An Empirical Study of Crash-inducing Commits in Mozilla Firefox. An, L., Khomh, F., & Gu�h�neuc, Y. Software Quality Journal (SQJ), 26(2):553–584, Springer, June, 2018. 33 pages.
An Empirical Study of Crash-inducing Commits in Mozilla Firefox [pdf]Paper  abstract   bibtex   
Software crashes are dreaded by both software organisations and end-users. Many software organisations have automatic crash reporting tools embedded in their software systems to help quality-assurance teams track and fix crash-related bugs. Previous approaches, which focused on the triaging of crash-types and crash-related bugs, can help software organisations increase their debugging efficiency of crashes. However, these approaches can only be applied after the software systems have been crashing for a certain period of time. To help software organisations detect and fix crash-prone code earlier, we examine the characteristics of commits that lead to crashes, which we call crash-inducing commits, in Mozilla Firefox. We observe that crash-inducing commits are often submitted by developers with less experience and that developers perform more addition and deletion of lines of code in crash-inducing commits but also that they need less effort to fix the bugs caused by these commits. We also characterise commits that would lead to frequent crashes, which impact a large user base, which we call highly impactful crash-inducing commits. Compared to other crash-related bugs, we observe that bugs due to highly impactful crash-inducing commits were less reopened by developers and tend to be fixed by a single commit. We build predictive models to help software organisations detect and fix crash-prone bugs early, when their developers commit code. Our predictive models achieve a precision of 61.2% and a recall of 94.5% to predict crash-inducing commits and a precision of 60.9% and a recall of 91.1% to predict highly impactful crash-inducing commits. Software organisations could use our models and approach to track and fix crash-prone commits early, before they negatively impact users, thus increasing bug fixing efficiency and user-perceived quality.

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