This method aims to provide a human-based assessment metrics to estimate how users perceived the distance between two interfaces, i.e., how they perceive them as different or not. Metrics have been identified through qualitative user studies [1] and their weights computed using pairwise comparison scaling method [2].
This script was written to work using hierarchy trees obtained from a XY-tree decomposition algorithm [3], or, if that is not available, by manually reproducing this decomposition. To perform this decomposition using human assessors, we used Figma and a json export plugin (https://github.com/R4ph3rd/XY-tree-figma-export).
Regarding Reading Flow, we recommend using an eye tracker to determine the reading sequence of stimulus interfaces over a given period of time (10 seconds for an initial overview estimate). This can be done using gaze tracking analysis tools (PyGaze, BeGaze) that allow you to extract the order in which a grid of areas of interest (AOI) are visually visited. The script expects the same tree structure output from the Figma json export plugin, with a grid organized from left to right by rows, with the name of each Leaf containing the order value (e.g., 1,2,...14,...52 or X if not visited).
More details will be released in the paper.
R. Perraud and S. Malacria, “Measuring interface similarity: Computing a more perceptual distance between graphical user interfaces,” in 36e conférence internationale francophone sur l’Interaction humain-machine (IHM’25), Toulouse, France, Nov. 2025. [Online]. Available: https://hal.science/hal-05302454
- under revision at CHI 2026
- Perez-Ortiz, M., & Mantiuk, R. K. (2017). A practical guide and software for analysing pairwise comparison experiments (No. arXiv:1712.03686). arXiv. https://doi.org/10.48550/arXiv.1712.03686
- Katharina Reinecke, Tom Yeh, Luke Miratrix, Rahmatri Mardiko, Yuechen Zhao, Jenny Liu, and Krzysztof Z. Gajos. 2013. Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). Association for Computing Machinery, New York, NY, USA, 2049-2058. https://doi.org/10.1145/2470654.2481281