An Empirical Study of Highly Impactful Bugs in Mozilla Projects

Abstract

Bug triaging is the process that consists in screening and prioritising bugs to allow a software organisation to focus its limited resources on bugs with high impact on software quality. In a previous work, we proposed an entropy-based crash triaging approach that can help software organisations identify crash-types that affect a large user base with high frequency. We refer to bugs associated to these crash-types as highly-impactful bugs. The proposed triaging approach can identify highly-impactful bugs only after they have led to crashes in the field for a certain period of time. Therefore, to reduce the impact of highly-impactful bugs on user perceived quality, an early identification of these bugs is necessary. In this paper, we examine the characteristics of highly-impactful bugs in Mozilla Firefox and Fennec for Android, and propose statistical models to help software organisations predict them early on before they impact a large population of users. Results show that our proposed prediction models can achieve a precision up to 64.2% (in Firefox) and a recall up to 98.3% (in Fennec). We also evaluate the benefits of our proposed models and found that, on average, they could help reduce 23.0% of Firefox' crashes and 13.4% of Fennec's crashes, while reducing 28.6% of impacted machine profiles for Firefox and 49.4% for Fennec. Software organisations could use our prediction models to catch highly-impactful bugs early during the triaging process, preventing them from impacting a larger user base.