PFIS-V: Modeling Foraging Behavior in the Presence of Variants

PFIS-V: Modeling Foraging Behavior in the Presence of Variants
Sruti Srinivasa Ragavan, Bhargav Pandya, David Piorkowski, Charles Hill, Sandeep Kaur Kuttal, Anita Sarma, Margaret Burnett

CHI '17: ACM CHI Conference on Human Factors in Computing Systems
Session: Helping Software Developers

Abstract
Foraging among similar variants of the same artifact is a common activity, but computational models of Information Foraging Theory (IFT) have not been developed to take such variants into account. Without being able to computationally predict people's foraging behavior with variants, our ability to harness the theory in practical ways--such as building and systematically assessing tools for people who forage different variants of an artifact--is limited. Therefore, in this paper, we introduce a new predictive model, PFIS-V, that builds upon PFIS3, the most recent of the PFIS family of modeling IFT in programming situations. Our empirical results show that PFIS-V is up to 25% more accurate than PFIS3 in predicting where a forager will navigate in a variationed information space.

Recorded at the ACM CHI Conference on Human Factors in Computing Systems in Denver, CO, USA May 6-11, 2017