An Efficient Model Inference Algorithm for Learning-based Testing of Reactive Systems
This work addresses the problem of scalable automated testing for software engineers, presenting an incremental improvement in model inference techniques.
The paper tackles the scalability challenge in learning-based testing of reactive systems by introducing the IKL algorithm, an active incremental learning method for deterministic Kripke structures, and demonstrates its effectiveness through formal correctness proofs and optimizations for scalable testing.
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.