Code churn estimation using organisational and code metrics: An experimental comparison

Abstract

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.