LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction

Abstract

Software defect prediction plays a key role in guiding resource allocation for software testing. However, previous defect prediction studies still have some limitations: (1) the granularity of defect prediction is still coarse, so high-risk code statements cannot be accurately located; (2) in fine-grained defect prediction, the semantic and structural information available in a single line of code is limited, and the content of code semantic information is not sufficient to achieve semantic differentiation. To address the above problems, we propose a two-phase line-level defect prediction method based on deep learning called LineFlowDP. We first extract the program dependency graph (PDG) of the source files. The lines of code corresponding to the nodes in the PDG are extended semantically with data flow and control flow information and embedded as nodes, and the model is further trained using an relational graph convolutional network. Finally, a graph interpreter GNNExplainer and a social network analysis method are used to rank the lines of code in the defective file according to risk. On 32 datasets from 9 projects, the experimental results show that LineFlowDP is 13%-404% more cost-effective than four state-of-the-art line-level defect prediction methods. The effectiveness of the flow information extension and code line risk ranking methods was also verified via ablation experiments.