Article

Change Bursts as Defect Predictors

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Brendan MurphyAuthors Info & Claims

ISSRE '10: Proceedings of the 2010 IEEE 21st International Symposium on Software Reliability Engineering

Pages 309 - 318

https://doi.org/10.1109/ISSRE.2010.25

Published: 01 November 2010 Publication History

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Abstract

In software development, every change induces a risk. What happens if code changes again and again in some period of time? In an empirical study on Windows Vista, we found that the features of such change bursts have the highest predictive power for defect-prone components. With precision and recall values well above 90%, change bursts significantly improve upon earlier predictors such as complexity metrics, code churn, or organizational structure. As they only rely on version history and a controlled change process, change bursts are straight-forward to detect and deploy.

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Pan WWu WMing HKim DYang ZMa Y(2024)Toward the Fractal Dimension of ClassesACM Transactions on Software Engineering and Methodology10.1145/368884434:2(1-50)Online publication date: 16-Aug-2024
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Oueslati KLaberge GLamothe MKhomh F(2024)Mining Action Rules for Defect Reduction PlanningProceedings of the ACM on Software Engineering10.1145/36608091:FSE(2309-2331)Online publication date: 12-Jul-2024
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Lee GXu DSalimi SLee BPayer MQuek TGao DZhou JCardenas A(2024)SyzRisk: A Change-Pattern-Based Continuous Kernel Regression FuzzerProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637642(1480-1494)Online publication date: 1-Jul-2024
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ISSRE '10: Proceedings of the 2010 IEEE 21st International Symposium on Software Reliability Engineering

November 2010

419 pages

ISBN:9780769542553

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 November 2010

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Pan WWu WMing HKim DYang ZMa Y(2024)Toward the Fractal Dimension of ClassesACM Transactions on Software Engineering and Methodology10.1145/368884434:2(1-50)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3688844
Oueslati KLaberge GLamothe MKhomh F(2024)Mining Action Rules for Defect Reduction PlanningProceedings of the ACM on Software Engineering10.1145/36608091:FSE(2309-2331)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660809
Lee GXu DSalimi SLee BPayer MQuek TGao DZhou JCardenas A(2024)SyzRisk: A Change-Pattern-Based Continuous Kernel Regression FuzzerProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637642(1480-1494)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3637642
Ahmad MGoseva-Popstojanova KLutz R(2024)The untold impact of learning approaches on software fault-proneness predictions: an analysis of temporal aspectsEmpirical Software Engineering10.1007/s10664-024-10454-829:4Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1007/s10664-024-10454-8
Reck JBach TStoess JChandra SBlincoe KTonella P(2023)A Multidimensional Analysis of Bug Density in SAP HANAProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613875(1997-2007)Online publication date: 30-Nov-2023
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Lipp SElsner DKacianka SPretschner ABöhme MBanescu SJust RFraser G(2023)Green Fuzzing: A Saturation-Based Stopping Criterion using Vulnerability PredictionProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598043(127-139)Online publication date: 12-Jul-2023
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Popoola SGray JGarcia-Dominguez AKolovos DKühn TSousa V(2022)Analyzing model changes with LoupeProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3561564(519-528)Online publication date: 23-Oct-2022
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Majumder SXia TKrishna RMenzies TLo DMcIntosh SNovielli N(2022)Methods for stabilizing models across large samples of projects (with case studies on predicting defect and project health)Proceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3527934(566-578)Online publication date: 23-May-2022
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Gesi JLi JAhmed ILanubile F(2021)An Empirical Examination of the Impact of Bias on Just-in-time Defect PredictionProceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1145/3475716.3475791(1-12)Online publication date: 11-Oct-2021
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Vogel-Heuser BNeumann EFischer J(2021)MICOSE4aPS: Industrially Applicable Maturity Metric to Improve Systematic Reuse of Control SoftwareACM Transactions on Software Engineering and Methodology10.1145/346789631:1(1-24)Online publication date: 28-Sep-2021
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Finding Predictors of Field Defects for Open Source Software Systems in Commonly Available Data Sources: A Case Study of OpenBSD

Which process metrics can significantly improve defect prediction models? An empirical study

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