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COnfECt: An Approach to Learn Models of Component-based Systems

Abstract : This paper addresses the problem of learning models of component-based systems. We focus on model learning approaches that generate state diagram models of software or systems. We present COnfECt, a method that supplements passive model learning approaches to generate models of component-based systems seen as black-boxes. We define the behaviours of components that call each other with Callable Extended FSMs (CEFSM). COnfECt tries to detect component behaviours from execution traces and generates systems of CEFSMs. To reach that purpose, COnfECt is based on the notions of trace analysis, event correlation, model similarity and data clustering. We describe the two main steps of COnfECt in the paper and show an example of integration with the passive model learning approach Gk-tail.
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Contributor : Sébastien Salva <>
Submitted on : Thursday, May 31, 2018 - 10:16:47 AM
Last modification on : Wednesday, March 4, 2020 - 12:28:03 PM
Document(s) archivé(s) le : Saturday, September 1, 2018 - 12:45:31 PM


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  • HAL Id : hal-01803923, version 1



Sébastien Salva, Elliott Blot. COnfECt: An Approach to Learn Models of Component-based Systems. Proceedings of the 13th International Conference on Software Technologies, ICSOFT, Jul 2018, porto, Portugal. ⟨hal-01803923⟩



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