Graph Neural Network Defect Prediction Method Combined with Developer Dependencies

In the software development process,timely identification and handling of high-risk defect modules are crucial.Traditional software defect prediction methods primarily rely on code-related information but often overlook the impact of developers' personal characteristics on software quality.To address this issue,this study proposes a novel software defect prediction model,DCN4SDP,which incorporates a developer consistency dependency network.This model first constructs a developer consistency dependency network using developer information and extracts code-related metrics as initial features for the network.It then employs a bidirectional gated graph neural network(BiGGNN) to learn the node features within the network structure.Experimental results demonstrate that the DCN4SDP model significantly outperforms traditional machine learning classifiers and other deep learning methods on multiple standard datasets.For instance,the DCN4SDP achieves an AUC value of 0.91 and a F1 score of 0.76,both notably higher than those of other compared models.These advantages indicate that integrating the developer dimension into software defect prediction can effectively enhance the model's predictive capabilities and practical value,providing new insights and directions for future research in software defect prediction.