Social Recommender System for Open Source Software Communities
This thesis aims to develop social recommender system service for Open Source Software (OSS) Communities.
Social Recommender Systems are successful in e-commerce application for suggesting/receiving new product information to/from trusted persons. Open source software (OSS) projects share similar features that make them accessible for recommender systems. Developers searching for new and exiting projects to put their efforts on are faced with an abundance of possibilities. OSS events where new projects are marketing themselves are therefore visited by thousands of developers. To support such match-making events the social recommender system should support the match-making process by analysing the features of OSS projects on public platforms like github and offer project a platform to meet developers and end-users. In future research the support can be extended for running projects offering more analytic capabilities for project management.
The thesis work is based on existing prototypes developed at the chair: the Requirements Bazaar, a platform for large-scale social requirements engineering, SWeVA, a Web-based platform for viusal analytics for OSS communities and AERCS, recommender system for computer scientists seeking collaboration opportunities.
The master thesis candidate should have knowledge on databases, Web, and XML technologies. Programming skills in Java are preferred. The thesis task can be adjusted for a bachelor thesis.