Recommendations for end-user development

Abstract

End-user development (EUD), the practice of users creating, modifying, or extending programs for personal use, is a valuable but often challenging task for nonprogrammers. From the beginning, EUD systems have shown that recommendations can improve the user experience. However, these usability improvements are limited by a reliance on handcrafted rules and heuristics to generate reasonable and useful suggestions. When the number of possible recommendations is large or the available context is too limited for traditional reasoning techniques, recommender technologies present a promising solution. In this paper, we provide an overview of the state of the art in end-user development, focusing on the different kinds of recommendations made to users. We identify four classes of suggestion that could most directly benefit from existing recommendation techniques. Along the way we explore straightforward applications of recommender algorithms as well as a few difficult but high-value recommendation problems in EUD. We discuss the ways that EUD systems have been evaluated in the past and suggest the modifications necessary to evaluate recommenders within the EUD context. We highlight EUD research as one area that can facilitate the transition of recommender system evaluation from algorithmic performance evaluation to a more user-centered approach. We conclude by restating our findings as a new set of research challenges for the recommender systems community.