@conference {Venkataramani:2013:DTE:2487788.2487832,
	title = {Discovery of Technical Expertise from Open Source Code Repositories},
	booktitle = {Proceedings of the 22Nd International Conference on World Wide Web Companion},
	series = {WWW {\textquoteright}13 Companion},
	year = {2013},
	pages = {97{\textendash}98},
	publisher = {International World Wide Web Conferences Steering Committee},
	organization = {International World Wide Web Conferences Steering Committee},
	address = {Republic and Canton of Geneva, Switzerland},
	abstract = {Online Question and Answer websites for developers have emerged as the main forums for interaction during the software development process. The veracity of an answer in such websites is typically verified by the number of {\textquoteright}upvotes{\textquoteright} that the answer garners from peer programmers using the same forum. Although this mechanism has proved to be extremely successful in rating the usefulness of the answers, it does not lend itself very elegantly to model the expertise of a user in a particular domain. In this paper, we propose a model to rank the expertise of the developers in a target domain by mining their activity in different opensource projects. To demonstrate the validity of the model, we built a recommendation system for StackOverflow which uses the data mined from GitHub.
},
	keywords = {github, knowledge discovery, recommendations, source code repository, stackoverflow, technical expertise},
	isbn = {978-1-4503-2038-2},
	url = {http://dl.acm.org/citation.cfm?id=2487788.2487832},
	author = {Venkataramani, Rahul and Gupta, Atul and Asadullah, Allahbaksh and Muddu, Basavaraju and Bhat, Vasudev}
}
