Software crashes are high-impact defects that affect software reliability, causing applications to terminate unexpectedly. Crash prioritization focuses the attention of maintenance teams on incoming types of crashes that are likely to have a large impact. Prior approaches have been applied to software that is continuously released, whereas, in the video game context, releases are often rolled out in large-scale scheduled launches, where a studio will work for months or years on a new title before releasing it on a scheduled date. In that context, crash data from live players is not available until after release, which is often too late to react.

In this study, we analyze post-release game crashes to identify temporal patterns that can inform strategies for prioritization at Ubisoft—a multinational video game publisher. Our analysis of temporal patterns shows that most types of post-release crashes impact few players, whereas a subset goes “viral,” quickly impacting many players. Those viral crashes may escalate immediately (i.e., outbreaks) or lie dormant before propagating to many players (i.e., time bombs). We use data from a previously released triple-A title to detect such viral crashes in a new title by leveraging stack-trace similarity and Machine Learning (ML). We find that our stack-trace similarity-based method outperforms ML-based methods, successfully identifying over half of the viral crash types in the new title. Moreover, the crash types detected by our similarity-based method account for a significantly larger number of crash occurrences than the viral crash types that are missed as false negatives. The findings of our study inform game development teams about proactive monitoring of mild crash types that can potentially impact many players, as well as approaches for early detection of potential viral crash types.