Predicting defects in SAP Java code: An experience report

TL;DR: The overall predictive power of these regression models is lower than expected; still, the resulting regression models successfully predicted 50–60% of the 20% most defect-prone components.

Abstract: Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. We found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50–60% of the 20% most defect-prone components.