一种基于关联分析与N-Gram 的错误参数检测方法

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).