A Comparative Study of Vectorization Methods on BugLocator

2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) Pub Date : 2019-08-01 DOI:10.1109/SEAA.2019.00045

S. Amasaki, Hirohisa Aman, Tomoyuki Yokogawa

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Abstract

CONTEXT: Debugging is a labor-intensive and time-consuming activity. Automatic bug localization techniques have been proposed for reducing this effort. Among the techniques, Information retrieval (IR) based bug localization techniques take a bug report and give a rank list of source modules which are likely to cause the bug. Those techniques use a few variants of tf-idf vectorizations for bug reports and software modules though different vectorizations may give vastly different performances. OBJECTIVE: To explore the effects of vectorization methods on IR-based bug localization. METHOD: An empirical evaluation was conducted with 46 public data sets and 6 vectorization methods. BugLocator was used as a test bed. RESULTS: We found a vectorization used in BugLocator was one of the best. However, we found a better vectorization for representing software modules. CONCLUSIONS: It is worth to examine different vectorization methods for better IR-based bug localization because a preference for the methods can change as demonstrated in this study.

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bug定位器矢量化方法的比较研究

背景:调试是一项劳动密集且耗时的活动。为了减少这种工作量，已经提出了自动错误定位技术。其中，基于信息检索(IR)的缺陷定位技术采用缺陷报告，并给出可能导致缺陷的源模块的等级列表。这些技术在bug报告和软件模块中使用了tf-idf矢量化的一些变体，尽管不同的矢量化可能会产生截然不同的性能。目的:探讨矢量化方法对基于ir的昆虫定位的影响。方法:利用46个公开数据集和6种矢量化方法进行实证评价。BugLocator被用作测试平台。结果:我们发现在BugLocator中使用的矢量化是最好的。然而，我们发现了一种更好的表示软件模块的矢量化方法。结论:值得研究不同的矢量化方法，以获得更好的基于ir的bug定位，因为正如本研究所表明的那样，人们对方法的偏好可能会发生变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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