About the Project
The current practice includes adversarial ML testing (finding inputs that, when changed minimally from their original versions, get classified differently), fuzz and search-based test input generation. For solving the ‘Oracle problem’ metamorphic relations have been studied – these are transformations of the test data that are expected to yield the same output, or to generate expected changes in the predictive result.
This project will focus on automating ML testing for autonomous driving, but there are other applications areas, including medical image analysis and machine translation for which collaboration opportunities are possible.
Candidates are expected to hold (or be about to obtain) a minimum 2:1 honours degree (or equivalent) in a related area / subject, e.g. Computer Science, Software Engineering, Mathematics, Machine Learning, etc. MSc, MA or relevant experience in a related discipline is highly desirable.
Sergio Segura, Gordon Fraser, Ana B. Sánchez, Antonio Ruiz Cortés: A Survey on Metamorphic Testing. IEEE Trans. Software Eng. 42(9): 805-824 (2016)
Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray: DeepTest: automated testing of deep-neural-network-driven autonomous cars. ICSE 2018: 303-314
Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu: Machine Learning Testing: Survey, Landscapes and Horizons. In IEEE Transactions on Software Engineering, doi: 10.1109/TSE.2019.2962027.
Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, Sarfraz Khurshid: DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems. ASE 2018: 132-142
Zhi Quan Zhou, Liqun Sun: Metamorphic testing of driverless cars. Commun. ACM 62(3): 61-67 (2019)
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