Learning regular sets from queries and counterexamples
Information and Computation
Diversity-based inference of finite automata
Journal of the ACM (JACM)
Holistic schedulability analysis for distributed hard real-time systems
Microprocessing and Microprogramming - Parallel processing in embedded real-time systems
Learning programs from traces using version space algebra
Proceedings of the 2nd international conference on Knowledge capture
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Automatic Generation and Validation of Models of Legacy Software
RTCSA '06 Proceedings of the 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
A framework for comparing models of computation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Automated execution of simulation studies demonstrated via a simulation of a car
Proceedings of the 40th Conference on Winter Simulation
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Embedded systems are often assembled from black box components. System-level analyses, including verification and timing analysis, typically assume the system description, such as RTL or source code, as an input. There is therefore a need to automatically generate formal models of black box components to facilitate analysis. We propose a new method to generate models of real-time embedded systems based on machine learning from execution traces, under a given hypothesis about the system's model of computation. Our technique is based on a novel formulation of the model generation problem as learning a dependency graph that indicates partial ordering between tasks. Tests based on an industry case study demonstrate that the learning algorithm can scale up and that the deduced system model accurately reflects dependencies between tasks in the original design. These dependencies help us formally prove properties of the system and also extract data dependencies that are not explicitly stated in the specifications of black box components.