Using Statistical Models to Predict Software Regressions

Abstract

Incorrect changes made to the stable parts of a software system can cause failures - software regressions. Early detection of faulty code changes can be beneficial for the quality of a software system when these errors can be fixed before the system is released. In this paper, a statistical model for predicting software regressions is proposed. The model predicts risk of regression for a code change by using software metrics: type and size of the change, number of affected components, dependency metrics, developerpsilas experience and code metrics of the affected components. Prediction results could be used to prioritize testing of changes: the higher is the risk of regression for the change, the more thorough testing it should receive.