代码缺陷检测中被测模块开销预测方法
A METHOD TO PREDICT THE COST OF TESTED MODULE IN CODE DEFECT DETECTION
严咏豪 1白汉利 2金大海 1王雅文1
作者信息
- 1. 北京邮电大学计算机学院 北京 100876
- 2. 中国空气动力研究与发展中心计算空气动力研究所 四川绵阳 621000
- 折叠
摘要
随着代码规模越来越大、代码文件越来越复杂,代码缺陷检测工具需要采用并行的方法进行调度.为了更好地使用并行的方法进行调度,提高缺陷检测效率和硬件资源利用率,提出一种代码缺陷检测中被测模块开销预测方法.该方法根据DTS(Defect Testing System)缺陷检测流程的特点提取出时间开销特征和空间开销特征,通过深度记忆网络提取出语义特征,将时间开销特征与语义特征进行融合得到融合特征,使用回归模型对融合特征进行时间开销的预测,对空间开销特征进行空间开销的预测.在8个开源C工程上的实验结果表明,该方法在开销预测方面有着较好的表现.
Abstract
With the increasing size of code and the increasing complexity of code files,code defect detection tools need to adopt parallel scheduling method for scheduling.In order to better use parallel method for scheduling and improve the efficiency of defect detection and utilization of hardware resources,we propose a method to predict the cost of the module tested in code defect detection.According to the characteristics of the defect testing system(DTS)defect detection process,the time cost feature and space cost feature were extracted.The semantic feature was extracted by deep memory network.The time cost feature and semantic feature were fused to get the fusion feature,and the regression model was used to predict the time cost of the fusion feature and the space cost of the space cost feature.Experimental results on 8 open source C projects show that the proposed method has a good performance in cost prediction.
关键词
代码缺陷检测/特征提取/深度记忆网络/开销预测Key words
Code defect detection/Feature extraction/Deep memory network/Cost prediction引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据