2017 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2017), September 4–8, 2017, Paderborn, Germany

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Crash Analysis
Research Papers
S3, Chair: Dirk Beyer
Automatically Analyzing Groups of Crashes for Finding Correlations
Publisher's Version
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.

Time stamp: 2020-09-21T17:01:39+02:00