A Fine-Grained Defect Prediction Method Based on Drift-Immune Graph Neural Networks  

作　　者：Fengyu Yang Fa Zhong Xiaohui Wei Guangdong Zeng 

机构地区：[1]School of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016,China [2]School of Software,Nanchang Hangkong University,Nanchang,330029,China

出　　处：《Computers, Materials & Continua》2025年第2期3563-3590,共28页计算机、材料和连续体(英文)

基　　金：The authors would like to express appreciation to the National Natural Science Foundation of China(Grant No.61762067)for their financial support.

摘　　要：The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.

关 键 词：Software defect prediction data drift graph neural networks information bottleneck 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]