Understanding the Impact of Domain Term Explanation on Duplicate Bug Report Detection
Usmi Mukherjee, Mohammad Masudur Rahman
TL;DR
This study addresses the challenge of detecting duplicate bug reports, particularly when duplicates are textually dissimilar and reports are short. It introduces a domain-term explanation framework for Java, built from StackOverflow, API docs, and glossaries, and uses a fine-tuned T5 model to generate explanations for extracted terms. Bug reports are enriched with these explanations (BR_E) and evaluated against seven baseline techniques, showing consistent performance gains across recall, precision, F1, and AUC, with larger benefits for dissimilar duplicates. The results suggest practical benefits for bug-tracking systems and point to future work integrating richer content and cross-project terminology analyses to further enhance duplicate detection and bug report comprehension.
Abstract
Duplicate bug reports make up 42% of all reports in bug tracking systems (e.g., Bugzilla), causing significant maintenance overhead. Hence, detecting and resolving duplicate bug reports is essential for effective issue management. Traditional techniques often focus on detecting textually similar duplicates. However, existing literature has shown that up to 23% of the duplicate bug reports are textually dissimilar. Moreover, about 78% of bug reports in open-source projects are very short (e.g., less than 100 words) often containing domain-specific terms or jargon, making the detection of their duplicate bug reports difficult. In this paper, we conduct a large-scale empirical study to investigate whether and how enrichment of bug reports with the explanations of their domain terms or jargon can help improve the detection of duplicate bug reports. We use 92,854 bug reports from three open-source systems, replicate seven existing baseline techniques for duplicate bug report detection, and answer two research questions in this work. We found significant performance gains in the existing techniques when explanations of domain-specific terms or jargon were leveraged to enrich the bug reports. Our findings also suggest that enriching bug reports with such explanations can significantly improve the detection of duplicate bug reports that are textually dissimilar.