A Metamorphic Testing Framework for Verification and Validation of Unsupervised Sensor Grouping in Smart Spaces via Spectral Clustering

<jats:title>ABSTRACT</jats:title> <jats:p>With the advancement of wireless technology, smart spaces such as homes and offices are increasingly populated with smart devices, including sensors and actuators. In such environments, user activities generate time‐series data that can be analysed to derive rules for the autonomous activation of actuators such as appliances, lights and switches. Since time‐series data in smart spaces grows continuously, manual annotation becomes impractical, making unsupervised learning techniques a practical choice for supporting automation. However, verifying the outcomes of these unsupervised methods is challenging due to the absence of ground‐truth labels. Moreover, the internal decision‐making processes of most unsupervised algorithms are complex and opaque, making their outputs difficult for end users to validate. These challenges give rise to the ‘oracle problem’, which complicates verification and validation. In this paper, we propose a metamorphic testing approach for verifying a novel sensor grouping technique in smart spaces. The technique employs a Spectral clustering algorithm with graph‐based feature representations derived from time‐series data. Our approach defines 10 metamorphic relations encompassing both verification and validation perspectives. Test cases are generated based on these relations, enabling the sensor grouping technique to be evaluated against variations in input data and clustering parameters. Experimental results demonstrate that the proposed approach effectively identifies implementation flaws in sensor relationship inference methods, assesses the structural consistency of clustering results in the absence of ground‐truth labels and evaluates the robustness of the technique across diverse smart space scenarios.</jats:p>