Abstract

In order to improve the efficiency of program slicing, eliminate the interference of irrelevant statements on defect detection, and solve the program of incomplete slicing or overly sensitive slice contours to dependencies, an inter-process slicing method based on control flow variable dependency graph(CFVDG) is proposed. The results show that compared with the SDG and DFG, the proportion of node reduction to the number of lines of code on datasets such as schedule is 42.7% and 3.7% on average.

Background: Slicing technology focuses on data association behavior and is suitable for variable dependency analysis in tight defect contexts.

Methods: By constructing a control flow variable dependency graph(CFVDG), performing slicing and semantic analysis and understanding, finally applied to detect defection.

Results: Compared with the slicing methods based on SDG and DFG, the consumption of time and space has decreased.

Conclusion: The experimental results show that this method can improve the precision of defect detection and reduce the false alarm rate, while reasonably and effectively shortening the time for graph construction and slicing, and reducing the consumption of edge storage space.

Keywords: Variable dependency, control flow, code slicing, context sensitive, defect characteristics, constraint rules.