An Efficient Model Inference Algorithm for Learning-based Testing of Reactive Systems
2012 (English)Report (Other academic)
Learning-based testing (LBT) is an emerging methodology to automate iterative black-box requirements testing of software systems. The methodology involves combining model inference with model checking techniques. However, a variety of optimisations on model inference are necessary in order to achieve scalable testing for large systems.
In this paper we describe the IKL learning algorithm which is an active incremental learning algorithm for deterministic Kripke structures. We formally prove the correctness of IKL. We discuss the optimisations it incorporates to achieve scalability of testing. We also evaluate a black box heuristic for test termination based on convergence of IKL learning.
Place, publisher, year, edition, pages
2012. , 29 p.
automata learning, black-box testing, learning-based testing, reactive systems
IdentifiersURN: urn:nbn:se:kth:diva-119266OAI: oai:DiVA.org:kth-119266DiVA: diva2:610364
QC 201303122013-03-112013-03-112013-03-12Bibliographically approved