Automated support for managing feature requests in open forums

ABSTRACT

The result is stable, focused, dynamic discussion threads that avoid redundant ideas and engage thousands of stakeholders.

REFERENCES

• 1: Basu, C., Hirsh, H., and Cohen, W. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 15th National Conference on Artificial Intelligence (Madison, WI, July 26--30). MIT Press, Cambridge, MA, 1998, 714--720.
• 2: Can, F. and Ozkarahan, E.A. Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases. ACM Transactions on Database Systems 15, 4 (Dec. 1990), 483--517.
• 3: Castro-Herrera, C., Duan, C., Cleland-Huang, J., and Mobasher, B. A recommender system for requirements elicitation in large-scale software projects. In Proceedings of the 2009 ACM Symposium on Applied Computing (Honolulu, HI, Mar. 9--12). ACM Press, New York, 2008, 1419--1426.
• 4: Davis, A., Dieste, O., Hickey, A., Juristo, N., and Moreno, A. Effectiveness of requirements elicitation techniques. In Proceedings of the 14th IEEE International Requirements Engineering Conference (Minneapolis, MN, Sept.). IEEE Computer Society, 2006, 179--188.
• 5: Dhillon, I.S. and Modha, D.S. Concept decompositions for large sparse text data using clustering. Machine Learning 42, 1--2 (Jan. 2001), 143--175.
• 6: Duan, C., Cleland-Huang, J., and Mobasher, B. A consensus-based approach to constrained clustering of software requirements. In Proceedings of the 17th ACM International Conference on Information and Knowledge Management (Napa, CA, Oct. 26--30). ACM Press, New York, 2008, 1073--1082.
• 7: Frakes, W.B. and Baeza-Yates, R. Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Englewood Cliffs, NJ, 1992.
• 8: Fred, A.L. and Jain, A.K. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 6 (June 2005), 835--850.
• 9: Second Life virtual 3D world; http://secondlife.com, feature requests downloaded from the Second Life issue tracker https://jira.secondlife.com/secure/Dashboard.jspa.
• 10: SourceForge. Repository of Open Source Code and Applications; feature requests for Open Bravo, ZIMBRA, PHPMyAdmin, and Mono downloaded from SourceForge forums http://sourceforge.net/.
• 11: Sugar CRM. Commercial open source customer relationship management software; http://www.sugarcrm.com/crm/; feature requests mined from feature requests at http://www.sugarcrm.com/forums/.
• 12: Wagstaff, K., Cardie, C., Rogers, S., and Schrödl, S. Constrained K-means clustering with background knowledge. In Proceedings of the 18th International Conference on Machine Learning (June 28--July 1). Morgan Kaufman Publishers, Inc., San Francisco, 2001, 577--584.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

Additional Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.0 General
      C.2.4 Distributed Systems

  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
  H.2 DATABASE MANAGEMENT
      H.2.0 General
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.0 General
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces

  J. Computer Applications
  J.0 GENERAL