Learning-Based Testing for Reactive Systems Using Term Rewriting Technology

Abstract : We show how the paradigm of learning-based testing (LBT) can be applied to automate specification-based black-box testing of reactive systems using term rewriting technology. A general model for a reactive system can be given by an extended Mealy automata (EMA) over an abstract data type (ADT). A finite state EMA over an ADT can be efficiently learned in polynomial time using the CGE regular inference algorithm, which builds a compact representation as a complete term rewriting system. We show how this rewriting system can be used to model check the learned automaton against a temporal logic specification by means of narrowing. Combining CGE learning with a narrowing model checker we obtain a new and general architecture for learning-based testing of reactive systems. We compare the performance of this LBT architecture against random testing using a case study.
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Burkhart Wolff; Fatiha Zaïdi. 23th International Conference on Testing Software and Systems (ICTSS), Nov 2011, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7019, pp.97-114, 2011, Testing Software and Systems. 〈10.1007/978-3-642-24580-0_8〉
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Karl Meinke, Fei Niu. Learning-Based Testing for Reactive Systems Using Term Rewriting Technology. Burkhart Wolff; Fatiha Zaïdi. 23th International Conference on Testing Software and Systems (ICTSS), Nov 2011, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7019, pp.97-114, 2011, Testing Software and Systems. 〈10.1007/978-3-642-24580-0_8〉. 〈hal-01583917〉

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