Using Data Abstraction for Clustering in the Context of Test Case Generation

Data abstraction plays a crucial role in various application domains, allowing for simplification and representation of complex data sets. This paper focuses on data abstraction in the context of data clustering for test case generation, specifically in the automotive domain. Our main objective is to investigate whether we can use data abstraction to enhance the clustering outcome. We propose different abstraction functions for vehicle sensor data obtained from real-world driving data. We use these abstracted data sets as input to a clustering approach that identifies similar driving scenarios and extracts driving episodes. We evaluate the quality of the clusters using three clustering validation metrics and a Pearson correlation-based metric that assesses the similarity between the extracted driving episodes. To evaluate the effectiveness of data abstraction, we compare the metrics results to those obtained using clustering based on the original data sets comprising numerical data. The findings indicate that data abstraction primarily improves the three clustering validation metrics while delivering nearly comparable results regarding the Pearson correlation-based metric and comes with a substantially reduced runtime.