Software defect prediction with semantic and structural information of codes based on Graph Neural Networks

Context: Most defect prediction methods consider a series of traditional manually designed static code metrics. However, only using these hand-crafted features is impractical. Some researchers use the Convolutional Neural Network (CNN) to capture the potential semantic information based on the program's Syntax Trees (ASTs). In recent years, leveraging the dependency relationships between software modules to construct a software network and using network embedding models to capture the structural information have been helpful in defect prediction. This paper simultaneously takes the semantic and structural information into account and proposes a method called CGCN. Objective: This study aims to validate the feasibility and performance of the proposed method in software defect prediction. Method: Abstract Syntax Trees and a Class Dependency Network (CDN) are first generated based on the source code. For ASTs, symbolic tokens are extracted and encoded into vectors. The numerical vectors are then used as input to the CNN to capture the semantic information. For CDN, a Graph Convolutional Network (GCN) is used to learn the structural information of the network automatically. Afterward, the learned semantic and structural information are combined with different weights. Finally, we concatenate the learned features with traditional hand-crafted features to train a classifier for more accurate defect prediction. Results: The proposed method outperforms the state-of-the-art defect prediction models for both within-project prediction (including within-version and cross-version) and cross-project prediction on 21 open-source projects. In general, within-version prediction achieves better performance in the three prediction tasks.Conclusion: The proposed method of combining semantic and structural information can improve the performance of software defect prediction.