.png){kind=icon}
返回
ACGDP: An Augmented Code Graph-Based System for Software Defect Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TR.2022.3161581.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Software
Codes
Predictive models
Feature extraction
Syntactics
Semantics
Data models
Code representation
defect types
graph neural networks
software defect prediction
期刊
IF:
5.7
论文数:
2.8K
被引数:
8.5K
机构
引用论文
.png){kind=icon}
VulDeeLocator: A Deep Learning-Based Fine-Grained Vulnerability DetectorVulDeeLocator: 一种基于深度学习的细粒度漏洞检测器
OAAI
OAAI分享收藏SySeVR: A Framework for Using Deep Learning to Detect Software VulnerabilitiesSySeVR: 使用深度学习检测软件漏洞的框架OAAI
OAAI分享收藏
查看更多内容