Reranking-based crash report deduplication

Software projects collect and deduplicate vastly numerous crash reports from users to fix bugs efficiently. However, most existing automated methods have performance issues during large-scale clustering. We propose a reranking- based crash report clustering method. Our method is a combination of two earlier methods. By computing similarity used in ReBucket for the crash reports that are highly similar to the query crash report, the method can process reports with throughput equal to that of PartyCrasher. We also introduce an automatically generated dataset for crash report clustering tasks. The evaluation revealed that our method performs at high processing speed while maintaining high accuracy.