基于复杂网络和图形神经网络的软件缺陷预测模型的研究
Mengtian Cui1, Songlin Long1, Yue Jiang1
1Key Laboratory of Computer System, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一个新的图形神经网络 (GNN) 框架用于软件缺陷预测,通过考虑模块连接来提高准确性. 与传统方法相比,GNN模型增强了缺陷预测指标.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
背景情况:
- 传统的软件缺陷预测模型主要分析代码特征,忽视模块之间的关系.
- 了解软件模块的依赖关系对于准确的缺陷预测至关重要.
研究的目的:
- 从复杂网络的角度提出使用图形神经网络 (GNN) 的新软件缺陷预测框架.
- 为了利用软件模块之间的关系,以便更有效地预测缺陷.
主要方法:
- 将软件表示为图形,类作为节点,依赖作为边缘.
- 使用社区检测算法将软件图分为子图.
- 使用改进的GNN模型来学习节点表示向量用于缺陷分类.
主要成果:
- 拟议的GNN框架在PROMISE数据集上的准确性,F-测量和马修斯相关系数 (MCC) 显著改善.
- 在GNN中,光谱和空间域图形卷积方法都比基准模型表现得更好.
- 平均指标改进幅度在6.3%至17.5%之间,具体取决于所使用的具体指标和卷积方法.
结论:
- 图形神经网络方法有效地捕捉了模块间的依赖关系,从而提高了软件缺陷的预测.
- 这种复杂的网络视角为推进软件缺陷预测方法提供了一个有希望的方向.
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