Reactive information foraging: an empirical investigation of theory-based recommender systems for programmers.

Information Foraging Theory (IFT) has established itself as an important theory to explain how people seek information, but most work has focused more on the theory itself than on how best to apply it. In this paper, we investigate how to apply a reactive variant of IFT (Reactive IFT) to design IFT-based tools, with a special focus on such tools for ill-structured problems. Toward this end, we designed and implemented a variety of recommender algorithms to empirically investigate how to help people with the ill-structured problem of finding where to look for information while debugging source code. We varied the algorithms based on scent type supported (words alone vs. words + code structure), and based on use of foraging momentum to estimate rapidity of foragers' goal changes. Our empirical results showed that (1) using both words and code structure significantly improved the ability of the algorithms to recommend where software developers should look for information; (2) participants used recommendations to discover new places in the code and also as shortcuts to navigate to known places; and (3) low-momentum recommendations were significantly more useful than high-momentum recommendations, suggesting rapid and numerous goal changes in this type of setting. Overall, our contributions include two new recommendation algorithms, empirical evidence about when and why participants found IFT-based recommendations useful, and implications for the design of tools based on Reactive IFT.