Reference : MiL Testing of Highly Configurable Continuous Controllers: Scalable Search Using Surr...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/17885
MiL Testing of Highly Configurable Continuous Controllers: Scalable Search Using Surrogate Models
English
Matinnejad, Reza mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [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 mailto [Delphi Automotive Systems, Luxembourg]
Sep-2014
International Conference on Automated Software Engineering (ASE 2014)
Yes
No
29th IEEE/ACM International Conference on Automated Software Engineering (ASE 2014)
from 15-9-2014 to 19-9-2014
[en] Search-based testing ; continuous controllers ; automotive software ; dimensionality reduction ; supervised learning
[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.
http://hdl.handle.net/10993/17885

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
paper.pdfAuthor postprint1.15 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.