A Soft Alignment Model for Bug Deduplication

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


Bug tracking systems (BTS) are widely used in software projects. An important task in such systems consists of identifying duplicate bug reports, i.e., distinct reports related to the same software issue. For several reasons, reporting bugs that have already been reported is quite frequent, making their manual triage impractical in large BTSs. In this paper, we present a novel deep learning network based on soft-attention alignment to improve duplicate bug report detection. For a given pair of possibly duplicate reports, the attention mechanism computes interdependent representations for each report, which is more powerful than previous approaches. We evaluate our model on four well-known datasets derived from BTSs of four popular open-source projects. Our evaluation is based on a ranking-based metric, which is more realistic than decision-making metrics used in many previous works. Achieved results demonstrate that our model outperforms state-of-the-art systems and strong baselines in different scenarios. Finally, an ablation study is performed to confirm that the proposed architecture improves the duplicate bug reports detection.

Original languageEnglish
Title of host publication2020 IEEE/ACM 17th International Conference on Mining Software Repositories : MSR 2020, Proceedings; Seoul, Republic of Korea 29-30 June 2020
Number of pages11
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Publication date29.06.2020
ISBN (Electronic)978-1-4503-7957-1
Publication statusPublished - 29.06.2020
Externally publishedYes
Event17th IEEE/ACM International Conference on Mining Software Repositories, MSR 2020, co-located with the 42nd International Conference on Software Engineering. ICSE 2020 - Virtual, Online, Korea, Republic of
Duration: 29.06.202030.06.2020

    Research areas

  • Attention Mechanism, Bug Tracking Systems, Deep Learning, Duplicate Bug Report Detection
  • Business informatics