Modeling context-aware command recommendation and acceptance in an IDE

Abstract

For software developers to use the full range of available commands in an integrated development environment, one has to provide proactive support which can suggest unknown commands that could be useful for the task at hand. Researchers started exploring the potential of recommender systems to provide this type of help, but so far there are still very few contributions. We propose a new multi-criteria context-aware rating prediction model that can be used to predict the user choice of either to accept or reject an IDE command recommendation. Individual command recommendation evaluation criteria are: performance expectancy, effort expectancy, and social influence; besides, the overall evaluation/rating is the intention to use a command. We have identified four types of contexts, namely, current practice, environment, interaction, and recommendation presentation context. The model is aimed at improving recommendation quality and enabling more effective recommendation presentations.