An empirical evaluation of defect prediction approaches in within-project and cross-project context

The software defect prediction approaches are evaluated, in within-project context only, with only a few other approaches, according to distinct scenarios and performance indicators. So, we conduct various experiments to evaluate well-known defect prediction approaches using different performance indicators. The evaluations are performed in the scenario of ranking the entities - with and without considering the effort to review the entities and classifying entities in within-project as well as cross-project contexts. The effect of imbalanced datasets on the ranking of the approaches is also evaluated. Our results indicate that in within-project as well as cross-project context, process metrics, the churn of source code, and entropy of source code perform significantly better under the context of classification and ranking - with and without effort consideration. The previous defect metrics and other single metric approaches (like lines of code) perform worst. The ranking of the approaches is not changed by imbalanced datasets. We suggest using the process metrics, the churn of source code, and entropy of source code metrics as predictors in future defect prediction studies and taking care while using the single metric approaches as predictors. Moreover, different evaluation scenarios generate different ordering of approaches in within-project and cross-project contexts. Therefore, we conclude that each problem context has distinct characteristics, and conclusions of within-project studies should not be generalized to cross-project context and vice versa.