Software Defect Prediction Method Based on Multi-Feature Fusion

Software defect prediction plays a vital role in software development. During the process of software updates and iterations, it helps developers anticipate potential defects in advance, thereby reducing unnecessary consumption of human, material, and time resources, while optimizing the allocation efficiency of Software Quality Assurance (SQA) resources. Given the widespread use of generative artificial intelligence in code development, effective software defect prediction has become increasingly important. Most previous studies primarily constructed defect prediction models using traditional metric-based features. However, in recent years, research focusing on semantic feature-based defect prediction has gained increasing attention, with researchers leveraging deep learning to automatically extract deep semantic information from source code. Nevertheless, existing approaches often rely on a single type of source code representation, overlooking the advantages and potential contributions of diverse features. To address this issue, this paper proposes a software defect prediction method based on multi-feature fusion, which incorporates multiple types of code representations to capture semantic information from different perspectives. The proposed model, DP-TACT, utilizes a multi-scale Convolutional Neural Network (Multiscale CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network to process Abstract Syntax Tree (AST) and Full-token features. It also employs a Multi-head SelfAttention mechanism to capture critical information. Additionally, a Graph Convolutional Network (GCN) is used to process Control Flow Graph (CFG) features. Finally, traditional features are integrated to validate the effectiveness of the feature fusion strategy. The model is evaluated on the PROMISE dataset, and experimental results demonstrate that the proposed approach outperforms baseline models across multiple metrics.