Abstract
Measuring visual complexity (VC) of human-computer user interfaces (UIs) sees increasing development, as VC has been found to affect users’ cognitive load, aesthetical impressions and overall performance. Spatial allocation and ordering of UI elements is the major feature manipulated by an interface designer, and in our paper we focus on perceived complexity of layouts. Algorithmic Information Theory has justified the use of data compression algorithms for generating metrics of VC as lengths of coded representations, so we consider two established algorithms: RLE and Deflate. First, we propose the method for obtaining coded representations of UI layouts based on decreasing of visual fidelity that roughly corresponds to the “squint test” widely used in practical usability engineering. To confirm applicability of the method and the predictive power of the compression algorithms, we ran two experimental surveys with over 4700 layout configurations, 21 real websites, and 149 participants overall. We found that the compression algorithms’ metrics were significant in VC models, but the classical purely informational Hick’s law metric was even more influential. Unexpectedly, algorithms with higher compression ratios that presumably come closer to the “real” Kolmogorov complexity did not explain layouts’ VC perception better. The proposed novel UI coding method and the analysis of the compression algorithms’ metrics can contribute to user behavior modeling in HCI and static testing of software UIs.
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Notes
- 1.
We are aware about the controversy existing in the research community about treating Likert and other ordinal scales as rational ones for some methods. In our analysis we tried to use methods appropriate for ordinal scales when possible, but nevertheless were not restricted to them, if more robust analysis could be performed. We ask the readers to judge for themselves whether the potential bias in the results overweighs their usefulness.
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Acknowledgement
The reported study was funded by Russian Ministry of Education and Science, according to the research project No. 2.2327.2017/4.6.
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Bakaev, M., Goltsova, E., Khvorostov, V., Razumnikova, O. (2019). Data Compression Algorithms in Analysis of UI Layouts Visual Complexity. In: Bjørner, N., Virbitskaite, I., Voronkov, A. (eds) Perspectives of System Informatics. PSI 2019. Lecture Notes in Computer Science(), vol 11964. Springer, Cham. https://doi.org/10.1007/978-3-030-37487-7_14
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