Adapting bug prediction models to predict reverted commits at Wayfair

Abstract

Researchers have proposed many algorithms to predict software bugs. Given a software entity (e.g., a file or method), these algorithms predict whether the entity is bug-prone. However, since these algorithms cannot identify specific bugs, this does not tend to be particularly useful in practice. In this work, we adapt this prior work to the related problem of predicting whether a commit is likely to be reverted. Given the batch nature of continuous integration deployment at scale, this allows developers to find time-sensitive bugs in production more quickly. The models in this paper are based on features extracted from the revision history of a codebase that are typically used in bug prediction. Our experiments, performed on the three main repositories for the Wayfair website, show that our models can rank reverted commits above 80% of non-reverted commits on average. Moreover, when given to Wayfair developers, our models reduce the amount of time needed to find certain kinds of bugs by 55%. Wayfair continues to use our findings and models today to help find bugs during software deployments.