RASCAL: A Recommender Agent for Software Components in an Agile Environment

As software organisations mature, their repository of reusable software components from previous projects will grow considerably. Remaining conversant with all components in such a repository presents a significant challenge to developers. Indeed the retrieval of a particular component in this large search space may prove problematic. We propose to infer the need for a component and proactively recommend that component to the developer using a technique which is consistent with the principles of Agile development. Our RASCAL recommender agent tracks usage histories of a group of users to recommend to an individual user components that are expected to be needed by that user. Unlike many traditional recommender systems we may recommend items which the developer has actually employed previously. We introduce a technique for ordering the set of recommended software components and present a comparative analysis of applying this technique to a number of Collaborative Filtering algorithms. We also investigate the relationship between the number of usage histories collected and recommendation accuracy. Our overall results indicate that RASCAL is a very promising tool for allowing developers discover reusable components at no additional cost.