On Combining IR Methods to Improve Bug localization
On Combining IR Methods to Improve Bug localization
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DOI：
10.1145/3387904.3389280
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Saket Khatiwada;Miroslav Tushev;Anas Mahmoud
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文献类型：
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作者：
Saket Khatiwada;Miroslav Tushev;Anas Mahmoud
信息检索（IR）方法最近已被用来提供自动支持的错误定位任务。然而，一个基于IR的错误定位工具是有用的，它必须达到足够的检索精度。较低的精确度和召回率可能会让开发人员处理大量不正确的信息。为了解决这个问题，在本文中，我们系统地研究了各种IR方法相结合的缺陷定位引擎的检索精度的影响。主要的假设是，不同的IR方法，针对工件之间的相似性的不同维度，可以用来增强彼此的结果的置信度。五个基准系统，从不同的应用领域进行分析。结果表明，a）接近最优的全局配置可以确定不同的IR方法的组合，B）优化的IR混合可以显着优于单独的方法以及其他未优化的方法，和c）混合方法实现其最佳性能时，利用信息理论的IR方法。我们的研究结果可用于提高基于IR的错误定位工具的实用性，并最大限度地减少开发人员在定位错误时经常面临的认知过载。
Information Retrieval (IR) methods have been recently employed to provide automatic support for bug localization tasks. However, for an IR-based bug localization tool to be useful, it has to achieve adequate retrieval accuracy. Lower precision and recall can leave developers with large amounts of incorrect information to wade through. To address this issue, in this paper, we systematically investigate the impact of combining various IR methods on the retrieval accuracy of bug localization engines. The main assumption is that different IR methods, targeting different dimensions of similarity between artifacts, can be used to enhance the confidence in each others' results. Five benchmark systems from different application domains are used to conduct our analysis. The results show that a) near-optimal global configurations can be determined for different combinations of IR methods, b) optimized IR-hybrids can significantly outperform individual methods as well as other unoptimized methods, and c) hybrid methods achieve their best performance when utilizing information-theoretic IR methods. Our findings can be used to enhance the practicality of IR-based bug localization tools and minimize the cognitive overload developers often face when locating bugs.