A Software Defect Prediction Method Based on Dynamic Path Adaptive Graph Convolutional Networks

Abstract

This application provides a software defect prediction method based on a dynamic path adaptive graph convolutional network. The method includes: constructing graph structure data of software modules; inputting the graph structure data into a graph convolutional network to learn the dependencies between modules; adaptively assigning weights to multi-hop paths through a dynamic path weighted convolutional layer and suppressing long path noise interference based on a path length decay coefficient; optimizing the adjacency matrix in real time based on a multi-head attention mechanism, enhancing key dependency edges and pruning redundant connections; embedding dynamic path scoring and an adaptive graph update algorithm into a classification objective function, updating training weights with a balancing strategy, and outputting defect prediction results through the optimized model. This application can effectively solve the performance degradation problems of traditional graph convolutional networks in scenarios with insufficient dependency modeling, noise accumulation, and class imbalance under the static graph assumption.

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