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Understanding User Engagement With Multi-Representational License Comprehension Interfaces

Understanding User Engagement With Multi-Representational License Comprehension Interfaces

Mahugnon Olivier Avande, Robin A. Gandhi, Harvey Siy
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 19
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781799806080|DOI: 10.4018/IJOSSP.2020100102
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MLA

Avande, Mahugnon Olivier, et al. "Understanding User Engagement With Multi-Representational License Comprehension Interfaces." IJOSSP vol.11, no.4 2020: pp.27-45. http://doi.org/10.4018/IJOSSP.2020100102

APA

Avande, M. O., Gandhi, R. A., & Siy, H. (2020). Understanding User Engagement With Multi-Representational License Comprehension Interfaces. International Journal of Open Source Software and Processes (IJOSSP), 11(4), 27-45. http://doi.org/10.4018/IJOSSP.2020100102

Chicago

Avande, Mahugnon Olivier, Robin A. Gandhi, and Harvey Siy. "Understanding User Engagement With Multi-Representational License Comprehension Interfaces," International Journal of Open Source Software and Processes (IJOSSP) 11, no.4: 27-45. http://doi.org/10.4018/IJOSSP.2020100102

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Abstract

License information for any non-trivial open-source software demonstrates the growing complexity of compliance management. Studies have shown that understanding open-source licenses is difficult. Prior research has not examined how developers would use interfaces displaying license text and its graphical models in studying a license. Consequently, a repeatable eye tracking-based methodology was developed to study user engagement when exploring open-source rights and obligations in a multi-modal fashion. Experiences of 10 participants in an exploratory case study design indicate that eye-tracking is feasible to quantitatively and qualitatively observe distinct interaction patterns in the use of license comprehension interfaces. A low correlation was observed between self-reported usability survey data and eye-tracking data. Conversely, a high correlation between eye-tracker and mouse data suggests the use of either in future studies. This paper provides a framework to conduct such studies as an alternative to surveys while offering interesting hypotheses for future studies.

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