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Verification challenges in the software industry, including testing, come in many different forms, due to significant differences across domains and contexts. But one common challenge is scalability, the capacity to test and verify increasingly large, complex systems. Another concern relates to practicality. Can the inputs required by a given technique be realistically provided by engineers? Though, to a large extent, Model-Driven Engineering (MDE) is a significant component of many verification techniques, a complete solution is necessarily multidisciplinary and involves, for example, machine learning or evolutionary computing components.
This talk reported on 10 years of research tackling verification and testing problems, in most cases in actual industrial contexts, relying on MDE but also metaheuristic search, optimisation, and machine learning. The focus of the talk was on how to scale to large system input spaces and achieve practicality by decreasing the level of detail and precision required in models and abstractions.
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