Automatically analyzing groups of crashes for finding correlations

M. Castelluccio, Carlo Sansone, L. Verdoliva, G. Poggi
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引用次数: 19

Abstract

We devised an algorithm, inspired by contrast-set mining algorithms such as STUCCO, to automatically find statistically significant properties (correlations) in crash groups. Many earlier works focused on improving the clustering of crashes but, to the best of our knowledge, the problem of automatically describing properties of a cluster of crashes is so far unexplored. This means developers currently spend a fair amount of time analyzing the groups themselves, which in turn means that a) they are not spending their time actually developing a fix for the crash; and b) they might miss something in their exploration of the crash data (there is a large number of attributes in crash reports and it is hard and error-prone to manually analyze everything). Our algorithm helps developers and release managers understand crash reports more easily and in an automated way, helping in pinpointing the root cause of the crash. The tool implementing the algorithm has been deployed on Mozilla's crash reporting service.
自动分析崩溃组以查找相关性
受对比集挖掘算法(如STUCCO)的启发,我们设计了一种算法,用于自动查找崩溃组中具有统计意义的属性(相关性)。许多早期的工作都集中在改进崩溃集群上,但据我们所知,自动描述崩溃集群属性的问题到目前为止还没有被探索过。这意味着开发人员目前花费了相当多的时间来分析组本身,这反过来意味着a)他们没有花时间真正开发崩溃的修复程序;b)他们在对崩溃数据的探索中可能会遗漏一些东西(崩溃报告中有大量的属性,手动分析所有的东西是很困难且容易出错的)。我们的算法帮助开发人员和发布管理人员更容易地以自动化的方式理解崩溃报告,帮助确定崩溃的根本原因。实现该算法的工具已经部署在Mozilla的崩溃报告服务上。
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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