Automated support for managing feature requests in open forums

Communications of the ACM

Volumn 52, Issue 10, 2009, Pages 68-74

  Cleland Huang, Jane ^a   Dumitru, Horatiu ^b   Duan, Chuan ^a   Castro Herrera, Carlos ^a  

^a DEPAUL UNIVERSITY   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AFM; AUTOMATED SUPPORT; CLUSTERING PROCESS; FEATURE REQUESTS; HIGH QUALITY; LARGE PROJECT; USER FEEDBACK;

CLUSTERING ALGORITHMS;

AUTOMATION;

EID: 68749118791     PISSN: 00010782     EISSN: 15577317     Source Type: Journal    
DOI: 10.1145/1562764.1562784     Document Type: Article

References (12)

1. Basu, C., Hirsh, H., and cohen, W. recommendation as classifcation: using social and content-based information in recommendation. in Proceedings of the 15th National Conference on Artifcial 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-coeffcient-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.