Automated repair of feature interaction failures in automated driving systems
Raja Ben Abdessalem, Annibale Panichella, Shiva Nejati, Lionel C. Briand, Thomas Stifter
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext a3a9589d-e3e8-40ec-a3b8-a37c1acb4c4aCited by top-tier papers5
- Detecting multi-sensor fusion errors in advanced driver-assistance systemsZiyuan Zhong, Zhisheng Hu, Shengjian Guo, Xinyang Zhang et al.ISSTA 2022 · 28 citations
- How Does Simulation-Based Testing for Self-Driving Cars Match Human Perception?Christian Birchler, Tanzil Kombarabettu Mohammed, Pooja Rani, Teodora Nechita et al.FSE 2024 · 21 citations
- Applying and Extending the Delta Debugging Algorithm for Elevator Dispatching Algorithms (Experience Paper)Pablo Valle, Aitor Arrieta, Maite ArratibelISSTA 2023 · 3 citations
- RegTrieve: Reducing System-Level Regression Errors for Machine Learning Systems via Retrieval-Enhanced EnsembleJunming Cao, Xuwen Xiang, Mingfei Cheng, Bihuan Chen et al.FSE 2025
- Automated Repair of Requirements for Cyber-Physical Systems in Simulink Requirements TablesAren A. Babikian, Alessio Di Sandro, Federico Formica, Claudio Menghi et al.FSE 2026
Related papers
- Synthesis-Based Resolution of Feature Interactions in Cyber-Physical SystemsBenjamin Gafford, Tobias Dürschmid, Gabriel A. Moreno, Eunsuk KangASE 2020 · 3 citations
- A comprehensive study of autonomous vehicle bugsJoshua Garcia, Yang Feng, Junjie Shen, Sumaya Almanee et al.ICSE 2020 · 127 citations
- Prunario: Testing Autonomous Driving Systems by Pruning Likely Redundant ScenariosMinsu Kim, Sunbeom So, Hakjoo OhOOPSLA 2026
- A Comprehensive Study of Bug-Fix Patterns in Autonomous Driving SystemsYuntianyi Chen, Yuqi Huai, Yirui He, Shilong Li et al.FSE 2025 · 1 citation
- Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary SearchYixing Luo, Xiao-Yi Zhang, Paolo Arcaini, Zhi Jin et al.ASE 2021 · 38 citations
