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Evolutionary Algorithms to Generate Test Cases for Safety and IT-Security in Automotive Systems

Lauber, Andreas; Sommer, Martin; Fuchs, Martin; Sax, Eric

Abstract:
The testing process of electronic control units (ECU) is time-consuming and cost-intensive. Virtual electronic control units (vECU) can solve these problems and also offer the advantage of a variety of observation points that are not available with classical ECUs. The additional observation points can be memory access or instruction monitoring. This paper shows a novel possibility to use evolutionary algorithms, which employ the observation points, to generate new test cases for safety and IT-security in automotive systems. The novel test cases and their ability to detect error classes are described in detail. It is shown, that evolutionary test methods are able to detect different error classes. However the evolutionary methods cannot detect all given error classes within one method. Hence, a combination of different tests methods is needed.



Originalveröffentlichung
DOI: 10.1109/SysCon47679.2020.9275836
Scopus
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 04.09.2020
Sprache Englisch
Identifikator ISBN: 978-1-72815-365-0
KITopen-ID: 1000123452
Erschienen in SYSCON 2020 : the 14th Annual IEEE International Systems Conference : August 24-27, 2020, virtual conference : 2020 conference proceedings
Veranstaltung 14th Annual IEEE International Systems Conference (SYSCON 2020), Online, 16.09.2020 – 17.09.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten Art.Nr. 09275836
Bemerkung zur Veröffentlichung Die Veranstaltung fand wegen der Corona-Pandemie als Online-Event statt
Schlagwörter testing; evolutionary algorithms; automotive systems
Nachgewiesen in Scopus
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