Reference : Localizing Multiple Faults in Simulink Models.

	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/10993/23993

Title :	Localizing Multiple Faults in Simulink Models.
Language :	English
Author, co-author :	Liu, Bing [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Lucia, Lucia [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]
Publication date :	2016
Main document title :	23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016)
Peer reviewed :	Yes
Audience :	International
Event name :	23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering
Event date :	14-03-2016 to 18-03-2016
Event place (city) :	Osaka
Event country :	Japan
Keywords :	[en] Fault localization ; statistical debugging ; machine learning ; decision trees ; Simulink models
Abstract :	[en] As Simulink is a widely used language in the embedded industry, there is a growing need to support debugging activities for Simulink models. In this work, we propose an approach to localize multiple faults in Simulink models. Our approach builds on statistical debugging and is iterative. At each iteration, we identify and resolve one fault and re-test models to focus on localizing faults that might have been masked before. We use decision trees to cluster together failures that satisfy similar (logical) conditions on model blocks or inputs. We then present two alternative selection criteria to choose a cluster that is more likely to yield the best fault localization results among the clusters produced by our decision trees. Engineers are expected to inspect the ranked list obtained from the selected cluster to identify faults. We evaluate our approach on 240 multi-fault models obtained from three different industrial subjects. We compare our approach with two baselines: (1) Statistical debugging without clustering, and (2) State-of-the-art clustering-based statistical debugging. Our results show that our approach significantly reduces the number of blocks that engineers need to inspect in order to localize all faults, when compared with the two baselines. Furthermore, with our approach, there is less performance degradation than in the baselines when increasing the number of faults in the underlying models.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Funders :	Fonds National de la Recherche - FnR
Permalink :	http://hdl.handle.net/10993/23993
FnR project :	FnR ; FNR8003491 > Bing Liu > > Automated Debugging and Fault Localization of MATLAB/Simulink Models > 01/03/2014 > 14/07/2016 > 2014

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