A Hybrid Test Generation Approach Based on Extended Finite State Machines


This paper presents a hybrid test generation approach from extended finite state machines combining genetic algorithms with local search techniques. Many test generation methods (both functional and structural testing methods) use genetic algorithms. Genetic algorithms may take a long time to converge to a global optimum and for a huge neighborhood they can be inefficient or unsuccessful. In this paper we use hybrid genetic algorithms to generate test data for some chosen paths for extended finite state machines. Local search is applied to improve the best individual for each generation of the genetic algorithm.

18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2016)