XML configurations are integral to the Android development framework, particularly in the realm of UI display. However, these configurations can introduce compatibility issues (bugs), resulting in divergent visual outcomes and system crashes across various Android API versions (levels). In this study, we systematically investigate LLM-based approaches for detecting and repairing configuration compatibility bugs. Our findings highlight certain limitations of LLMs in effectively identifying and resolving these bugs, while also revealing their potential in addressing complex, hard-to-repair issues that traditional tools struggle with. Leveraging these insights, we introduce the LLM-CompDroid framework, which combines the strengths of LLMs and traditional tools for bug resolution. Our experimental results demonstrate a significant enhancement in bug resolution performance by LLM-CompDroid, with LLM-CompDroid-GPT-3.5 and LLM-CompDroid-GPT-4 surpassing the state-of-the-art tool, ConfFix, by at least 9.8% and 10.4% in both Correct and Correct@k metrics, respectively. In addition, our real-world evaluation shows that LLM-CompDroid successfully repairs 21 configuration compatibility bugs with a 100% success rate, demonstrating its practical utility. This innovative approach holds promise for advancing the reliability and robustness of Android applications, making a valuable contribution to the field of software development.

Monitoring driver's behavior is the foundation for ensuring road safety. However, identifying unsafe behavior of the driver during the driving process still faces many challenges. In this paper, we propose a recognition network based on graph convolution, aimed at effectively identifying various driver's unsafe behaviors during the driving process. The SDRM-CSTJM graph convolutional network (SC-GCN) comprises two modules: the stacked deconvolution residual module (SDRM) and the channel-spatial-temporal joint module (CSTJM). The SDRM extracts information about the driver's unsafe actions across various scales by stacking multiple unit structures and introducing multi-level residual blocks. The CSTJM extracts features from different dimensions by integrating channel, spatial, and temporal information. Integrating SDRM and CSTJM into the spatial temporal graph convolutional network (ST-GCN), and a two-stream network is introduced to handle joint data and skeletal data separately. Subsequently, integrating the outputs of the two streams. Achieving recognition of various behaviors exhibited by the driver based on their body posture features. Finally, we evaluated the proposed method on the dataset. Experiments demonstrate that the proposed method achieves the highest recognition accuracy on the driver action dataset.