Incremental Learning and Testing of Reactive Systems
Abstract: This thesis concerns the design, implementation and evaluation of a specification based testing architecture for reactive systems using the paradigm of learning-based testing. As part of this work we have designed, verified and implemented new incremental learning algorithms for DFA and Kripke structures.These have been integrated with the NuSMV model checker to give a new learning-based testing architecture. We have evaluated our architecture on case studies and shown that the method is effective.
CLICK HERE TO DOWNLOAD THE WHOLE DISSERTATION. (in PDF format)