Automatic Localization of Bugs to Faulty Components in Large Scale Software Systems Using Bayesian Classification

Leif Jonsson, David Broman, Måns Magnusson, K. Sandahl, M. Villani, Sigrid Eldh
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引用次数: 7

Abstract

We suggest a Bayesian approach to the problem of reducing bug turn-around time in large software development organizations. Our approach is to use classification to predict where bugs are located in components. This classification is a form of automatic fault localization (AFL) at the component level. The approach only relies on historical bug reports and does not require detailed analysis of source code or detailed test runs. Our approach addresses two problems identified in user studies of AFL tools. The first problem concerns the trust in which the user can put in the results of the tool. The second problem concerns understanding how the results were computed. The proposed model quantifies the uncertainty in its predictions and all estimated model parameters. Additionally, the output of the model explains why a result was suggested. We evaluate the approach on more than 50000 bugs.
基于贝叶斯分类的大规模软件系统缺陷自动定位
我们建议使用贝叶斯方法来解决大型软件开发组织中减少bug周转时间的问题。我们的方法是使用分类来预测错误在组件中的位置。这种分类是组件级自动故障定位(AFL)的一种形式。该方法仅依赖于历史错误报告,不需要详细的源代码分析或详细的测试运行。我们的方法解决了在AFL工具的用户研究中发现的两个问题。第一个问题涉及用户对工具结果的信任。第二个问题涉及如何计算结果。所提出的模型量化了其预测和所有估计模型参数的不确定性。此外,模型的输出解释了为什么提出了一个结果。我们对超过50000个bug进行了评估。
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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