U-test: Evolving, modelling and testing realistic uncertain behaviours of cyber-physical systems

Uncertainty is intrinsic in Cyber-Physical Systems (CPSs) due to novel interactions of embedded systems, networking equipment, cloud infrastructures and humans. Our daily life has been increasing dependent on CPS applications in safety/mission critical domains such as healthcare, aerospace, oil/gas and maritime. For example, the National Institute of Standards and Technology (NIST) reported that direct CPS applications account for more than $32.3 trillions and expect to grow $82 trillions by 2025 (about half of the world economy). Expecting enormous dependence of our lives on CPSs in the future, dealing with uncertainty at an acceptable cost is vital to avoid posing undue threats to its users and environment. To ensure correct delivery of their functions at an acceptable cost even in the presence of uncertainty, CPSs must be reliable, robust, efficient, safe, and secure. All these properties are facets of a more general property often known as dependability. Improving system dependability first and foremost relies on the ability to verify and validate CPSs in a cost-effective manner and one way of achieving this is via systematic and automated Model-Based Testing (MBT): automated derivation of test cases from a behavioral model of a system. MBT supports rigorous, systematic, and automated testing, which eventually reduces the number of faults in the delivered systems and thus improves their quality. The goal of the U-Test project (a recently funded project under the EU Horizon2020 program (http://ec.europa.eu/programmes/horizon2020/) is to improve the dependability of CPSs, via cost-effective, model-based and search-based testing of CPSs under unknown risky uncertainty. Unknown uncertainty is the state of a CPS that can only be determined at the runtime as opposed to known uncertainty that is known at the design time and outcome from risky uncertainty is undesirable. To achieve our goal, we will advance the current state-of-art of testing CPSs by developing a novel solution based on sound theoretical foundation for uncertainty testing in the following steps: 1) Developing a light-weight modelling solution with rich formalism to support minimal modelling of known uncertainty with risk information; 2) Intelligently evolving known uncertainty models towards realistic and risky unknown uncertainty models (evolved models) using search algorithms (e.g., genetic algorithms mimicking natural selection); and 3) Automatically generating test cases from the evolved models to test a CPS under unknown uncertainty to ensure that the CPS continues to operate properly and possibly at a reduced quality of operation, rather than failing completely.