Title
A framework for mining hybrid automata from input/output traces
Abstract
Automata-based models of embedded systems are useful and attractive for many reasons: they are intuitive, precise, at a high level of abstraction, tool independent and can be simulated and analyzed. They also have the advantage of facilitating readability and system comprehension in the case of large systems. This paper proposes an approach for mining automata-based models from input/output execution traces of embedded control systems. The models mined by our approach are hybrid automata models, which capture discrete as well as continuous system behavior. Specifically this paper proposes a framework for analyzing multiple input/output traces by identifying steps like segmentation, clustering, generation of event traces, and automata inference. The framework is general enough to admit multiple techniques or future enhancements of these steps. We demonstrate the power of the framework by using some specific existing methods and tools in two case studies. Our initial results are encouraging and should spur further research in the domain.
Year
DOI
Venue
2015
10.1109/EMSOFT.2015.7318273
International Conference on Embedded Software
Keywords
Field
DocType
hybrid automata-based model mining,embedded control systems,readability,system comprehension,input execution trace,output execution trace,continuous system behavior,segmentation step,clustering step,event trace generation,automata inference
Segmentation,Inference,Computer science,Automaton,Input/output,Real-time computing,Feature extraction,Theoretical computer science,Side channel attack,Control system,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-1-4673-8079-9
6
0.45
References 
Authors
27
4
Name
Order
Citations
PageRank
Ramy Medhat1384.55
Ramesh, S.214419.02
Borzoo Bonakdarpour349045.02
Sebastian Fischmeister440952.75