A Comprehensive Technique to Predict the Size of Maintenance Tasks

Software systems continuously evolve over time because of changes in the requirements, code refactoring, or bug fixing activities. A way to quantify the extent of a change is given by code churn, that represent the number of lines of code changed by a developer to perform such a change. Previous research showed that code churn can be adopted by practitioners to perform early evaluation of defect density, presence of vulnerabilities, or to simply monitor the impact of a code change. We argue that an automated software analytics technique able to inform developers of the quantity of code needed to perform a maintenance task might be useful when estimating the likely effort needed to issue it or assessing the possible hidden risks. In this paper, we present a novel code churn prediction model, that uses a mix of product, process, and developer-related factors to output a nominal value indicating an estimate of the code churn for a given maintenance task. We employ the model in a large-scale empirical study involving 17 open-source software sys- tems, comparing it with baselines relying on (i) only product, (ii) only pro- cess, and (iii) a combination of product and process metrics. We show that the proposed model is pretty accurate in the estimations reaching up to 70% of F-Measure and 80% of AUC-ROC. Furthermore, it is statistically better than other baseline models in 88% of the cases.