Automated Repair of Feature Interaction Failures in Automated Driving Systems
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.
Mon 20 JulDisplayed time zone: Tijuana, Baja California change
13:30 - 14:30
REPAIR AND DEBUGTechnical Papers at Zoom
Chair(s): Xuan Bach D. Le The University of Melbourne
Public Live Stream/Recording. Registered participants should join via the Zoom link distributed in Slack.
|Can Automated Program Repair Refine Fault Localization? A Unified Debugging Approach|
Yiling Lou Peking University, China, Ali Ghanbari Iowa State University, Xia Li Kennesaw State University, Lingming Zhang The University of Texas at Dallas, Haotian Zhang Ant Financial, Dan Hao Peking University, Lu Zhang Peking University, ChinaDOI Pre-print Media Attached
|Automated Repair of Feature Interaction Failures in Automated Driving Systems|
Raja Ben Abdessalem SnT Centre/University of Luxembourg, Annibale Panichella Delft University of Technology, Shiva Nejati University of Ottawa, Lionel C. Briand SnT Centre/University of Luxembourg, Thomas StifterDOI Pre-print
|CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair|
Thibaud Lutellier , Viet Hung Pham University of Waterloo, Lawrence Pang , Yitong Li , Moshi Wei , Lin Tan Purdue UniversityDOI Media Attached