Whitebox Testing, Debugging, and Repairing for Multi-module Autonomous Vehicles in Near-Collision Traffic Scenarios

Principal Investigator: Tianyi Zhang

This project seeks to develop principled algorithms and techniques for systematically testing, debugging, and repairing multi-module ADS to improve their safety and reliability. The core of our research is (1) a method for automated test-scenario construction that decouples high-level semantics and low-level details through a novel Domain Specific Language-based synthesis algorithm, (2) a search-based testing method that efficiently explores the enormous space of possible scenarios and identifies collision-inducing scenarios through a layered abstraction of multi-module autonomous systems and hierarchical optimization, and (3) a new adaptive debugging and repair technique that strategically diagnoses and fixes different kinds of safety bugs in different modules at different levels of granularity.

Personnel

Other PIs: Xiangyu Zhang

Students: Tu Zhi

Representative Publications

• Lou, Guannan, Yao Deng, Xi Zheng, Mengshi Zhang, and Tianyi Zhang. "Testing of autonomous driving systems: where are we and where should we go?." In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 31-43. 2022.

• Deng, Yao, Xi Zheng, Mengshi Zhang, Guannan Lou, and Tianyi Zhang. "Scenario-based test reduction and prioritization for multi-module autonomous driving systems." In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 82-93. 2022.

• Deng, Y., Zheng, X., Zhang, T., Liu, H., Lou, G., Kim, M., & Chen, T. Y. (2022). A declarative metamorphic testing framework for autonomous driving. IEEE Transactions on Software Engineering, 49(4), 1964-1982.