Cart (Loading....) | Create Account
Close category search window

Predicting Project Outcome Leveraging Socio-Technical Network Patterns

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Surian, D. ; Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia ; Yuan Tian ; Lo, D. ; Hong Cheng
more authors

There are many software projects started daily, some are successful, while others are not. Successful projects get completed, are used by many people, and bring benefits to users. Failed projects do not bring similar benefits. In this work, we are interested in developing an effective machine learning solution that predicts project outcome (i.e., success or failures) from developer socio-technical network. To do so, we investigate successful and failed projects to find factors that differentiate the two. We analyze the socio-technical aspect of the software development process by focusing at the people that contribute to these projects and the interactions among them. We first form a collaboration graph for each software project. We then create a training set consisting of two graph databases corresponding to successful and failed projects respectively. A new data mining approach is then employed to extract discriminative rich patterns that appear frequently on the successful projects but rarely on the failed projects. We find that these automatically mined patterns are effective features to predict project outcomes. We experiment our solution on projects in Source Forge. Net, the largest open source software development portal, and show that under 10 fold cross validation, our approach could achieve an accuracy of more than 90% and an AUC score of 0.86. We also present and analyze some mined socio-technical patterns.

Published in:

Software Maintenance and Reengineering (CSMR), 2013 17th European Conference on

Date of Conference:

5-8 March 2013

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.