Home // International Journal On Advances in Software, volume 15, numbers 1 and 2, 2022 // View article

An Approach for Learning Behavioural Models of Communicating Systems

Authors:
Sébastien Salva

Keywords: Reverse engineering; Model learning; Event Log; Communicating systems.

Abstract:
This paper is concerned with recovering formal models from event logs collected from communicating systems. We refer here to systems made up of components interacting with each other by data networks and whose communications can be monitored, e.g., Internet of Things (IoT) systems, distributed applications or Web service compositions. Our approach, which we call method{}, aims at generating, from en event log, one Input Output Labelled Transition System (IOLTS) for every component participating in the communications and one graph illustrating the directional dependencies with the other components. These models can help engineers better and quicker understand how a communicating system behaves and is structured. They can also be used for bug detection or for test generation. Compared to other model learning approaches specialised for communicating systems, method{} improves the precision of the generated models by integrating algorithms that better recognise sessions in event logs. method{} revisits and extends a first approach by simplifying the set of requirements and assumptions in order to increase its applicability on communicating systems. It now integrates two new trace extraction algorithms: the former segments event logs into traces by trying to detect sessions; the latter assumes event logs to include session identifiers and allows to quicker generate models. We report experimental results obtained from 10 case studies and show that method{} has the capability of producing precise models in reasonable time delays.

Pages: 111 to 127

Copyright: Copyright (c) to authors, 2022. Used with permission.

Publication date: June 30, 2022

Published in: journal

ISSN: 1942-2628