Testing-based Model Learning Approach for Legacy Components

2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) Pub Date : 2019-01-01 DOI:10.1109/IBCAST.2019.8667149

Shahbaz Ali, Hailong Sun, Yongwang Zhao, Naveed Akram

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Abstract

Operating, maintaining, and upgrading legacy systems are the foremost challenges which are being faced by many organizations today. Usually, these systems are based on outdated technologies, have limited documentation, and actual developers are unavailable. It is risky to upgrade black-box legacy systems without knowing their internal structures. In this paper, we have proposed an approach which is based on the state of the art dynamic analysis technique known as Model Learning, a reverse engineering approach, to infer the behavioral models of legacy systems. We prepared and utilized our test-bed for black-box vending machines (considered as legacy systems) to learn the behavioral models of all the software modules embedded in vending machines. The in-depth analysis of learned models is helpful in the operation, up-gradation, and maintenance of the legacy system. The experimental results reveal that our proposed approach is very auspicious to modernize the legacy components and explore the concealed structures of the black-box systems automatically.

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遗留组件的基于测试的模型学习方法

操作、维护和升级遗留系统是当今许多组织面临的首要挑战。通常，这些系统基于过时的技术，文档有限，并且实际的开发人员不可用。在不了解其内部结构的情况下升级黑盒遗留系统是有风险的。在本文中，我们提出了一种方法，该方法基于最先进的动态分析技术，即模型学习，一种逆向工程方法，来推断遗留系统的行为模型。我们准备并利用了黑盒自动售货机(被认为是遗留系统)的测试平台来学习嵌入在自动售货机中的所有软件模块的行为模型。对学习模型的深入分析有助于遗留系统的操作、升级和维护。实验结果表明，我们提出的方法非常有利于对遗留组件进行现代化改造，并自动探索黑箱系统的隐藏结构。

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2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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