Pruning Infeasible Paths via Graph Transformations and Symbolic Execution: a Method and a Tool

Path-biased random testing is an interesting alternative to classical path-based approaches faced to the explosion of the number of paths, and to the weak structural coverage of random methods based on the input domain only. Given a graph representation of the system under test a probability distribution on paths of a certain length is computed and then used for drawing paths. A limitation of this approach, similarly to other methods based on symbolic execution and static analysis, is the existence of infeasible paths that often leads to a lot of unexploitable drawings. We present a prototype for pruning some infeasible paths, thus eliminating useless drawings. It is based on graph transformations that have been proved to preserve the actual behaviour of the program. It is driven by symbolic execution and heuristics that use detection of subsumptions and the abstract-check-refine paradigm. The approach is illustrated on some detailed examples.