The Case for Dumb Requirements Engineering Tools

  • Daniel Berry
  • Ricardo Gacitua
  • Pete Sawyer
  • Sri Fatimah Tjong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7195)

Abstract

[Context and Motivation] This paper notes the advanced state of the natural language (NL) processing art and considers four broad categories of tools for processing NL requirements documents. These tools are used in a variety of scenarios. The strength of a tool for a NL processing task is measured by its recall and precision. [Question/Problem] In some scenarios, for some tasks, any tool with less than 100% recall is not helpful and the user may be better off doing the task entirely manually. [Principal Ideas/Results] The paper suggests that perhaps a dumb tool doing an identifiable part of such a task may be better than an intelligent tool trying but failing in unidentifiable ways to do the entire task. [Contribution] Perhaps a new direction is needed in research for RE tools.

Keywords

Requirement Engineer Requirement Engineer Trace Link Intelligent Tool Lexical Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mich, L., Franch, M., Inverardi, P.N.: Market research for requirements analysis using linguistic tools. Requirements Engineering Journal 9, 40–56 (2004)CrossRefGoogle Scholar
  2. 2.
    Wilson, W.M., Rosenberg, L.H., Hyatt, L.E.: Automated analysis of requirement specifications. In: Proc. 19th Int. Conf. on Software Engineering (ICSE), pp. 161–171 (1997)Google Scholar
  3. 3.
    Bucchiarone, A., Gnesi, S., Pierini, P.: Quality analysis of NL requirements: An industrial case study. In: Proc. 13th IEEE Int. Requirements Engineering Conf. (RE), pp. 390–394 (2005)Google Scholar
  4. 4.
    Tjong, S.F.: Avoiding Ambiguities in Requirements Specifications. PhD thesis, University of Nottingham, Maylasia Campus (2008)Google Scholar
  5. 5.
    Chantree, F., Nuseibeh, B., de Roeck, A., Willis, A.: Identifying nocuous ambiguities in natural language requirements. In: Proc. 14th IEEE Int. Requirements Engineering Conf. (RE), pp. 56–65 (2006)Google Scholar
  6. 6.
    Kof, L.: Scenarios: Identifying missing objects and actions by means of computational linguistics. In: Proc. 15th IEEE Int. Requirements Engineering Conf. (RE), pp. 121–130 (2007)Google Scholar
  7. 7.
    Popescu, D., Rugaber, S., Medvidovic, N., Berry, D.M.: Reducing ambiguities in requirements specifications via automatically created object-oriented models. In: Paech, B., Martell, C. (eds.) Innovations for Requirement Analysis: From Stakeholders’ Needs to Formal Designs, pp. 103–124 (2008)Google Scholar
  8. 8.
    Cleland-Huang, J., Berenbach, B., Clark, S., Settimi, R., Romanova, E.: Best practices for automated traceability. IEEE Computer 40, 27–35 (2007)CrossRefGoogle Scholar
  9. 9.
    Hayes, J.H., Dekhtyar, A., Sundaram, S.K.: Advancing candidate link generation for requirements tracing: The study of methods. IEEE Transactions on Software Engineering 32, 4–19 (2006)CrossRefGoogle Scholar
  10. 10.
    Goldin, L., Berry, D.M.: AbstFinder: A prototype abstraction finder for natural language text for use in requirements elicitation. Automated Software Engineering 4, 375–412 (1997)CrossRefGoogle Scholar
  11. 11.
    Gacitua, R., Sawyer, P., Gervasi, V.: On the effectiveness of abstraction identification in requirements engineering. In: Proc. 18th IEEE Int. Requirements Engineering Conf. (RE), pp. 5–14 (2010)Google Scholar
  12. 12.
    Ryan, K.: The role of natural language in requirements engineering. In: Proc. IEEE Int. Symp. on Requirements Engineering (RE), pp. 240–242 (1993)Google Scholar
  13. 13.
    Viller, S., Bowers, J., Rodden, T.: Human factors in requirements engineering: A survey of human sciences literature relevant to the improvement of dependable systems development processes. Interacting with Computers 11, 665–698 (1999)CrossRefGoogle Scholar
  14. 14.
    Dekhtyar, A., Dekhtyar, O., Holden, J., Hayes, J., Cuddeback, D., Kong, W.K.: On human analyst performance in assisted requirements tracing: Statistical analysis. In: Proc. 19th IEEE Int. Requirements Engineering Conf. (RE), pp. 111–120 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Berry
    • 1
  • Ricardo Gacitua
    • 2
  • Pete Sawyer
    • 2
    • 4
  • Sri Fatimah Tjong
    • 3
  1. 1.Cheriton School of Computer ScienceUniversity of WaterlooCanada
  2. 2.School of Computing and CommunicationsLancaster UniversityUK
  3. 3.University of Nottingham Malaysia CampusMalaysia
  4. 4.INRIA Paris — RocquencourtLe ChesnayFrance

Personalised recommendations