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Enhancing Bug-Fixing Time Prediction with LSTM-Based Approach

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Product-Focused Software Process Improvement (PROFES 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14484))

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Abstract

This work presents an approach based on Long short-term memory (LSTM) for estimating the bug-fixing time in the bug triage process. Existing bug-fixing time predictor approaches underutilize useful semantic information and long-term dependencies between activities in the bug-fixing sequence. Therefore, the proposed approach is a deep learning-based model that converts activities into vectors of real numbers based on their semantic meaning. It then uses LSTM to identify long-term dependencies between activities and classifies sequences as having either short fixing time or long fixing time. The evaluation on bug reports from the Eclipse project shows that this approach performs slightly better than the current best in the literature, boasting improved metrics such as accuracy, precision, f-score, and recall.

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Authors and Affiliations

  1. Department of Informatics, University of Bari Aldo Moro, Bari, Italy

    Pasquale Ardimento

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  1. Pasquale Ardimento
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Correspondence to Pasquale Ardimento .

Editor information

Editors and Affiliations

  1. FHV Vorarlberg University of Applied Science, Dornbirn, Austria

    Regine Kadgien

  2. Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany

    Andreas Jedlitschka

  3. FHV Vorarlberg University of Applied Science, Dornbirn, Austria

    Andrea Janes

  4. University of Oulu, Oulu, Finland

    Valentina Lenarduzzi

  5. University of Oulu, Oulu, Finland

    Xiaozhou Li

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Ardimento, P. (2024). Enhancing Bug-Fixing Time Prediction with LSTM-Based Approach. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V., Li, X. (eds) Product-Focused Software Process Improvement. PROFES 2023. Lecture Notes in Computer Science, vol 14484. Springer, Cham. https://doi.org/10.1007/978-3-031-49269-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-49269-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49268-6

  • Online ISBN: 978-3-031-49269-3

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