Software defect prediction via cost-sensitive Siamese parallel fully-connected neural networks

Abstract

Software defect prediction (SDP) has caused widespread concern among software engineering researchers, which aims to erect a software defect predictor according to historical data. However, it is still difficult to develop an effective SDP model on high-dimensional and limited data. In this study, a novel SDP model for this problem is proposed, called Siamese parallel fully-connected networks (SPFCNN), which combines the advantages of Siamese networks and deep learning into a unified method. And training this model is administered by AdamW algorithm for finding the best weights. The minimum value of a singular formula is the target of training for SPFCNN model. Significantly, we extensively compared SPFCNN method with the state-of-the-art SDP approaches using six openly available datasets from the NASA repository. Six indexes are used to evaluate the performance of the proposed method. Experimental results showed that the SPFCNN method contributes to significantly higher performance compared with benchmarked SDP approaches, indicating that a cost-sensitive neural network could be developed successfully for SDP.