TY - GEN
T1 - Automated repair of feature interaction failures in automated driving systems
AU - Abdessalem, Raja Ben
AU - Panichella, Annibale
AU - Nejati, Shiva
AU - Briand, Lionel C.
AU - Stifter, Thomas
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/18
Y1 - 2020/7/18
N2 - 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.
AB - 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.
KW - Automated Driving Systems
KW - Automated Software Repair
KW - Feature Interaction Problem
KW - Search-based Software Testing
UR - http://www.scopus.com/inward/record.url?scp=85088921165&partnerID=8YFLogxK
U2 - 10.1145/3395363.3397386
DO - 10.1145/3395363.3397386
M3 - Conference contribution
AN - SCOPUS:85088921165
T3 - ISSTA 2020 - Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis
SP - 88
EP - 100
BT - ISSTA 2020 - Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis
A2 - Khurshid, Sarfraz
A2 - Pasareanu, Corina S.
PB - Association for Computing Machinery, Inc
T2 - 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2020
Y2 - 18 July 2020 through 22 July 2020
ER -