research-article

Learning-to-rank vs ranking-to-learn: strategies for regression testing in continuous integration

Authors:

Antonia Bertolino,

Antonio Guerriero,

Roberto Pietrantuono,

Stefano RussoAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

Pages 1 - 12

https://doi.org/10.1145/3377811.3380369

Published: 01 October 2020 Publication History

Abstract

In Continuous Integration (CI), regression testing is constrained by the time between commits. This demands for careful selection and/or prioritization of test cases within test suites too large to be run entirely. To this aim, some Machine Learning (ML) techniques have been proposed, as an alternative to deterministic approaches. Two broad strategies for ML-based prioritization are learning-to-rank and what we call ranking-to-learn (i.e., reinforcement learning). Various ML algorithms can be applied in each strategy. In this paper we introduce ten of such algorithms for adoption in CI practices, and perform a comprehensive study comparing them against each other using subjects from the Apache Commons project. We analyze the influence of several features of the code under test and of the test process. The results allow to draw criteria to support testers in selecting and tuning the technique that best fits their context.

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Cited By

Wang JWang KNie PFilkov VRay BZhou M(2024)Efficient Incremental Code Coverage Analysis for Regression Test SuitesProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695551(1882-1894)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695551
ÖZER EBUZLUCA F(2024)Test Case Prioritization For Embedded SoftwareProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651794(81-89)Online publication date: 1-Feb-2024
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Wang GSun ZChen YZhao YLiang QHao DChristakis MPradel M(2024)Commit Artifact Preserving Build PredictionProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680356(1236-1248)Online publication date: 11-Sep-2024
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Index Terms

Learning-to-rank vs ranking-to-learn: strategies for regression testing in continuous integration
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

June 2020

1640 pages

ISBN:9781450371216

DOI:10.1145/3377811

General Chairs:
Gregg Rothermel
North Carolina State University
,
Doo-Hwan Bae
KAIST, South Korea

Copyright © 2020 ACM.

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Published: 01 October 2020

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Cited By

Wang JWang KNie PFilkov VRay BZhou M(2024)Efficient Incremental Code Coverage Analysis for Regression Test SuitesProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695551(1882-1894)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695551
ÖZER EBUZLUCA F(2024)Test Case Prioritization For Embedded SoftwareProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651794(81-89)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3651781.3651794
Wang GSun ZChen YZhao YLiang QHao DChristakis MPradel M(2024)Commit Artifact Preserving Build PredictionProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680356(1236-1248)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680356
Douglas Ferrari Mendonça WAssunção WVergilio S(2024)Feature-oriented Test Case Prioritization Strategies: An Evaluation for Highly Configurable SystemsProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672592(72-83)Online publication date: 2-Sep-2024
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Khan MAzim ALiscano RSmith KChang YTauseef QSeferi GSaadatmand MLonetti FBudnik CLi JGuerriero A(2024)Machine Learning-based Test Case Prioritization using Hyperparameter OptimizationProceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)10.1145/3644032.3644467(125-135)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3644032.3644467
Zeng ZXiao TLamothe MHata HMcintosh SSpinellis DConstantinou EBacchelli A(2024)A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBaseProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644914(556-568)Online publication date: 15-Apr-2024
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Wang XAli SYue TArcaini P(2024)Quantum Approximate Optimization Algorithm for Test Case OptimizationIEEE Transactions on Software Engineering10.1109/TSE.2024.3479421(1-16)Online publication date: 2024
https://doi.org/10.1109/TSE.2024.3479421
Zhang ZChen JGu YLi ZSosu R(2024)Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression TestingIET Software10.1049/2024/99429592024(1-14)Online publication date: 4-Apr-2024
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Mendonça WAssunção WVergilio S(2024)Feature-oriented test case selection and prioritization during the evolution of highly-configurable systemsJournal of Systems and Software10.1016/j.jss.2024.112157217(112157)Online publication date: Nov-2024
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Roza EPrado Lima JVergilio S(2024)On the use of contextual information for machine learning based test case prioritization in continuous integration developmentInformation and Software Technology10.1016/j.infsof.2024.107444171:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107444
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