Requirements Engineering

, Volume 14, Issue 2, pp 73–89 | Cite as

Towards automated requirements prioritization and triage

  • Chuan Duan
  • Paula Laurent
  • Jane Cleland-HuangEmail author
  • Charles Kwiatkowski
Special Issue-RE'07 Best Papers


Time-to-market deadlines and budgetary restrictions require stakeholders to carefully prioritize requirements and determine which ones to implement in a given product release. Unfortunately, existing prioritization techniques do not provide sufficient automation for large projects with hundreds of stakeholders and thousands of potentially conflicting requests and requirements. This paper therefore describes a new approach for automating a significant part of the prioritization process. The proposed method utilizes data-mining and machine learning techniques to prioritize requirements according to stakeholders’ interests, business goals, and cross-cutting concerns such as security or performance requirements. The effectiveness of the approach is illustrated and evaluated through two case studies based on the requirements of the Ice Breaker System, and also on a set of stakeholders’ raw feature requests mined from the discussion forum of an open source product named SugarCRM.


Requirements prioritization Requirements triage Data mining Non-functional requirements 


  1. 1.
    Davis AM (2003) The art of requirements triage. IEEE Comput 36(3):42–49Google Scholar
  2. 2.
    Goldstein H (2005) Who killed the virtual case file? IEEE Spectr 42(9):24–35CrossRefGoogle Scholar
  3. 3.
    Standish Group (1995) CHAOS reportGoogle Scholar
  4. 4.
    Laurent P, Cleland-Huang J, Duan C (2007) Towards automated requirements triage. In: IEEE conference on requirements engineering, New DelhiGoogle Scholar
  5. 5.
    Brackett JW (1990) Software engineering. In: Proceedings of software engineering institute, 19(1.2). Carnegie Mellon University, PittsburghGoogle Scholar
  6. 6.
    Karlsson J (1995) Towards a strategy for software requirements selection. Licentiate Thesis 513, Department of Computer and Information Science, Linkoping UniversityGoogle Scholar
  7. 7.
    Beck K (2000) Extreme programming explained: embrace change. Addison-Wesley, ReadingGoogle Scholar
  8. 8.
    Wiegers KE (1999) Software requirements. Microsoft Press, RedmondGoogle Scholar
  9. 9.
    Leffingwell D, Widrig D (2003) Managing software requirements: a use case approach, 2nd edn. Addison-Wesley, BostonGoogle Scholar
  10. 10.
    Mead NR (2006) Requirements prioritization introduction. Software Engineering Institute Web Publication, Carnegie Mellon University, PittsburghGoogle Scholar
  11. 11.
    Karlsson J, Ryan K (1997) A cost-value approach for prioritizing requirements. IEEE Softw 14(5):67–75CrossRefGoogle Scholar
  12. 12.
    Boehm BW, Ross R (1989) Theory-W software project management: principles and examples. IEEE Trans Softw Eng 15(7):902–916CrossRefGoogle Scholar
  13. 13.
    Moisiadis F (2000) Prioritising scenario evolution. In: 4th international conference on requirements engineering, Schaumburg, pp 85–94Google Scholar
  14. 14.
    Azar J, Smith RK, Cordes D (2007) Value-oriented requirements prioritization in a small development organization. IEEE Softw 32–73Google Scholar
  15. 15.
    Cleland-Huang J, Settimi R, Duan C, Zou X (2005) Utilizing supporting evidence to improve dynamic requirements traceability. In: International requirements engineering conference, Paris, France, pp 135–144Google Scholar
  16. 16.
    Cleland-Huang J, Settimi R, BenKhadra O, Berezhanskaya E, Christina S (2005) Goal-centric traceability for managing non-functional requirements. In: International. conference on software engineering, pp 362–371Google Scholar
  17. 17.
    Robertson S, Robertson J (1999) Mastering the requirements process. Addison-Wesley, ReadingGoogle Scholar
  18. 18.
    Kowalski G (1997) Information retrieval systems—theory and implementation. Kluwer, DordrechtzbMATHGoogle Scholar
  19. 19.
    Cutting DR, Karger DR, Pedersen JO, Tukey JW (1992) Scatter/gather: a cluster-based approach to browsing large document collections. In: Conference on research and development in information retrieval, Copenhagen, Denmark, June 21–24, pp 318–329Google Scholar
