This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Boisselle V, Destefanis G, De Marco A, Adams B.2016. Signature-based detection of behavioural deviations in flight simulators - Experiments on FlightGear and JSBSim. PeerJ Preprints4:e2670v1https://doi.org/10.7287/peerj.preprints.2670v1
Flight simulators are systems composed of numerous off-the-shelf components that allow pilots and maintenance crew to prepare for common and emergency flight procedures for a given aircraft model. A simulator must follow severe safety specifications to guarantee correct behaviour and requires an extensive series of prolonged manual tests to identify bugs or safety issues. In order to reduce the time required to test a new simulator version, this paper presents rule-based models able to automatically identify unexpected behaviour (deviations). The models represent signature trends in the behaviour of a successful simulator version that are compared to the behaviour of a new simulator version. Empirical analysis on nine types of injected faults in the popular FlightGear and JSBSim open source simulators shows that our approach does not miss any deviating behaviour considering faults which change the flight environment, and that we are able to find all the injected deviations in 4 out 7 functional faults and 75% of the deviations in 2 other faults.
This is a submission to PeerJ Computer Science for review.