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

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

Content Provider	ACM Digital Library
Author	Langer, Falk Oswald, Erik
Abstract	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. .
Starting Page	38
Ending Page	44
Page Count	7
File Format	PDF
ISBN	9781450328463
DOI	10.1145/2593801.2593808
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Testing procedures Network trace analysis Self-learning test methods Embedded system validation
Content Type	Text
Resource Type	Article

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