  20. 20.
    Ertz L, Steinbach M, Kumar V (2001) Finding topics in collections of documents: a shared nearest neighbor approach. In: Text mine ‘01, workshop on text mining, first SIAM intn’l conf. on data mining, ChicagoGoogle Scholar
  21. 21.
    Zamir O, Etzioni O, Madani O, Karp RM (1997) Fast and intuitive clustering of web documents. In: Proceedings of the third international conference on knowledge discovery and data mining, 14–17 August, pp 287–290Google Scholar
  22. 22.
    Dhillon IS, Modha DS (2001) Concept decompositions for large sparse text data using clustering. Mach Learn 42(1/2):143–175zbMATHCrossRefGoogle Scholar
  23. 23.
    Steinbach M, Karypis G, Kumar V (2000) A comparison of document clustering techniques. KDD workshop on text miningGoogle Scholar
  24. 24.
    Hsia P, Hsu CT, Kung DC, Holder LB (1996) User-centered system decomposition: Z-based requirements clustering. In: Proceedings of the 2nd international conference on requirements engineering, Colorado Springs, p 126Google Scholar
  25. 25.
    Yaung AT (1992) Design and implementation of a requirements clustering analyzer for software system decomposition. In: ACM/SIGAPP symposium on applied computing: technological challenges of the 1990’s, Kansas City, pp 1048–1054Google Scholar
  26. 26.
    Al-Otaiby TN, AlSherif M, Bond WP (2005) Toward software requirements modularization using hierarchical clustering techniques. In: Proceedings of the 43rd annual southeast regional conference, vol 2, Kennesaw, GA, pp 223–228Google Scholar
  27. 27.
    Chen K, Zhang W, Zhao H, Mei H (2005) An approach to constructing feature models based on requirements clustering. In: International conference on requirements engineering, Paris, France, pp 31–40Google Scholar
  28. 28.
    Goldin L, Berry DM (1997) AbstFinder, a prototype natural language text abstraction finder for use in requirements elicitation. Autom Softw Eng 4(4):375–412CrossRefGoogle Scholar
  29. 29.
    Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  30. 30.
    Nuseibeh B (2001) Weaving together requirements and archi-tecture. IEEE Comput 34(3):115–117Google Scholar
  31. 31.
    Cleland-Huang J, Settimi R, Zou X, Solc P (2006) The detection and classification of non-functional requirements with application to early aspects. In: IEEE conference on requirements eng., Minneapolis, MN, pp 39–48Google Scholar
  32. 32.
    SugarCRM, Product Information. Available at
  33. 33.
    Cleland-Huang J, Habrat R (2007) Visual support in automated tracing. In: International workshop on requirements engineering visualization, New Delhi, India, OctoberGoogle Scholar
  34. 34.
    Can F, Ozkarahan EA (1990) Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases. ACM Trans Database Syst 15(4):483–517CrossRefGoogle Scholar
  35. 35.
    Duan C, Cleland-Huang J (2007) Clustering support for automated tracing. In: IEEE international conference on automated software engineering, Atlanta, Georgia, November, pp 244–253Google Scholar
  36. 36.
    Duan C (2008) Clustering and its application in requirements engineering. Technical report #08-001. School of Computing, DePaul UniversityGoogle Scholar
  37. 37.
    Cleland-Huang J, Berenbach B, Clark S, Settimi R, Romanova E (2007) Best practices for automated traceability. IEEE Comput 40(6):27–35Google Scholar
  38. 38.
    Denne M, Cleland-Huang J (2004) The incremental funding method, a data driven approach to software development. IEEE Softw 39–47Google Scholar
  39. 39.
    Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  40. 40.
    Duan C, Cleland-Huang J, Mobasher B (2008) A consensus based approach to constrained clustering of software requirements. In: Accepted at ACM 17th conference on information and knowledge management, California, OctoberGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Chuan Duan
    • 1
  • Paula Laurent
    • 1
  • Jane Cleland-Huang
    • 1
    Email author
  • Charles Kwiatkowski
    • 1
  1. 1.School of ComputingDePaul UniversityChicagoUSA

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