In this RQ, our goal is to validate the effectiveness of our model-based analysis in identifying abnormal behavior across three trustworthiness perspectives and explore the relationship between semantic-wise metrics and analysis performance. 

Since semantic-wise metrics are crafted to gauge the abstract model's quality from a semantic standpoint. We anticipate a strong correlation with the task's performance, aiming to confirm this hypothesis in our investigation. This inquiry not only serves as a performance check but also aids in pinpointing optimal abstract models for analysis, a process further supplemented by examining the correlation between model-wise metrics and performance for model selection.

In the following visual analysis, we present two comprehensive plots, each encapsulating crucial aspects of our investigation through a series of subplots dedicated to three distinct tasks.

Abstract Model-wise Metrics:

The first plot delves into the realm of abstract model-wise metrics, showcasing the minimum, mean, and maximum values of each metric across the three tasks in the form of intuitive radar plots. This visualization serves as a crucial tool for discerning the variability and central tendencies of these metrics, aiding in a holistic understanding of the abstract models' performance and characteristics.

Semantic-wise Metrics:

The second plot shifts the focus to semantic metrics, following a similar structure of presenting the minimum, mean, and maximum values through radar plots for each task. This enables a direct comparison and correlation analysis between the semantic quality of the abstract models and their performance across the tasks, providing valuable insights for model selection and performance optimization.