Deep Neural Network based models are widely used for perception and control in autonomous driving. Recent work leverages metamorphic testing to improve defect detection but is limited to using only an equality-based metamorphic relation. Thus, it does not provide expressiveness for users to define custom metamorphic relations nor means to automatically generate meaningful inputs based on such expressive metamorphic relations that reflect real-world traffic behaviors.

In this paper, we preliminary design and evaluate BMT, a declarative behavior driven development-based metamorphic testing framework.BMT enables domain experts to specify custom traffic behaviors—a car shall decelerate by x% when a bicycle is in front,etc. It then automatically translates a human-written behavior to a corresponding metamorphic relation and synthesizes meaningful test inputs using a variety of image and graphics processing techniques.

Our preliminary evaluation shows that BMT can detect a significant number of erroneous predictions of three driving models for speed predictions. These detected erroneous predictions are manually examined and confirmed by six human judges as meaningful traffic violations. By automating test generation from custom behaviors,BMT enables experts to easily express domain-specific constraints and finds meaningful violations of such constraints.