Article

Finding failure-inducing changes in java programs using change classification

Authors:

Maximilian Stoerzer,

Barbara G. Ryder,

Frank TipAuthors Info & Claims

SIGSOFT '06/FSE-14: Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering

Pages 57 - 68

https://doi.org/10.1145/1181775.1181783

Published: 05 November 2006 Publication History

Abstract

Testing and code editing are interleaved activities during program development. When tests fail unexpectedly, the changes that caused the failure(s) are not always easy to find. We explore how change classification can focus programmer attention on failure-inducing changes by automatically labeling changes Red, Yellow, or Green, indicating the likelihood that they have contributed to a test failure. We implemented our change classification tool JUnit/CIA as an ex-tension to the JUnit component within Eclipse, and evaluated its effectiveness in two case studies. Our results indicate that change classification is an effective technique for finding failure-inducing changes.

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Cited By

Krüger JLi YLossev KZhu CChechik MBerger TRubin J(2024)A Meta-Study of Software-Change IntentionsACM Computing Surveys10.1145/366148456:12(1-41)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3661484
Elkholy MElfatatry A(2018)Change Taxonomy: A Fine-Grained Classification of Software ChangeIT Professional10.1109/MITP.2018.04314166620:4(28-36)Online publication date: Jul-2018
https://doi.org/10.1109/MITP.2018.043141666
Liu XHuang LEgyed AGe J(2018)Do code data sharing dependencies support an early prediction of software actual change impact set?Journal of Software: Evolution and Process10.1002/smr.196030:11Online publication date: 14-Nov-2018
https://dl.acm.org/doi/10.1002/smr.1960
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Index Terms

Finding failure-inducing changes in java programs using change classification
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software version control
    2. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software notations and tools
    1. Software configuration management and version control systems

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cover image ACM Conferences

SIGSOFT '06/FSE-14: Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering

November 2006

298 pages

ISBN:1595934685

DOI:10.1145/1181775

General Chair:
Michal Young
University of Oregon
,
Program Chair:
Premkumar Devanbu
University of California, Davis

Copyright © 2006 ACM.

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Published: 05 November 2006

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SIGSOFT06/FSE-14: SIGSOFT 2006 -14th International Symposium on the Foundations of Software Engineering

November 5 - 11, 2006

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Cited By

Krüger JLi YLossev KZhu CChechik MBerger TRubin J(2024)A Meta-Study of Software-Change IntentionsACM Computing Surveys10.1145/366148456:12(1-41)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3661484
Elkholy MElfatatry A(2018)Change Taxonomy: A Fine-Grained Classification of Software ChangeIT Professional10.1109/MITP.2018.04314166620:4(28-36)Online publication date: Jul-2018
https://doi.org/10.1109/MITP.2018.043141666
Liu XHuang LEgyed AGe J(2018)Do code data sharing dependencies support an early prediction of software actual change impact set?Journal of Software: Evolution and Process10.1002/smr.196030:11Online publication date: 14-Nov-2018
https://dl.acm.org/doi/10.1002/smr.1960
Soetens QRobbes RDemeyer S(2017)Changes as First-Class CitizensACM Computing Surveys10.1145/303892650:2(1-38)Online publication date: 6-Apr-2017
https://dl.acm.org/doi/10.1145/3038926
Kreutzer PDotzler GRing MEskofier BPhilippsen MKim MRobbes RBird C(2016)Automatic clustering of code changesProceedings of the 13th International Conference on Mining Software Repositories10.1145/2901739.2901749(61-72)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2901739.2901749
Ye XBunescu RLiu C(2016)Mapping Bug Reports to Relevant FilesIEEE Transactions on Software Engineering10.1109/TSE.2015.247923242:4(379-402)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1109/TSE.2015.2479232
Gao QLi JXiong YHao DXiao XTaneja KZhang LXie T(2016)High-confidence software evolutionScience China Information Sciences10.1007/s11432-016-5572-259:7Online publication date: 13-Jun-2016
https://doi.org/10.1007/s11432-016-5572-2
Herzig KJust SZeller A(2016)The impact of tangled code changes on defect prediction modelsEmpirical Software Engineering10.1007/s10664-015-9376-621:2(303-336)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1007/s10664-015-9376-6
Alves EMachado PMassoni TKim M(2016)Prioritizing test cases for early detection of refactoring faultsSoftware Testing, Verification & Reliability10.1002/stvr.160326:5(402-426)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1002/stvr.1603
Tang XWang SMao K(2015)Will This Bug-Fixing Change Break Regression Testing?2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1109/ESEM.2015.7321218(1-10)Online publication date: Oct-2015
https://doi.org/10.1109/ESEM.2015.7321218
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