A Soft Alignment Model for Bug Deduplication

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

A Soft Alignment Model for Bug Deduplication. / Rodrigues, Irving Muller; Aloise, Daniel; Fernandes, Eraldo Rezende et al.
2020 IEEE/ACM 17th International Conference on Mining Software Repositories: MSR 2020, Proceedings; Seoul, Republic of Korea 29-30 June 2020. New York: Association for Computing Machinery, Inc, 2020. S. 43-53.

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Rodrigues, IM, Aloise, D, Fernandes, ER & Dagenais, M 2020, A Soft Alignment Model for Bug Deduplication. in 2020 IEEE/ACM 17th International Conference on Mining Software Repositories: MSR 2020, Proceedings; Seoul, Republic of Korea 29-30 June 2020. Association for Computing Machinery, Inc, New York, S. 43-53, 17th 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, 29.06.20. https://doi.org/10.1145/3379597.3387470

APA

Rodrigues, I. M., Aloise, D., Fernandes, E. R., & Dagenais, M. (2020). A Soft Alignment Model for Bug Deduplication. In 2020 IEEE/ACM 17th International Conference on Mining Software Repositories: MSR 2020, Proceedings; Seoul, Republic of Korea 29-30 June 2020 (S. 43-53). Association for Computing Machinery, Inc. https://doi.org/10.1145/3379597.3387470

Vancouver

Rodrigues IM, Aloise D, Fernandes ER, Dagenais M. A Soft Alignment Model for Bug Deduplication. in 2020 IEEE/ACM 17th International Conference on Mining Software Repositories: MSR 2020, Proceedings; Seoul, Republic of Korea 29-30 June 2020. New York: Association for Computing Machinery, Inc. 2020. S. 43-53 doi: 10.1145/3379597.3387470

Bibtex

@inbook{dcb86a3acebc4a468d692929fca74b7d,
title = "A Soft Alignment Model for Bug Deduplication",
abstract = "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. ",
keywords = "Attention Mechanism, Bug Tracking Systems, Deep Learning, Duplicate Bug Report Detection, Business informatics",
author = "Rodrigues, {Irving Muller} and Daniel Aloise and Fernandes, {Eraldo Rezende} and Michel Dagenais",
year = "2020",
month = jun,
day = "29",
doi = "10.1145/3379597.3387470",
language = "English",
pages = "43--53",
booktitle = "2020 IEEE/ACM 17th International Conference on Mining Software Repositories",
publisher = "Association for Computing Machinery, Inc",
address = "United States",
note = "17th IEEE/ACM International Conference on Mining Software Repositories, MSR 2020, co-located with the 42nd International Conference on Software Engineering. ICSE 2020 ; Conference date: 29-06-2020 Through 30-06-2020",

}

RIS

TY - CHAP

T1 - A Soft Alignment Model for Bug Deduplication

AU - Rodrigues, Irving Muller

AU - Aloise, Daniel

AU - Fernandes, Eraldo Rezende

AU - Dagenais, Michel

PY - 2020/6/29

Y1 - 2020/6/29

N2 - 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.

AB - 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.

KW - Attention Mechanism

KW - Bug Tracking Systems

KW - Deep Learning

KW - Duplicate Bug Report Detection

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85093703740&partnerID=8YFLogxK

U2 - 10.1145/3379597.3387470

DO - 10.1145/3379597.3387470

M3 - Article in conference proceedings

AN - SCOPUS:85093703740

SP - 43

EP - 53

BT - 2020 IEEE/ACM 17th International Conference on Mining Software Repositories

PB - Association for Computing Machinery, Inc

CY - New York

T2 - 17th IEEE/ACM International Conference on Mining Software Repositories, MSR 2020, co-located with the 42nd International Conference on Software Engineering. ICSE 2020

Y2 - 29 June 2020 through 30 June 2020

ER -

DOI

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