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Autonomous driving systems (ADS) require extensive testing and validation before deployment. However, it is tedious and time-consuming to construct traffic scenarios for ADS testing. In this paper, we propose TrafficComposer, a multi-modal traffic scenario construction approach for ADS testing. TrafficComposer takes as input a natural language (NL) description of a desired traffic scenario and a complementary traffic scene image. Then, it generates the corresponding traffic scenario in a simulator, such as CARLA and LGSVL. Specifically, TrafficComposer integrates high-level dynamic information about the traffic scenario from the NL description and intricate details about the surrounding vehicles, pedestrians, and the road network from the image. The information from the two modalities is complementary to each other and helps generate high-quality traffic scenarios for ADS testing. On a benchmark of 120 traffic scenarios, TrafficComposer achieves 97.0% accuracy, outperforming the best-performing baseline by 7.3%. Both direct testing and fuzz testing experiments on six ADSs prove the bug detection capabilities of the traffic scenarios generated by TrafficComposer. These scenarios can directly discover 37 bugs and help two fuzzing methods find 33%–124% more bugs serving as initial seeds. 

Increasingly popular Robot Operating System (ROS) framework allows building robotic systems by integrating newly developed and/or reused modules, where the modules can use different versions of the framework (e.g., ROS1 or ROS2) and programming language (e.g. C++ or Python). The majority of such robotic systems' work happens in callbacks. The framework provides various elements for initializing callbacks and for setting up the execution of callbacks. It is the responsibility of developers to compose callbacks and their execution setup elements, and hence can lead to inconsistencies related to the setup of callback execution due to developer's incomplete knowledge of the semantics of elements in various versions of the framework. Some of these inconsistencies do not throw errors at runtime, making their detection difficult for developers. We propose a static approach to detecting such inconsistencies by extracting a static view of the composition of robotic system's callbacks and their execution setup, and then checking it against the composition conventions based on the elements' semantics. We evaluate our ROSCallBaX prototype on the dataset created from the posts on developer forums and ROS projects that are publicly available. The evaluation results show that our approach can detect real inconsistencies.