We present a survey of the recent research efforts in integrating model learning with model-
based testing. We distinguished two strands of work in this domain, namely test-based …

Abstract Model-based testing is a promising technique for quality assurance. In practice,
however, a model is not always present. Hence, model learning techniques attain increasing …

Abstract Automata learning techniques automatically generate systemmodels fromtest
observations. Typically, these techniques fall into two categories: passive and active. On the …

Abstract System verification is often hindered by the absence of formal models. Peled et al.
proposed black-box checking as a solution to this problem. This technique applies active …

Abstract Model checking has a long-standing tradition in software verification. Given a
system design it checks whether desired properties are satisfied. Unlike testing, it cannot be …

The Internet of Things (IoT) extends the Internet to include also wireless embedded
computers that are often equipped with sensors and actuators to monitor and control their …

We present an efficient incremental learning algorithm for Deterministic Finite Automaton
(DFA) with the help of inverse query (IQ) and membership query (MQ). This algorithm is an …

A resurgent interest for grammatical inference aka automaton learning has emerged in
several intriguing areas of computer sciences such as machine learning, software …

Black-box components conceal parts of software execution paths, which makes systematic
testing, eg, via symbolic execution, difficult. In this paper, we use automata learning to …

Abstract Model checking has a long-standing tradition in software verification. Given a
system design it checks whether desired properties are satisfied. Unlike testing, it cannot be …