论文标题
一种有效的模型推理算法，用于基于学习的反应性系统测试
An Efficient Model Inference Algorithm for Learning-based Testing of Reactive Systems
论文作者
论文摘要
基于学习的测试（LBT）是一种新兴方法，可自动化软件系统的迭代黑框需求测试。该方法涉及将模型推断与模型检查技术相结合。但是，为了实现大型系统的可扩展测试，需要进行各种模型推理的优化。在本文中，我们描述了IKL学习算法，该算法是确定性Kripke结构的主动增量学习算法。我们正式证明了IKL的正确性。我们讨论了它包含的优化，以实现测试的可扩展性。我们还根据IKL学习的收敛来评估一个黑匣子启发式测试终止。
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.
