Learn More
This paper introduces a multi-objective evolutionary approach to test case generation from extended finite state machines (EFSM), named MOST. Testing from an (E)FSM generally involves executing various transition paths, until a given coverage criterion (e.g. cover all transitions) is met. As traditional test generation methods from FSM only consider the(More)
Search-based testing techniques using meta-heuristics, like evolutionary algorithms, has been largely used for test data generation, but most approaches were proposed for white-box testing. In this paper we present an evolutionary approach for test sequence generation from a behavior model, in particular, Extended Finite State Machine. An open problem is(More)
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(More)
  • T. Yano
  • 2006
This paper reports on the recent development and application of the multi dimensional system. At first, classify the multi dimensional drive system by the drive principle. The advantages of the multi dimensional drive systems are shown. The problems of the multi dimensional drive system are discussed. Researches of the multi dimensional drive systems are(More)
Since Critical Infrastructures (CI) strongly rely on network services, robustness testing is a key step to assure the resilience of such systems in the presence of abnormal situations. This work proposes a dynamic robustness test cases generation from state models, using an executable version of the model. A meta-heuristics search based algorithm is used to(More)
In this article a procedure to tune robust Generalized Predictive Controllers (GPC) is presented. To tune the controller parameters a multiobjective optimization problem is formulated so the designer can consider conflicting objectives simultaneously without establishing any prior preference. Moreover model uncertainty, represented by a list of possible(More)
  • 1