Use of relative code churn measures to predict system defect density
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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Churn rates can signal challenges such as higher defect density.
“Churn rates can signal challenges such as higher defect density [23];”
From Self-Admitted GenAI Usage in Open-Source Software (2026) · cites this paper as [23]
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An Index of Factorial Simplicity
Kaiser, H. F. (1974) · Psychometrika · doi:10.1007/bf02291575 · Cited by 14,898 · see citing works
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A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154 · see citing works
An exploratory study of the pull-based software development model
Gousios, G., Pinzger, M., & Deursen, A. V. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568260 · Cited by 628 · see citing works
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Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574 · see citing works
Predicting Defective Lines Using a Model-Agnostic Technique
Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., & Matsumoto, K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3023177 · Cited by 137 · see citing works
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture
Cai, Y., Xiao, L., Kazman, R., Mo, R., & Feng, Q. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2797899 · Cited by 39 · see citing works
Self-Admitted GenAI Usage in Open-Source Software
Xiao, T., Fan, Y., Calefato, F., Treude, C., Kula, R. G., Hata, H., & Baltes, S. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3681886 · Cited by 1 · see citing works
- Authors
- Nagappan, N., & Ball, T.
- Affiliations
- North Carolina State University (US)
- Microsoft (United States) (US)
- ORCID
- Nagappan, Nachiappan
- Venue
- Proceedings of the 27th international conference on Software engineering - ICSE '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 284
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2100945416
- Cited by
- 775
- References
- 1
- Citations by year
- 2026: 11 · 2025: 16 · 2024: 22 · 2023: 29 · 2022: 26 · 2021: 50 · 2020: 47 · 2019: 43 · 2018: 43 · 2017: 53 · 2016: 59 · 2015: 51 · 2014: 51 · 2013: 46 · 2012: 48
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Reliability and Analysis Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- code churn, software defect prediction, software evolution, component size, Windows Server 2003
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