Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-Analysis

Abstract

Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally rest on the relationships between metric values and defect-proneness of classes. The objective of this study is to empirically examine whether there are effective threshold values of OO metrics by analyzing existing threshold derivation methods with a large-scale meta-analysis. Based on five representative threshold derivation methods (i.e. VARL, ROC, BPP, MFM, and MGM) and 3268 releases from 65 Java projects, we first employ statistical meta-analysis and sensitivity analysis techniques to derive thresholds for 62 OO metrics on the training data. Then, we investigate the predictive performance of five candidate thresholds for each metric on the validation data to explore which of these candidate thresholds can be served as the threshold. Finally, we evaluate their predictive performance on the test data. The experimental results show that 26 of 62 metrics have the threshold effect and the derived thresholds by meta-analysis achieve promising results of GM values and significantly outperform almost all five representative (baseline) thresholds.