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Model learning: a survey of foundations, tools and applications
Frontiers of Computer Science ( IF 4.2 ) Pub Date : 2021-06-29 , DOI: 10.1007/s11704-019-9212-z
Shahbaz Ali , Hailong Sun , Yongwang Zhao

Software systems are present all around us and playing their vital roles in our daily life. The correct functioning of these systems is of prime concern. In addition to classical testing techniques, formal techniques like model checking are used to reinforce the quality and reliability of software systems. However, obtaining of behavior model, which is essential for model-based techniques, of unknown software systems is a challenging task. To mitigate this problem, an emerging black-box analysis technique, called Model Learning, can be applied. It complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully automatically. This paper surveys the model learning technique, which recently has attracted much attention from researchers, especially from the domains of testing and verification. First, we review the background and foundations of model learning, which form the basis of subsequent sections. Second, we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison table. Third, we describe the successful applications of model learning in multidisciplinary fields, current challenges along with possible future works, and concluding remarks.



中文翻译:

模型学习:基础、工具和应用调查

软件系统无处不在,在我们的日常生活中扮演着重要的角色。这些系统的正确运行是首要关注的问题。除了经典的测试技术外,还使用模型检查等形式化技术来加强软件系统的质量和可靠性。然而,获取未知软件系统的行为模型对于基于模型的技术至关重要,这是一项具有挑战性的任务。为了缓解这个问题,一种新兴的黑盒分析技术,称为模型学习,可以应用。它通过完全自动地提供黑盒系统的行为模型来补充现有的基于模型的测试和验证方法。本文调查了模型学习技术,该技术最近引起了研究人员的广泛关注,尤其是在测试和验证领域。首先,我们回顾了模型学习的背景和基础,它们构成了后续章节的基础。其次,我们介绍了一些著名的模型学习工具,并以比较表的形式提供了它们的优缺点。第三,我们描述了模型学习在多学科领域的成功应用、当前的挑战以及未来可能的工作以及结束语。

更新日期:2021-06-29
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