A Source Code Recommender System to Support Newcomers

Abstract

Newcomers in a software development project often need assistance to complete their first tasks. Then a mentor, an experienced member of the team, usually teaches the newcomers what they need to complete their tasks. But, to allocate an experienced member of a team to teach a newcomer during a long time is neither always possible nor desirable, because the mentor could be more helpful doing more important tasks. During the development the team interacts with a version control system, bug tracking and mailing lists, and all these tools record data creating the project memory. Recommender systems can use the project memory to help newcomers in some tasks answering their questions, thus in some cases the developers do not need a mentor. In this paper we present Mentor, a recommender system to help newcomers to solve change requests. Mentor uses the Prediction by Partial Matching (PPM) algorithm and some heuristics to analyze the change requests, and the version control data, and recommend potentially relevant source code that will help the developer in the change request solution. We did three experiments to compare the PPM algorithm with the Latent Semantic Indexing (LSI). Using PPM we achieved results for recall rate between 37% and 66.8%, and using LSI the results were between 20.3% and 51.6%.