Large-Scale Patch Recommendation at Alibaba

Abstract

We present Precfix, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. Precfix collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22%. Precfix has been rolled out to Alibaba to support various critical businesses.