|Title||Discovering and Testing Unknown Uncertainties of Cyber-Physical Systems|
|Publication Type||Talk, keynote|
|Year of Publication||2016|
|Location of Talk||National Software Application Conference (NASAC), Kunming, China|
|Type of Talk||Keynote|
Cyber-Physical Systems (CPSs), when deployed for real applications, are inherently prone to uncertainty. Considering their applications in critical domains, it is important that such CPSs are tested sufficiently, with the consideration of uncertainty, before they are deployed for their intended applications. However, existing methodologies can only test a CPSs under known uncertainties. Thus, it is very necessary to develop novel methodologies that can discover and therefore test CPSs under unknown uncertainties. In this talk, I will present a framework for evolving test ready models (specified with extended UML notations) of CPSs under uncertainty. The key inputs for the framework (named as U-Evolve) include initial test ready models of CPSs with known subjective uncertainty and real data collected from their applications. The output is evolved test ready models with newly discovered “unknown” uncertainty. I will also present a Model-Based Testing framework for testing CPSs under uncertainties, which consists of an Uncertainty Modeling Framework (UMF) for modeling test ready models and an Uncertainty Testing Framework (UTF) for automatically and systematically generating executable test cases, and integrates with the Modeling and Analysis of Real-Time and Embedded Systems (MARTE) and the UML Testing Profile (UTP) V.2. The U-Evolve, UMF and UTF are an integrated solution developed in the context of a large EU Horizon 2020 project: U-Test (http://www.u-test.eu/).