Empirical Study in using Version Histories for Change Risk Classification

Many techniques have been proposed for mining software repositories, predicting code quality and evaluating code changes. Prior work has established links between code ownership and churn metrics, and software quality at file and directory level based on changes that fix bugs. Other metrics have been used to evaluate individual code changes based on preceding changes that induce fixes. This paper combines the two approaches in an empirical study of assessing risk of code changes using established code ownership and churn metrics with fix inducing changes on a large proprietary code repository. We establish a machine learning model for change risk classification which achieves average precision of 0.76 using metrics from prior works and 0.90 using a wider array of metrics. Our results suggest that code ownership metrics can be applied in change risk classification models based on fix inducing changes.