Existing defect prediction models use product or process metrics and machine learning methods to identify defect-prone source code entities. Different classifiers (e.g., linear regression, logistic regression, or classification trees) have been investigated in the last decade. The results achieved so far are sometimes contrasting and do not show a clear winner. In this paper we present an empirical study aiming at statistically analyzing the equivalence of different defect predictors. We also propose a combined approach, coined as CODEP (COmbined DEfect Predictor), that employs the classification provided by different machine learning techniques to improve the detection of defect-prone entities. The study was conducted on 10 open source software systems and in the context of cross-project defect prediction, that represents one of the main challenges in the defect prediction field. The statistical analysis of the results indicates that the investigated classifiers are not equivalent and they can complement each other. This is also confirmed by the superior prediction accuracy achieved by CODEP when compared to stand-alone defect predictors.
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