Predicting the Impact of Crashes Across Release Channels
Suhaib Mujahid, Diego Elias Costa, Marco Castelluccio
TL;DR
The work addresses predicting the downstream impact of crashes observed in Nightly channels when software moves to more stable release channels. It proposes a data-driven framework that analyzes crash volumes and characteristics across channels to forecast their effect on stable releases. Key challenges include handling crash reports after fixes, feature toggles, and gradual rollouts that can obscure true crash incidence. Public Firefox datasets, including CrashStats, Bugzilla, and BugBug, are identified as sources to support cross-channel crash impact analysis and prioritization for release management.
Abstract
Software maintenance faces a persistent challenge with crash bugs, especially across diverse release channels catering to distinct user bases. Nightly builds, favoured by enthusiasts, often reveal crashes that are cheaper to fix but may differ significantly from those in stable releases. In this paper, we emphasize the need for a data-driven solution to predict the impact of crashes happening on nightly channels once they are released to stable channels. We also list the challenges that need to be considered when approaching this problem.