Reference : MiL Testing of Highly Configurable Continuous Controllers: Scalable Search Using Surr...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/17885

Title :	MiL Testing of Highly Configurable Continuous Controllers: Scalable Search Using Surrogate Models
Language :	English
Author, co-author :	Matinnejad, Reza [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Nejati, Shiva [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
	Bruckmann, Thomas [Delphi Automotive Systems, Luxembourg]
Publication date :	Sep-2014
Main document title :	International Conference on Automated Software Engineering (ASE 2014)
Peer reviewed :	Yes
On invitation :	No
Event name :	29th IEEE/ACM International Conference on Automated Software Engineering (ASE 2014)
Event date :	from 15-9-2014 to 19-9-2014
Keywords :	[en] Search-based testing ; continuous controllers ; automotive software ; dimensionality reduction ; supervised learning
Abstract :	[en] Continuous controllers have been widely used in automotive do- main to monitor and control physical components. These con- trollers are subject to three rounds of testing: Model-in-the-Loop (MiL), Software-in-the-Loop and Hardware-in-the-Loop. In our earlier work, we used meta-heuristic search to automate MiL test- ing of fixed configurations of continuous controllers. In this paper, we extend our work to support MiL testing of all feasible configura- tions of continuous controllers. Specifically, we use a combination of dimensionality reduction and surrogate modeling techniques to scale our earlier MiL testing approach to large, multi-dimensional input spaces formed by configuration parameters. We evaluated our approach by applying it to a complex, industrial continuous controller. Our experiment shows that our approach identifies test cases indicating requirements violations. Further, we demonstrate that dimensionally reduction helps generate surrogate models with higher prediction accuracy. Finally, we show that combining our search algorithm with surrogate modelling improves its efficiency for two out of three requirements.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Permalink :	http://hdl.handle.net/10993/17885

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access paper.pdf Author postprint 1.15 MB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map