|Authors||M. Zhang, S. Ali, T. Yue and R. Norgren|
|Title||Uncertainty-Wise Evolution of Test Ready Models|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Journal||Information and Software Technology (IST)|
|Keywords||Belief Model, Belief Test Ready Model, Model Evolution, Model-based Testing, Uncertainty|
Context: Cyber-Physical Systems (CPSs), when deployed for operation, are inherently prone to uncertainty. Considering their applications in critical domains (e.g., healthcare), it is important that such CPSs are tested sufficiently, with the explicit consideration of uncertainty. Model-based testing (MBT) involves creating test ready models capturing the expected behavior of a CPS and its operating environment. These test ready models are then used for generating executable test cases. It is, therefore, necessary to develop methods that can continuously evolve, based on real operational data collected during the operation of CPSs, test ready models and uncertainty captured in them, all together termed as Belief Test Ready Models (BMs)
Objective: Our objective is to propose a model evolution framework that can interactively improve the quality of BMs, based on operational data. Such BMs are developed by one or more test modelers (belief agents) with their assumptions about the expected behavior of a CPS, its expected physical environment, and potential future deployments. Thus, these models explicitly contain subjective uncertainty of the test modelers.
Method: We propose a framework (named as UncerTolve) for interactively evolving BMs (specified with extended UML notations) of CPSs with subjective uncertainty developed by test modelers. The key inputs of UncerTolve include initial BMs of CPSs with known subjective uncertainty and real data collected from the operation of CPSs. UncerTolve has three key features: 1) Validating the syntactic correctness and conformance of BMs against real operational data via model execution, 2) Evolving objective uncertainty measurements of BMs via model execution, and 3) Evolving state invariants (modeling test oracles) and guards of transitions (modeling constraints for test data generation) of BMs with a machine learning technique.
Results: As a proof-of-concept, we evaluated UncerTolve with one industrial CPS case study, i.e., GeoSports from the healthcare domain. Using UncerTolve, we managed to evolve 51% of belief elements, 18% of states, and 21% of transitions as compared to the initial BM developed in an industrial setting.
Conclusion: UncerTolve can successfully evolve model elements of the initial BM, in addition to objective uncertainty measurements using real operational data. The evolved model can be used to generate additional test cases covering evolved model elements and objective uncertainty. These additional test cases can be used to test the current and future deployments of a CPS to ensure that it will handle uncertainty gracefully during its operations.