Reference : Automated Repair of Feature Interaction Failures in Automated Driving Systems
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/43281
Automated Repair of Feature Interaction Failures in Automated Driving Systems
English
Ben Abdessalem, Raja [University of Luxembourg > SnT Centre]
Panichella, Annibale [Delft University of Technology]
Nejati, Shiva mailto [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]
Jul-2020
Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2020)
Yes
The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2020)
from 18-07-2020 to 22-07-2020
[en] Search-based Software Testing ; Automated Driving Systems ; Automated Software Repair ; Feature Interaction Problem
[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.
http://hdl.handle.net/10993/43281
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

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