GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method

Abstract

Constraint solving has been applied to many domains of program analysis and is further used in concurrent program analysis. Concurrent programs have been widely used with the rapid development of multi-core processors. However, concurrent bugs threaten the security and reliability of concurrent programs, and thus it is of great importance to detect concurrent bugs. The explosion of thread interleaving caused by the uncertainty of the execution of concurrent program threads brings some challenges to the detection of concurrent bugs. Existing concurrent defect detection algorithms reduce the exploration cost in the state space of concurrent programs by reducing invalid thread interleaving. For example, the maximal causal model algorithm transforms the state space exploration problem of concurrent programs into a constraint solving problem. However, it will produce a large number of redundant and conflicting constraints during constraint construction, which greatly prolongs the time of constraint solving, increases the number of constraint solver calls, and reduces the exploration efficiency of concurrent program state space. Thus, this study proposes a directed graph constraint-guided maximal causality reduction method, called GC-MCR. This method aims to improve the speed of constraint solving and the efficiency of the state space exploration of concurrent programs by filtering and reducing constraints using directed graphs. The experimental results show that the GC-MCR method can effectively optimize the expression of constraints, so as to improve the solving speed of the constraint solver and reduce the number of solver calls. Compared with the existing J-MCR method, GC-MCR can significantly improve the detection efficiency of concurrent program bugs without reducing the detection ability of concurrent bugs, and the test time on 38 groups of concurrent test programs widely used by existing research methods can be reduced by 34.01% on average.