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Lecture Notes in Computer Science
In this paper, we present Autofunk, a fast and scalable framework designed at Michelin to automatically build formal models (Symbolic Transition Systems) based on production messages gathered from production ... Our approach combines model-driven engineering with rule-based expert systems and human knowledge. ... Conclusion We built a fast and scalable framework combining model inference, expert systems and statistical analyses to derive STSs models based on production traces, i.e. generating formal models from ...doi:10.1007/978-3-319-19249-9_36 fatcat:putjw4ncardxhepsv2avx42s6i
Our framework, called Autofunk and designed with the collaboration of our industrial partner Michelin, combines formal model-driven engineering and expert systems to infer formal models that can be used ... This paper proposes a model inference framework for production systems distributed over multiple devices exchanging thousands of events. ... We focus on exact and formal model generation, using expert systems and inference rules to emulate human knowledge, and transition systems to embrace formal tools. ...doi:10.1145/2675743.2771876 dblp:conf/debs/SalvaD15 fatcat:y2ue6ojwejaojfjajietyreqiy
This paper contributes to this issue by proposing a framework called Autofunk, which combines different fields such as model inference, expert systems, and machine learning. ... This framework, designed with the collaboration of our industrial partner Michelin, infers formal models that can be used as specifications to perform offline passive testing. ... OFFLINE PASSIVE TESTING We consider both models S N and R(S N ) of a system under analysis SUA, generated by our inference-based model generation framework, as reference models. ...doi:10.1109/memcod.2015.7340480 dblp:conf/memocode/DurandS15 fatcat:yxw24anlevfovl2l75eltlvtiy