There has been a recent growth of interest in software engineering community to use grammatical inference, aka automaton learning, in software engineering applications. This is primarily due to the reason that capacity of underlying hardware resources has improved significantly over the last years; which has enabled the use of this approach beyond the toy examples in a greater frequency. In this paper, we present a new grammatical inference algorithm, DKL, to learn deterministic Kripke structures. The DKL is only the second of its kind for incremental learning of deterministic Kripke structures. Earlier, IKL algorithm was introduced for learning deterministic Kripke structures but it often constructs a hypothesis much larger in state size than the target Kripke structure. This problem is known as state-space explosion and it primarily occurs due to the sub-direct product construction used in the IKL design, which in turn affects time efficiency of IKL; especially when it is used for practical applications of software engineering. The DKL algorithm is designed to resolve the problem of state-space explosion. We give a proof of correctness and termination of the DKL algorithm. We also compared the performance of DKL with IKL by implementing an evaluation framework. Our results show that DKL is more efficient in terms of time in reaching the target automaton than the IKL algorithm and is not prone to the problem of state-space explosion.

中文翻译：

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DKL：学习确定性 Kripke 结构的有效算法

最近，软件工程社区对在软件工程应用程序中使用语法推理（又名自动机学习）的兴趣有所增长。这主要是由于过去几年底层硬件资源的容量有了显着提高；这使得这种方法的使用频率超出了玩具示例的范围。在本文中，我们提出了一种新的语法推理算法 DKL，以学习确定性 Kripke 结构。DKL 只是同类中第二个用于确定性 Kripke 结构的增量学习。早些时候，IKL 算法被引入用于学习确定性 Kripke 结构，但它通常构造一个状态大小比目标 Kripke 结构大得多的假设。这个问题被称为状态空间爆炸，它主要是由于 IKL 设计中使用的子直接产品构造而发生的，这反过来影响了 IKL 的时间效率；特别是当它用于软件工程的实际应用时。DKL 算法旨在解决状态空间爆炸问题。我们给出了 DKL 算法正确性和终止的证明。我们还通过实施评估框架比较了 DKL 与 IKL 的性能。我们的结果表明，DKL 在到达目标自动机的时间方面比 IKL 算法更有效，并且不容易出现状态空间爆炸的问题。特别是当它用于软件工程的实际应用时。DKL 算法旨在解决状态空间爆炸问题。我们给出了 DKL 算法正确性和终止的证明。我们还通过实施评估框架比较了 DKL 与 IKL 的性能。我们的结果表明，DKL 在到达目标自动机的时间方面比 IKL 算法更有效，并且不容易出现状态空间爆炸的问题。特别是当它用于软件工程的实际应用时。DKL 算法旨在解决状态空间爆炸问题。我们给出了 DKL 算法正确性和终止的证明。我们还通过实施评估框架比较了 DKL 与 IKL 的性能。我们的结果表明，DKL 在到达目标自动机的时间方面比 IKL 算法更有效，并且不容易出现状态空间爆炸的问题。