基于改进GN算法的程序控制流图划分方法

The accuracy and efficiency of program analyses are hindered by very large control flow graphs (CFG). This paper presents an improved GN (Girvan-Newman) algorithm for CFG division. The node weights are added as parameters to the betweenness calculation to better balance the subgraph sizes with the sizes controlled dynamically to terminate the algorithm at a suitable time to improve the execution efficiency. Then, the binary programs indicated by the CFGs are analyzed using the angr tool. The improved GN algorithm, K-means algorithm, spectral clustering algorithm and naive aggregation algorithm were all tested with the results showing the improved GN algorithm provided the best modularity and subgraph size balance.