Abstract
In this paper, we present a novel approach to defect prediction within project source code. Since defect prediction datasets are typically imbalanced, and there are few defective examples, we treat defect prediction as anomaly detection. We present our Reconstruction Error Probability Distribution (REPD) model and compare it on five different datasets to five standardly used models: Gaussian Naive Bayes, Logistic regression, k-nearest-neighbors, decision tree, and SVM. For the main performance results we use F1-scores. Using statistical means, we show that our model produces significantly better results, improving F1-score up to 10.11%.
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