A Comprehensive Technique to Predict the Size of Maintenance Issues

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 represents the sum of added, modified and deleted lines of code 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 churn needed to perform a maintenance issue might be useful when assessing the complexity and the possible hidden risks behind that maintenance issue (e.g., this task is critical, so it should be tested more or additional resources are needed).In this paper, we present a novel code churn prediction model, that uses a mix of product, process, and developer-related factors never used in this context to output a nominal value indicating an estimate of the category of code churn for a given maintenance issue. We employ the model in a large-scale empirical study involving 17 open-source software systems, comparing it with baselines relying on (i) only product, (ii) only process, and (iii) a combination of product and process metrics. We show that the proposed model is pretty accurate in the estima- tions reaching up to 70% of F-Measure and 80% of AUC-ROC. Furthermore, its performance is statistically better than the one of other baseline models in 88% of the cases.