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Canadian Conference on Artificial Intelligence

Canadian AI 2020: Advances in Artificial Intelligence pp 470–482Cite as

Amalgamated Models for Detecting Duplicate Bug Reports

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12109))

Abstract

Automatic identification of duplicate bug reports is a critical research problem in the software repositories’ mining area. The aim of this paper is to propose and compare amalgamated models for detecting duplicate bug reports using textual and non-textual information of bug reports. The algorithmic models viz. LDA, TF-IDF, GloVe, Word2Vec, and their amalgamation are used to rank bug reports according to their similarity with each other. The amalgamated score is generated by aggregating the ranks generated by models. The empirical evaluation has been performed on the open datasets from large open source software projects. The metrics used for evaluation are mean average precision (MAP), mean reciprocal rank (MRR) and recall rate. The experimental results show that amalgamated model (TF-IDF + Word2Vec + LDA) outperforms other amalgamated models for duplicate bug recommendations. It is also concluded that amalgamation of Word2Vec with TF-IDF models works better than TF-IDF with GloVe. The future scope of current work is to develop a python package that allows the user to select the individual models and their amalgamation with other models on a given dataset.

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Notes

  1. 1.

    https://zenodo.org/record/400614#.XaNPt-ZKh8x, last accessed: March 2020.

  2. 2.

    https://colab.research.google.com.

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Authors and Affiliations

  1. Wilfrid Laurier University, Waterloo, Canada

    Sukhjit Singh Sehra

  2. Ryesron University, Toronto, Canada

    Tamer Abdou & Ayşe Başar

  3. Guru Nanak Dev Engineering College, Punjab, India

    Sumeet Kaur Sehra

Authors
  1. Sukhjit Singh Sehra
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  2. Tamer Abdou
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  3. Ayşe Başar
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  4. Sumeet Kaur Sehra
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Corresponding author

Correspondence to Sukhjit Singh Sehra .

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Editors and Affiliations

  1. National Research Council Canada, Ottawa, ON, Canada

    Cyril Goutte

  2. Queen’s University, Kingston, ON, Canada

    Xiaodan Zhu

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Sehra, S.S., Abdou, T., Başar, A., Sehra, S.K. (2020). Amalgamated Models for Detecting Duplicate Bug Reports. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_49

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  • DOI: https://doi.org/10.1007/978-3-030-47358-7_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47357-0

  • Online ISBN: 978-3-030-47358-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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