research-article

Continuous Software Bug Prediction

Authors:

Nachiappan NagappanAuthors Info & Claims

ESEM '21: Proceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

Article No.: 14, Pages 1 - 12

https://doi.org/10.1145/3475716.3475790

Published: 11 October 2021 Publication History

Abstract

Background: Many software bug prediction models have been proposed and evaluated on a set of well-known benchmark datasets. We conducted pilot studies on the widely used benchmark datasets and observed common issues among them. Specifically, most of existing benchmark datasets consist of randomly selected historical versions of software projects, which poses non-trivial threats to the validity of existing bug prediction studies since the real-world software projects often evolve continuously. Yet how to conduct software bug prediction in the real-world continuous software development scenarios is not well studied.

Aims: In this paper, to bridge the gap between current software bug prediction practice and real-world continuous software development, we propose new approaches to conduct bug prediction in real-world continuous software development regarding model building, updating, and evaluation.

Method: For model building, we propose ConBuild, which leverages distributional characteristics of bug prediction data to guide the training version selection. For model updating, we propose ConUpdate, which leverages the evolution of distributional characteristics of bug prediction data between versions to guide the reuse or update of bug prediction models in continuous software development. For model evaluation, we propose ConEA, which leverages the evolution of buggy probability of files between versions to conduct effort-aware evaluation.

Results: Experiments on 120 continuously release versions that span across six large-scale open-source software systems show the practical value of our approaches.

Conclusions: This paper provides new insights and guidelines for conducting software bug prediction in the context of continuous software development.

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Chowdhury S(2025)The Good, the Bad, and the Monstrous: Predicting Highly Change-Prone Source Code Methods at Their InceptionACM Transactions on Software Engineering and Methodology10.1145/3715006Online publication date: 23-Jan-2025
https://doi.org/10.1145/3715006
Chowdhury SUddin GHemmati HHolmes R(2024)Method-level Bug Prediction: Problems and PromisesACM Transactions on Software Engineering and Methodology10.1145/364033133:4(1-31)Online publication date: 13-Jan-2024
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Index Terms

Continuous Software Bug Prediction
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Empirical software validation
      2. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ESEM '21: Proceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

October 2021

368 pages

ISBN:9781450386654

DOI:10.1145/3475716

General Chair:
Filippo Lanubile

Copyright © 2021 ACM.

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Published: 11 October 2021

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ESEM '21: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement

October 11 - 15, 2021

Bari, Italy

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ESEM '21 Paper Acceptance Rate 24 of 124 submissions, 19%;

Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

Chowdhury S(2025)The Good, the Bad, and the Monstrous: Predicting Highly Change-Prone Source Code Methods at Their InceptionACM Transactions on Software Engineering and Methodology10.1145/3715006Online publication date: 23-Jan-2025
https://doi.org/10.1145/3715006
Chowdhury SUddin GHemmati HHolmes R(2024)Method-level Bug Prediction: Problems and PromisesACM Transactions on Software Engineering and Methodology10.1145/364033133:4(1-31)Online publication date: 13-Jan-2024
https://dl.acm.org/doi/10.1145/3640331
Tian ZTian BLv JChen YChen L(2024)Enhancing vulnerability detection via AST decomposition and neural sub-tree encodingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121865238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121865
Singh SMehrotra MBharti T(2023)Modeling Reliability Growth among Different Issue Types for Multi-Version Open Source Software2023 6th International Conference on Information Systems and Computer Networks (ISCON)10.1109/ISCON57294.2023.10112061(1-5)Online publication date: 3-Mar-2023
https://doi.org/10.1109/ISCON57294.2023.10112061
Frieyadie Abdullah MSetiawan F(2023)The Effectiveness of Resampling Method for Handling Class Imbalances in Software Defect Prediction2023 International Conference on Information Technology Research and Innovation (ICITRI)10.1109/ICITRI59340.2023.10249255(22-27)Online publication date: 16-Aug-2023
https://doi.org/10.1109/ICITRI59340.2023.10249255
Shin JAleithan RNam JWang JHarzevili NWang S(2023)An Empirical Study on the Stability of Explainable Software Defect Prediction2023 30th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC60848.2023.00024(141-150)Online publication date: 4-Dec-2023
https://doi.org/10.1109/APSEC60848.2023.00024
Yang FXu HXiao PZhong FZeng G(2023)A Method-Level Defect Prediction Approach Based on Structural Features of Method-Calling NetworkIEEE Access10.1109/ACCESS.2023.323926611(7933-7946)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3239266
Siva RS KHariharan BPremkumar N(2023)Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term MemoryWireless Personal Communications: An International Journal10.1007/s11277-023-10694-9132:3(1975-1998)Online publication date: 23-Aug-2023
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Rajendra VMotupalli R(2022)Analysis on the Estimators to the OSELM Model2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N56670.2022.10074089(1631-1634)Online publication date: 16-Dec-2022
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