ABSTRACT
Ultra-Large-Scale (ULS) software projects are anticipated to be highly complex and to involve thousands, or even hundreds of thousands of stakeholders. Unfortunately numerous accounts of recent failures and challenges in industrial and governmental projects have demonstrated that current requirements elicitation and prioritization practices do not scale adequately to address the needs of large projects. This position paper directly addresses this problem through proposing an open, inclusive, and robust elicitation and prioritization process that utilizes data-mining and recommender technologies to facilitate the active involvement of many thousands of stakeholders. We believe that the approach described in this paper is a fundamental building block towards addressing higher level requirements problems facing ULS Systems.
- Basu, C., Hirsh, H., & Cohen, W. "Recommendation as Classification: Using Social and Content-Based Information in Recommendation" National Conference on Artificial Intelligence, Madison, WI. (1998) 714--720. Google ScholarDigital Library
- Burke, R., Mobasher, B., Williams, C., & Bhaumik, R. "Detecting Profile Injection Attacks in Collaborative Recommender Systems", IEEE Joint Conf. on E-Commerce Technology and Enterprise Computing. (Palo-Alto, 2006) 23. Google ScholarDigital Library
- Cleland-Huang, J., Settimi, R., Zou, X., & Solc, P. "Automated Detection and Classification of Quality Requirements", Requirements Engineering Journal, Springer-Verlag, (August, 2007) 36--45. Google ScholarDigital Library
- Davis, A., Dieste, O., Hickey, A., Juristo, N., & Moreno, A. "Effectiveness of Requirements Elicitation Techniques", IEEE International Requirements Engineering Conference, (Minneapolis, MN, Sept. 2006) 179--188. Google ScholarDigital Library
- Duan, C., & Cleland-Huang, J. "A Clustering Technique for Early Detection of Dominant and Recessive Cross Cutting Concerns", Early Aspects. (Minneapolis, MN, 2007). Google ScholarDigital Library
- Duan, C., & Cleland-Huang, J., "Clustering Support for Automated Traceability", Automated Software Engineering, (Atlanta, Georgia, 2007) 244--253. Google ScholarDigital Library
- Duan, C., Clustering and its Application in Requirements Engineering, Technical Report #08-001, School of Computing., DePaul University, Available online at http://www.cs.depaul.edu (Chicago, Feb. 2008).Google Scholar
- Goldstein, H. "Who Killed the Virtual Case File?" IEEE Spectrum, 42,9 (2005) 24--35. Google ScholarDigital Library
- Gruenbacher, P.,, "Integrating Groupware and CASE Capabilities for Improving Stakeholder Involvement in Requirements Engineering," Euromicro, 2 (2000) 2232,Google Scholar
- Hooks, I. F., & Farry, K. Creating Successful Products Through Smart Requirements Management. New York: Amacon. (2001).Google Scholar
- Karlsson, J., & Ryan, K. "A Cost-Value Approach for Prioritizing Requirements", IEEE Software, 5 (1997), 67--75. Google ScholarDigital Library
- Lam, S., & Riedl, J. "Shilling Recommender Systems for Fun and Profit", Conf. on the World Wide Web. (2004) 393. Google ScholarDigital Library
- N. R. Mead, "Requirements Prioritization Introduction", Software Eng. Inst. web pub., Carnegie Mellon Univ., (2006).Google Scholar
- Mobasher, B., Burke, R., & Sandvig, J., "Model-based collaborative filtering as a defense against profile injection attacks", Proceedings of the 21st National Conference on Artificial Intelligence, (Boston, MA, 2006). Google ScholarDigital Library
- Mobasher, B., Burke, R., Bhaumik, F., & Williams, C. "Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness", ACM Transactions on Internet Technology, 7,4 (2007). Google ScholarDigital Library
- Northrop, L., Feiler, P., Gabriel, R., Goodenought, J., Linger, R., Longstaff, T., Kazman, R., Klein, M., Schmidt, D., Sullivan, K., Wallnau, K., Ultra-Large-Scale Systems: The Software Challenge of the Future, Technical Report, Software Engineering Institute, Carnegie Mellon, (June 2006).Google Scholar
- Nunamaker, J. F., Briggs, R., & Mittleman, D. "Lessons from a Decade of Group Support Systems Research", Hawaii International Conference on System Sciences, (Hawaii, 1996) 418--427. Google ScholarDigital Library
- Pazzani, M., & Billsus, D. "Content-Based Recommendation Systems". In P. Brusilovsky, A. Kobsa, & W. Nejdl, The Adaptive Web: Methods and Strategies of Web Personalization, Berlin Heidelberg NewYork: Springer-Verlag. (2007). Google ScholarDigital Library
- Sandvig, J. J., Mobasher, B., & Burke, R., "Attacks and Remedies in Collaborative Recommendation", Expert Sys, Special Issue on Recommender Sys., 22, 3, (2007). Google ScholarDigital Library
- Schafer, J. B., Frankowski, D., & Shilad, S., "Collaborative Filtering Recommender Systems" In P. Brusilovsky, A. Kobsa, & W. Nejdl, The Adaptive Web: Methods and Strategies of Web Personalization. New York: Springer-Verlag. (2007). Google ScholarDigital Library
- Wiegers, K. E., Software Requirements, Microsoft Press, Redmond, WA, (1999). Google ScholarDigital Library
Index Terms
- Using data mining and recommender systems to scale up the requirements process
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