Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

A self-learning approach for validation of communication in embedded systems

: Langer, Falk; Oswald, Erik

Postprint urn:nbn:de:0011-n-3037680 (884 KByte PDF)
MD5 Fingerprint: e50e2bde4af4af9fb04cdcc0ee83382c
© ACM This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Created on: 23.9.2014

Association for Computing Machinery -ACM-:
3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014. Proceedings : Hyderabad, India, May 31 - June 7, 2014, in conjunction with ICSE 2014
New York: ACM, 2014
ISBN: 978-1-4503-2846-3
International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) <3, 2014, Hyderabad>
International Conference on Software Engineering (ICSE) <36, 2014, Hyderabad>
Conference Paper, Electronic Publication
Fraunhofer ESK ()
embedded system validation; testing procedure; network trace analysis; self-learning test method; communication behavior; artificial intelligence; vehicle network; automotive; automotive networks

This paper demonstrates a new approach that addresses the problem of evaluating the communication behavior of embedded systems by applying algorithms from the area of artificial intelligence. An important problem for the validation for the interaction in the distributed system is missing, wrong or incomplete specification. This paper demonstrates the application of a new self-learning approach for assessing the communication behavior based on reference traces. The benefit of the approach is that it works automatically, with low additional effort and without using any specification. The investigated methodology uses algorithms from the field of machine learning and data mining to extract behavior models out of a reference trace. For showing the application, this paper provides a use case and the basic setup for the proposed method. The applicability of this self-learning methodology is evaluated based on real vehicle network data.