Teleportation is a widely adopted locomotion technique in virtual reality (VR), with reorientation commonly integrated into commercial systems to reduce physical turning and improve navigational control. However, research on reorientation-enabled teleportation has received limited attention and still lacks clear design guidance, resulting in longer interaction times, greater complexity, and higher mental workload. To bridge this gap, we revisit reorientation-enabled teleportation through bare-hand interaction, a natural and device-free modality that is now increasingly supported and widely adopted in mainstream VR systems. Accordingly, four gesture-based techniques, Concurrent Wrist Rotation (C-WR), Decoupled Wrist Rotation (D-WR), Joystick Wrist Rotation (J-WR), and Direction Drawing (DD), were introduced and evaluated in a controlled user study with 24 participants. Results show that C-WR was fastest but error-prone, D-WR improved accuracy at the cost of time, J-WR supported precise orientation but was demanding and slow, while DD offered balanced performance and was most preferred overall. From these findings, four design implications were distilled, emphasizing efficiency, intuitive mappings, and support for diverse user preferences. Finally, two example extensions illustrate how these techniques may extend to broader VR applications.

Given a list$L$of elements and a property$\psi$that$L$exhibits, ddmin is a classic test input minimization algorithm that aims to automatically remove$\psi$-irrelevant elements from$L$. This algorithm has been widely adopted in domains such as test input minimization and software debloating. Recently, ProbDD, a variant of ddmin, has been proposed and achieved state-of-the-art performance. By employing Bayesian optimization, ProbDD estimates the probability of each element in$L$being relevant to$\psi$, and statistically decides which and how many elements should be deleted together each time. However, the theoretical probabilistic model of ProbDD is rather intricate, and the underlying details for the superior performance of ProbDD have not been adequately explored. In this paper, we conduct the first in-depth theoretical analysis of ProbDD, clarifying the trends in probability and subset size changes and simplifying the probability model. We complement this analysis with empirical experiments, including success rate analysis, ablation studies, and examinations of trade-offs and limitations, to further comprehend and demystify this state-of-the-art algorithm. Our success rate analysis reveals how ProbDD effectively addresses bottlenecks that slow down ddmin by skipping inefficient queries that attempt to delete complements of subsets and previously tried subsets. The ablation study illustrates that randomness in ProbDD has no significant impact on efficiency. These findings provide valuable insights for future research and applications of test input minimization algorithms. Based on the findings above, we propose CDD, a simplified version of ProbDD, reducing the complexity in both theory and implementation. CDD assists in 1 validating the correctness of our key findings, e.g., that probabilities in ProbDD essentially serve as monotonically increasing counters for each element, and 2 identifying the main factors that truly contribute to ProbDD's superior performance. Our comprehensive evaluations across 76 benchmarks in test input minimization and software debloating demonstrate that CDD can achieve the same performance as ProbDD, despite being much simplified.