一种基于关联分析与N-Gram的错误参数检测方法

Association Analysis and N-Gram Based Detection of Incorrect Arguments

为了检测软件系统中存在错误参数的函数调用,提出了一种基于关联分析和N-Gram语言模型的静态检测方法(ANiaD).基于海量开源代码,构建了关联分析模型以挖掘参数间存在的强关联规则.针对参数间存在强关联规则的函数调用构建N-Gram语言模型.基于训练过的N-Gram模型,计算给定函数调用语句正确的概率.低概率的函数调用被报告为异常函数调用.基于10个开源Java项目对该方法进行实验验证.实验结果表明,该方法检测的查准率约43.40%,显著高于现有的基于相似度的检测方法(查准率25%).

To detect the method calls with incorrect arguments in software systems, an association analysis and N-Gram based static anomaly detection approach （ANiaD） is proposed. Based on the massive open source code, an association analysis model is constructed to mine the strong association rules between arguments. An N-Gram model is constructed for method calls with strong association rules between arguments. Using the trained N-Gram model, the probability of a given method call statement is calculated. Low probability method calls are reported as potential bugs. The proposed approach is evaluated based on 10 open-source Java projects. The results show that the accuracy of the proposed approach is about 43.40%, significantly greater than that of similarity-based approach （25% accuracy）.