Software defect prediction based on collaborative representation classification
Xiao‐Yuan Jing, Zhiwu Zhang, Shi Ying, Feng Wang, Yangping Zhu · 2014
In recent years, machine learning techniques have been successfully applied into software defect prediction. Although they can yield reasonably good prediction results, there still exists much room for improvement on the aspect of prediction accuracy. Sparse representation is one of the most advanced machine learning techniques. It performs well with respect to signal compression and classification, but suffers from its time-consuming sparse coding. Compared with sparse representation, collaborative representation classification (CRC) can yield significantly lower computational complexity and competitive classification performance in pattern recognition domains. To achieve better defect prediction results, we introduce the CRC technique in this paper and propose a CRC based software defect prediction (CSDP) approach. We first design a CRC based learner to build a prediction model, whose computational burden is low. Then, we design a CRC based predictor to classify whether the query software modules are defective or defective-free. Experimental results on the widely used NASA datasets demonstrate the effectiveness and efficiency of the proposed approach.