My Master’s student, Ovivier Avande’s thesis work was extended and developed into a Journal publication.

Journal Avande, M. Gandhi, R., Siy, H., Understanding User Engagement With Multi-Representational License Comprehension Interfaces, IJOSSP: Volume 11, Issue 4, Article 2, Accepted, December 2020.

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 multi-modal fashion. Experiences of ten 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.

My PhD student, Akshay Kale’s Master’s thesis work was extended and developed into a Journal publication.

Journal Kale, A., Ricks, B., & Gandhi, R. (2021) New Measure to Understand and Compare Bridge Conditions Based on​ Inspections Time-Series Data. Journal of infrastructure systems, doi:10.1061/(ASCE)IS.1943-555X.0000633.

Abstract

The C+ score for US bridges on the 2017 infrastructure report card underscores the need for improved data-driven methods to understand bridge performance. There is a lot of interest and prior work in using inspection records to determine bridge health scores. However, aggregating, cleaning, and analyzing bridge inspection records from all states and all past years is a challenging task, limiting access and reproducibility of findings. This research introduces a new score computed using inspection records from the National Bridge Inventory (NBI) dataset. Differences between the time-series of condition ratings for a bridge and a time-series of average national condition ratings by age are used to develop a health score for that bridge. This baseline difference score complements NBI condition ratings in further understanding a bridge’s performance over time. Moreover, the role of bridge attributes and environmental factors can be analyzed using the score. Such analysis shows that bridge material type has the highest association with the baseline difference score, followed by snowfall and maintenance. This research also makes a methodological contribution by outlining a data-driven approach for repeatable and scalable analysis of the NBI dataset.