Virtual Reality (VR) is an emerging technology increasingly adopted in sectors such as gaming, education, border security, and industrial training. However, testing VR applications presents unique challenges due to factors like active user interaction, hardware dependencies, and immersive environments. Recent studies suggest that developers often write fewer test cases for VR applications, and these limited test cases frequently exhibit test smells. Current research on VR test smell detection can only identify a small subset of test smells and often lacks the necessary context for comprehensive detection. This highlights a critical gap in current testing practices for VR applications and underscores the need for approaches tailored to detecting and addressing quality issues in VR test cases.To address this research gap, we developed VRTestSniffer, a static analysis-based tool that extends test smell detection capabilities specifically for Unity-based VR applications. VRTestSniffer can detect 17 test smell categories, building upon those identified by the state-of-the-art tool tsDetect, and achieves an F1-score of 95.61%. It leverages abstract syntax trees (ASTs), control flow graphs (CFGs), and data flow graphs (DFGs) to enhance detection accuracy by capturing both control and data dependencies specific to VR testing patterns. In parallel, we conducted an empirical analysis of real-world VR projects to examine the prevalence and characteristics of these test smells. Our findings reveal that a few smelly test categories are associated with design issues such as Blob and Complex Class in functional code. We believe that VRTestSniffer, along with the empirical insights derived from this study, can help VR developers write more effective, reliable, and maintainable test cases. To support further research and replication, our tool, dataset, and analysis results are publicly available at [1].