A Soft Alignment Model for Bug Deduplication

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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.

OriginalspracheEnglisch
Titel2020 IEEE/ACM 17th International Conference on Mining Software Repositories : MSR 2020, Proceedings; Seoul, Republic of Korea 29-30 June 2020
Anzahl der Seiten11
ErscheinungsortNew York
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum29.06.2020
Seiten43-53
ISBN (elektronisch)978-1-4503-7957-1
DOIs
PublikationsstatusErschienen - 29.06.2020
Extern publiziertJa
Veranstaltung17th IEEE/ACM International Conference on Mining Software Repositories, MSR 2020, co-located with the 42nd International Conference on Software Engineering. ICSE 2020 - Virtual, Online, Südkorea
Dauer: 29.06.202030.06.2020

DOI

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