Article

Use of relative code churn measures to predict system defect density

Authors:

Nachiappan Nagappan,

Thomas BallAuthors Info & Claims

ICSE '05: Proceedings of the 27th international conference on Software engineering

Pages 284 - 292

https://doi.org/10.1145/1062455.1062514

Published: 15 May 2005 Publication History

Abstract

Software systems evolve over time due to changes in requirements, optimization of code, fixes for security and reliability bugs etc. Code churn, which measures the changes made to a component over a period of time, quantifies the extent of this change. We present a technique for early prediction of system defect density using a set of relative code churn measures that relate the amount of churn to other variables such as component size and the temporal extent of churn.Using statistical regression models, we show that while absolute measures of code churn are poor predictors of defect density, our set of relative measures of code churn is highly predictive of defect density. A case study performed on Windows Server 2003 indicates the validity of the relative code churn measures as early indicators of system defect density. Furthermore, our code churn metric suite is able to discriminate between fault and not fault-prone binaries with an accuracy of 89.0 percent.

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Cited By

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https://doi.org/10.1016/j.jss.2024.112263
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Kaur JKaur A(2025)Exploring Defect Density Prediction: A Comparative Analysis of Feature Reduction Techniques and Staking MethodologyIntelligent Solutions for Smart Adaptation in Digital Era10.1007/978-981-97-8193-5_4(39-50)Online publication date: 14-Feb-2025
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cover image ACM Conferences

ICSE '05: Proceedings of the 27th international conference on Software engineering

May 2005

754 pages

ISBN:1581139632

DOI:10.1145/1062455

General Chair:
Gruia-Catalin Roman
Washington University in St. Louis
,
Program Chairs:
William Griswold
University of California, San Diego
,
Bashar Nuseibeh
The Open University, UK

Copyright © 2005 ACM.

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Published: 15 May 2005

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May 15 - 21, 2005

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https://dl.acm.org/doi/10.1007/s10664-024-10600-2
Kaur JKaur A(2025)Exploring Defect Density Prediction: A Comparative Analysis of Feature Reduction Techniques and Staking MethodologyIntelligent Solutions for Smart Adaptation in Digital Era10.1007/978-981-97-8193-5_4(39-50)Online publication date: 14-Feb-2025
https://doi.org/10.1007/978-981-97-8193-5_4
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