Learning and integration of parameterized components through testing

Abstract

We investigate the use of parameterized state machine models to drive integration testing, in the case where the models of components are not available beforehand. Therefore, observations from tests are used to learn partial models of components, from which further tests can be derived for integration. We have extended previous algorithms to the case of finite state models with predicates on input parameters and observable non-determinism. We also propose a new strategy where integration tests can be derived from the data collected during the learning process. Our work typically addresses the problem of assembling telecommunication services from black box COTS. © IFIP- International Federation for Information Processing 2007.