Exploiting hubs for self-adaptive secondary re-ranking in bug report duplicate detection

Abstract

Bug duplicate detection is an integral part of many bug tracking systems. Most bugs are reported multiple times and detecting the duplicates saves time and valuable resources. We propose a novel approach to potential duplicate report query ranking. Our secondary re-ranking procedure is self-adaptive, as it learns from previous report occurrences. It is based on the analysis of temporal evolution of the underlying distribution of influence. The experiments show definite improvements in system performance.