TCM: Test Coverage Metrics For Artificial Intelligence

Description

This project studies the adaptation of software engineering techniques – in particular, coverage-guided testing – to work with machine/deep learning.

Funding Agency: Defence Science and Technology Laboratory (Dstl)

Project Time: 2018 - 2021

Personnel

  • Xiaowei Huang (PI)
  • Sven Schewe (Co-I)
  • Simon Maskell (Co-I)
  • Youcheng Sun (postdoc, 2018-2019, placed at Oxford)
  • Nicolas Berthier (postdoc, 2019-)

External Collaborators

  • Daniel Kroening (Co-I, Oxford, 2018-2019)
  • Wenjie Ruan (Co-I, Lancaster, 2019-2021)
  • Youcheng Sun (Co-I, QUB, 2019-2021)
  • Jie Meng (Co-I, Loughborough, 2019-2021)

Publications

  • Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M. and Kroening, D., 2018, September. Concolic testing for deep neural networks. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (pp. 109-119). (link)
  • Wicker, M., Huang, X. and Kwiatkowska, M., 2018, April. Feature-guided black-box safety testing of deep neural networks. In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (pp. 408-426). Springer, Cham. (link)
  • Ruan, W., Huang, X. and Kwiatkowska, M., 2018. Reachability analysis of deep neural networks with provable guarantees. arXiv preprint arXiv:1805.02242. (link)
  • Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M. and Ashmore, R., 2019. Structural test coverage criteria for deep neural networks. ACM Transactions on Embedded Computing Systems (TECS), 18(5s), pp.1-23. (link)
  • Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., Fisher, M., Papacchini, F. and Ferrando, A., 2019, September. Towards integrating formal verification of autonomous robots with battery prognostics and health management. In International Conference on Software Engineering and Formal Methods (pp. 105-124). Springer, Cham. (link)
  • Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D. and Kwiatkowska, M., 2019, August. Global robustness evaluation of deep neural networks with provable guarantees for the hamming distance. IJCAI. (link)
  • Wu, M., Louw, T., Lahijanian, M., Ruan, W., Huang, X., Merat, N. and Kwiatkowska, M., 2019, November. Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6210-6216). IEEE. (link)
  • Wu, M., Wicker, M., Ruan, W., Huang, X. and Kwiatkowska, M., 2020. A game-based approximate verification of deep neural networks with provable guarantees. Theoretical Computer Science, 807, pp.298-329. (link)
  • Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., Wu, M. and Yi, X., 2020. A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Computer Science Review, 37, p.100270. (link)
  • Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M. and Ashmore, R., 2019, May. DeepConcolic: Testing and debugging deep neural networks. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (pp. 111-114). IEEE. (link)
  • Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M. and Huang, X., 2020, September. A safety framework for critical systems utilising deep neural networks. In International Conference on Computer Safety, Reliability, and Security (pp. 244-259). Springer, Cham. (link)
  • Sun, Y., Zhou, Y., Maskell, S., Sharp, J. and Huang, X., 2020, May. Reliability validation of learning enabled vehicle tracking. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 9390-9396). IEEE. (link)
  • Huang, W., Zhou, Y., Sun, Y., Sharp, J., Maskell, S. and Huang, X., 2020. Practical Verification of Neural Network Enabled State Estimation System for Robotics.(link)