TrajectoryTest: A Trajectory-Specific Test Input Prioritization Technique and Empirical Evaluation

Abstract

Trajectory prediction models have become increasingly vital in various safety-critical domains, such as healthcare, maritime surveillance, and intelligent transportation systems. These applications utilize machine learning models to predict the operational state of a moving agent based on its motion trajectory. Despite their widespread deployment in these domains, testing such models remains a significant challenge due to the high cost of manual labeling, further exacerbated by the large scale and domain-specific characteristics of trajectory datasets. Test input prioritization has emerged as a promising solution to address the labeling cost issue, aiming to identify potentially misclassified inputs early to accelerate debugging and improve testing efficiency. The current state‑of‑the‑art test prioritization technique that can be applied to trajectory prediction tasks is MLPrior. However, MLPrior has the following limitations when applied to trajectory prediction scenarios: 1) MLPrior relies on rich input features, which are limited in trajectory data. Trajectory inputs typically contain only basic spatiotemporal coordinates (e.g., latitude, longitude, timestamps), offering insufficient attribute richness. 2) MLPrior inherits the limitations of learning‑based approaches. When the training data are imbalanced, their effectiveness can be significantly reduced. To overcome these limitations, we propose TrajectoryTest, a trajectory-specific test prioritization strategy that integrates trajectory-related information with MLPrior-derived representations to rank test inputs. Moreover, TrajectoryTest employs an adaptive strategy that switches between learning-based and uncertainty-based ranking depending on the model’s prediction error ratio. We conduct a comprehensive empirical study that confirms the limitations of MLPrior in trajectory prediction scenarios and demonstrates that TrajectoryTest outperforms all existing test input prioritization techniques, including the state-of-the-art approach MLPrior, multiple confidence-based methods, and the baseline random selection. The experimental results show that TrajectoryTest achieves improvements ranging from 7.03% to 9.56% over MLPrior (the state-of-the-art method) and confidence-based approaches on natural datasets, and from 6.71% to 9.94% on noisy datasets.