Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning

We present a semi-supervised approach for software defect prediction. The proposed method is designed to address the special problematic characteristics of software defect datasets, namely, lack of labeled samples and class-imbalanced data. To alleviate these problems, the proposed method features the following components. Being a semi-supervised approach, it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels, for each unlabeled sample. And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation, to ensure that the learned features (sparse code) are optimal for the trained classifier. Finally, during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance. Experimental results demonstrate that our method outperforms several representative state-of-the-art defect prediction methods.