Estimating the feasibility of transition paths in extended finite state machines
Automated Software Engineering
Search-based test data generation from stateflow statecharts
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Generation of improved test cases from UML state diagram using genetic algorithm
Proceedings of the 4th India Software Engineering Conference
Transition coverage testing for simulink/stateflow models using messy genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A multi-objective evolutionary algorithm to obtain test cases with variable lengths
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information and Software Technology
Validation of SDL-based architectural design models using communication-based coverage criteria
Information and Software Technology
An improved test generation approach from extended finite state machines using genetic algorithms
SEFM'12 Proceedings of the 10th international conference on Software Engineering and Formal Methods
Computers in Biology and Medicine
Automatic generation of basis test paths using variable length genetic algorithm
Information Processing Letters
Hi-index | 0.00 |
The problem of testing from an extended finite state machine (EFSM) can be expressed in terms of finding suitable paths through the EFSM and then deriving test data to follow the paths. A chosen path may be infeasible and so it is desirable to have methods that can direct the search for appropriate paths through the EFSM towards those that are likely to be feasible. However, generating feasible transition paths (FTPs) for model based testing is a challenging task and is an open research problem. This paper introduces a novel fitness metric that analyzes data flow dependence among the actions and conditions of the transitions of a path in order to estimate its feasibility. The proposed fitness metric is evaluated by being used in a genetic algorithm to guide the search for FTPs.