为遗留响应系统高效地生成基于测试的模型

Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940) Pub Date : 2004-11-10 DOI:10.1109/HLDVT.2004.1431246

T. Margaria, Oliver Niese, Harald Raffelt, B. Steffen

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引用次数: 83

摘要

我们介绍了使用一种有效的算法进行基于行为的模型综合的效果，该算法专门针对反应性(遗留)系统行为进行定制。概念主干是经典的自动机学习过程L*，我们根据所考虑的应用概况对其进行了调整。由此产生的学习过程L*Meal，直接从通过自动化测试环境收集的行为观察中合成广义Mealy自动机，大大优于确定性有限自动机的经典学习算法。因此，它标志着将工业遗留系统向基于模型的测试套件增强、测试覆盖分析和在线测试开放的一个里程碑。

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Efficient test-based model generation for legacy reactive systems

We present the effects of using an efficient algorithm for behavior-based model synthesis which is specifically tailored to reactive (legacy) system behaviors. Conceptual backbone is the classical automata learning procedure L*, which we adapt according to the considered application profile. The resulting learning procedure L*Meal , which directly synthesizes generalized Mealy automata from behavioral observations gathered via an automated test environment, drastically outperforms the classical learning algorithm for deterministic finite automata. Thus it marks a milestone towards opening industrial legacy systems to model-based test suite enhancement, test coverage analysis, and online testing.

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Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940)

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