Model-Driven Verification and Testing of Cyber-Physical Systems: Tackling Scalability and Practicality Challenges (Invited Talk)

摘要

Testing and verification problems in the software industry come in many different forms, due to significant differences across domains and contexts. But one common challenge in the context of cyber-physical systems is scalability; the capacity to test and verify increasingly large systems interacting with complex physical environments. Another concern relates to practicality. Can the inputs required by a given technique be realistically provided by engineers given their background and time constraints? This talk reports on ten years of research tackling the verification and testing of cyber-physical systems as a search and optimization problem, often but not always relying on abstractions and models of the system under test. This experience spans several application domains and organizations. Our observation is that most of the problems we faced could be effectively re-expressed so as to make use of appropriate search and optimization techniques to automate specific testing or verification strategies, targeting various categories of faults. However, to achieve scalability, such solutions had to be often complemented by machine learning to help the search focus on regions of the input space that were more likely to exhibit failures.