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.
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