Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

Abstract

Mouse tracking tests play a crucial role in evaluating software usability, but their efficient image processing remains challenging, especially with high-resolution images and numerous participants. This article introduces a novel method, leveraging parallel computing, for the efficient analysis of interaction zones in mouse tracking test images. Following Pratt's iterative research pattern, the proposed method is implemented using Python libraries Dask and OpenCV, validated through a proof of concept on Eclipse software. Results demonstrate the parallel approach's remarkable efficiency, being 252 times faster than the sequential method across various executions. The method's potential impact is discussed, providing a valuable reference for developing tools in usability and other application contexts. The open-source tools employed, Dask and OpenCV, prove suitable for parallel image analysis, offering versatility for broader application in diverse fields. This work contributes to advancing the field of mouse tracking analysis by significantly improving processing efficiency and lays the groundwork for future tools and methodologies.