It achieves the best recall, FN-R, G-mean1 and balance on 9 out of the 10 datasets, and F-measure and J-coefficient on 7 out of the 10 datasets. The results show that Bayesian network achieves a noteworthy performance. We evaluate the performance of defect prediction approaches on 10 defect datasets from PROMISE repository. Our goal is to explore a practical and sophisticated way for evaluating the prediction approaches comprehensively. And we evaluate these 6 approaches on 14 evaluation metrics (e.g., G-mean, F-measure, balance, MCC, J-coefficient, and AUC). We choose 6 state-of-the-art approaches including naive Bayes, decision tree, logistic regression, kNN, random forest and Bayesian network which are widely used in defect prediction literature. In this paper, we investigate the performance of within-project defect prediction approaches on a large number of evaluation metrics. However, in machine learning literature, there are a large number of evaluation metrics to evaluate the performance of an algorithm, (e.g., Matthews Correlation Coefficient, G-means, etc.), and these metrics evaluate an approach from different aspects. To evaluate the performance of a within-project defect prediction approach, people normally use precision, recall, and F-measure scores.
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