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The optimization of a system’s configuration options is crucial for determining its performance and functionality, particularly in the case of autonomous driving software (ADS) systems because they possess a multitude of such options. Research efforts in the domain of ADS have prioritized the development of automated testing methods to enhance the safety and security of self-driving cars. Presently, search-based approaches are utilized to test ADS systems in a virtual environment, thereby simulating real-world scenarios. However, such approaches rely on optimizing the waypoints of ego cars and obstacles to generate diverse scenarios that trigger violations, and no prior techniques focus on optimizing the ADS from the perspective of configuration. To address this challenge, we present a framework called ConfVE, which is the first automated configuration testing framework for ADSes. ConfVE’s design focuses on the emergence of violations through rerunning scenarios generated by different ADS testing approaches under different configurations, leveraging 9 test oracles to enable previous ADS testing approaches to find more types of violations without modifying their designs or implementations and employing a novel technique to identify bug-revealing violations and eliminate duplicate violations. Our evaluation results demonstrate that ConfVE can discover 1,818 unique violations and reduce 74.19% of duplicate violations.
Chen et al. (Fri,) studied this question.