LAUSR

Recognizing and repairing defects to enhance quality in software life circle has become a critical research topic. Unfortunately, it is difficult to guarantee the validity of the defect prediction method based on manually designed features proposed in previous studies. Numerous scholars have endeavored to use a single model to obtain prediction results for different types of fault, but this is difficult to perform. This article improves the defect representation and prediction model in software defect prediction, proposing Augmented-Code Property Graph (CPG) based defect prediction method (ACGDP). Augmented-CPG is a novel encoding graph format introduced in this article. Based on Augmented-CPG, we suggested defect region candidate extraction approach linked to the defect category. Graph neural networks are used for obtaining defect characteristics. Experiments on three distinct types of defects indicate that ACGDP can predict certain classed of defects effectively.