计算机应用与软件2024，Vol.41Issue(8)：9-16,35,9.DOI:10.3969/j.issn.1000-386x.2024.08.002
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
摘要
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分类
计算机与自动化引用本文复制引用
严咏豪,白汉利,金大海,王雅文..代码缺陷检测中被测模块开销预测方法[J].计算机应用与软件,2024,41(8):9-16,35,9.基金项目
国家数值风洞工程项目. （）
