the accuracy of bug localization. The objective of this paper is to propose an effective information retrieval-based bug localization method to find suspicious files and methods for resolving bugs.MethodIn this paper, we propose a novel information retrieval-based bug localization approach, termed Bug Localization using Integrated Analysis (BLIA). Our proposed BLIA integrates analyzed data by utilizing texts, stack traces and comments in bug reports, structured information of source files, and the source code change history. We improved the granularity of bug localization from the file level to the method level by extending previous bug repository data.ResultsWe evaluated the effectiveness of our approach based on experiments using three open-source projects, namely AspectJ, SWT, and ZXing. In terms of the mean average precision, on average our approach improves the metric of BugLocator, BLUiR, BRTracer, AmaLgam and the preliminary version of BLIA by 54%, 42%, 30%, 25% and 15%, respectively, at the file level of bug localization.ConclusionCompared with prior tools, the results showed that BLIA outperforms these other methods. We analyzed the influence of each score of BLIA from various combinations based on the analyzed information. Our proposed enhancement significantly improved the accuracy. To improve the granularity level of bug localization, a new approach at the method level is proposed and its potential is evaluated.