Formal Feature Interpretation of Hybrid Systems
This work addresses the need for more nuanced analysis in hybrid systems modeling, though it appears incremental as it builds on existing feature interpretation methods.
The paper tackles the problem of moving beyond assertion-based analysis of hybrid systems by introducing a feature-based approach for quantitative evaluation of behavioral attributes, demonstrating how satisfiability modulo theory solvers can extract corner-case traces in control and circuit examples.
In current practice a formal analysis of hybrid system models is assertion-based. The work presented here is based on features that look beyond functional correctness toward a quantitative evaluation of behavioral attributes. A feature defines a real-valued evaluation function over a specific set of traces. This paper describes an improved method for the interpretation of features over hybrid automata models. It further demonstrates how satisfiability modulo theory solvers can be used for extracting behavioral traces corresponding to corner cases of a feature. Results are demonstrated on examples from the control and circuit domains.