  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.
