A Test Suite Generation Approach based on EFSMs using a multi-objective algorithm


Using extended finite state machines for test data generation can be a difficult process because we need to generate paths that are feasible and we also need to find input data that traverse a given path. This paper presents a test suite generation algorithm for extended finite state machines. The algorithm produces a set of feasible transition paths that cover all transitions using a modified multi-objective genetic algorithm (deleting redundant paths and shortening the solutions). The multi-objective problem aims to optimize the transitions coverage and the path feasibility, based on dataflow dependencies. Having a set of paths resulted from this algorithm, we can easily find input parameters for each path.

19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2017)