Object-oriented metrics that predict maintainability
Journal of Systems and Software - Special issue on object-oriented software
Software evolution: code delta and code churn
Journal of Systems and Software - Special issue on software maintenance
Software development cost estimation approaches – A survey
Annals of Software Engineering
Code Churn: A Measure for Estimating the Impact of Code Change
ICSM '98 Proceedings of the International Conference on Software Maintenance
Detection of software modules with high debug code churn in a very large legacy system
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Use of relative code churn measures to predict system defect density
Proceedings of the 27th international conference on Software engineering
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
The influence of organizational structure on software quality: an empirical case study
Proceedings of the 30th international conference on Software engineering
An application of Bayesian network for predicting object-oriented software maintainability
Information and Software Technology
Feature construction and selection using genetic programming and a genetic algorithm
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A study of language usage evolution in open source software
Proceedings of the 8th Working Conference on Mining Software Repositories
Predicting the maintainability of XSL transformations
Science of Computer Programming
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Context: Source code revision control systems contain vast amounts of data that can be exploited for various purposes. For example, the data can be used as a base for estimating future code maintenance effort in order to plan software maintenance activities. Previous work has extensively studied the use of metrics extracted from object-oriented source code to estimate future coding effort. In comparison, the use of other types of metrics for this purpose has received significantly less attention. Objective: This paper applies machine learning techniques to unveil predictors of yearly cumulative code churn of software projects on the basis of metrics extracted from revision control systems. Method: The study is based on a collection of object-oriented code metrics, XML code metrics, and organisational metrics. Several models are constructed with different subsets of these metrics. The predictive power of these models is analysed based on a dataset extracted from eight open-source projects. Results: The study shows that a code churn estimation model built purely with organisational metrics is superior to one built purely with code metrics. However, a combined model provides the highest predictive power. Conclusion: The results suggest that code metrics in general, and XML metrics in particular, are complementary to organisational metrics for the purpose of estimating code churn.