Reference : Automated Repair of Feature Interaction Failures in Automated Driving Systems

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/43281

Title :	Automated Repair of Feature Interaction Failures in Automated Driving Systems
Language :	English
Author, co-author :	Ben Abdessalem, Raja [University of Luxembourg > SnT Centre]
	Panichella, Annibale [Delft University of Technology]
	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) > >]
	Stifter, Thomas [IEE Luxembourg]
Publication date :	Jul-2020
Main document title :	Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2020)
Peer reviewed :	Yes
Event name :	The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2020)
Event date :	from 18-07-2020 to 22-07-2020
Keywords :	[en] Search-based Software Testing ; Automated Driving Systems ; Automated Software Repair ; Feature Interaction Problem
Abstract :	[en] In the past years, several automated repair strategies have been proposed to fix bugs in individual software programs without any human intervention. There has been, however, little work on how automated repair techniques can resolve failures that arise at the system-level and are caused by undesired interactions among different system components or functions. Feature interaction failures are common in complex systems such as autonomous cars that are typically built as a composition of independent features (i.e., units of functionality). In this paper, we propose a repair technique to automatically resolve undesired feature interaction failures in automated driving systems (ADS) that lead to the violation of system safety requirements. Our repair strategy achieves its goal by (1) localizing faults spanning several lines of code, (2) simultaneously resolving multiple interaction failures caused by independent faults, (3) scaling repair strategies from the unit-level to the system-level, and (4) resolving failures based on their order of severity. We have evaluated our approach using two industrial ADS containing four features. Our results show that our repair strategy resolves the undesired interaction failures in these two systems in less than 16h and outperforms existing automated repair techniques.
Permalink :	http://hdl.handle.net/10993/43281
Framework Programme / European Project :	H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

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