A multi-objective evolutionary algorithm to obtain test cases with variable lengths
Thaise Yano, Eliane Martins, Fabiano Luís de Sousa · 2011
In this paper a new multi-objective implementation of the generalized extremal optimization (GEO) algorithm, named M-GEOvsl, is presented. It was developed primarily to be used as a test case generator to find transition paths from extended finite state machines (EFSM), taking into account not only the transition to be covered but also the minimization of the test length. M-GEOvsl has the capability to deal with strings whose number of elements vary dynamically, making it possible to generate solutions with different lengths. The steps of the algorithm are described for a general multi-objective problem in which the solution length is an element to be optimized. Experiments were performed to generate test case from EFSM benchmark models using M-GEOvsl and the approach was compared with a related work.