Automatic Detection of Ambiguous Terminology for Software Requirements

  • Yue Wang
  • Irene L. Manotas Gutièrrez
  • Kristina Winbladh
  • Hui Fang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)


Identifying ambiguous requirements is an important aspect of software development, as it prevents design and implementation errors that are costly to correct. Unfortunately, few efforts have been made to automatically solve the problem. In this paper, we study the problem of lexical ambiguity detection and propose methods that can automatically identify potentially ambiguous concepts in software requirement specifications. Specifically, we focus on two types of lexical ambiguities, i.e., Overloaded and Synonymous ambiguity. Experiment results over four real-world software requirement collections show that the proposed methods are effective in detecting ambiguous terminology.


Ambiguity detection Software requirements Overloaded ambiguity Synonymous ambiguity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Berry, D.M.: Ambiguity in natural language requirements documents. In: Paech, B., Martell, C. (eds.) Monterey Workshop 2007. LNCS, vol. 5320, pp. 1–7. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Berry, D.M., Kamsties, E., Krieger, M.M.: From contract drafting to software specification: Linguistic sources of ambiguity (2003),
  3. 3.
    Boehm, B.W., Papaccio, P.N.: Understanding and controlling software costs. IEEE Transaction of Software Engineering 14, 1462–1477 (1988)CrossRefGoogle Scholar
  4. 4.
    Chantree, F., Nuseibeh, B., de Roeck, A., Willis, A.: Identifying nocuous ambiguities in natural language requirements. In: Proceedings of the 14th IEEE International Requirements Engineering Conference, Washington, DC, USA, pp. 56–65 (2006)Google Scholar
  5. 5.
    Cobleigh, R.L., Avrunin, G.S., Clarke, L.A.: User guidance for creating precise and accessible property specifications. In: ACM SIGSOFT 14th International Symposium on Foundations of Software Engineering, pp. 208–218 (2006)Google Scholar
  6. 6.
    Damas, C., Lambeau, B., Dupont, P., van Lamsweerde, A.: Generating annotated behavior models from end-user scenarios. IEEE Transaction of Software Engineering 31, 1056–1073 (2005)CrossRefGoogle Scholar
  7. 7.
    Frantzi, K., Ananiadou, S.: Extracting nested collocations. In: Proceedings of the 16th Conference on Computational Linguistics, vol. 1, pp. 41–46 (1996)Google Scholar
  8. 8.
    Greenspan, S., Mylopoulos, J., Borgida, A.: On formal requirements modeling languages: Rml revisited. In: Proceedings of the 16th International Conference on Software Engineering, Los Alamitos, CA, USA, pp. 135–147 (1994)Google Scholar
  9. 9.
    Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, Stroudsburg, PA, USA, vol. 2, pp. 539–545 (1992)Google Scholar
  10. 10.
    Hussain, I., Ormandjieva, O., Kosseim, L.: Automatic Quality Assessment of SRS Text by Means of a Decision-Tree-Based Text Classifier. In: Seventh International Conference on Quality Software (QSIC), pp. 209–218 (2007)Google Scholar
  11. 11.
    Ide, N., Véronis, J.: Word sense disambiguation: The state of the art. Computational Linguistics 24, 1–40 (1998)Google Scholar
  12. 12.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)zbMATHCrossRefGoogle Scholar
  13. 13.
    Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  14. 14.
    Maynard, D., Funk, A., Peters, W.: Using lexico-syntactic ontology design patterns for ontology creation and population. In: Proceedings of WOP 2009 Collocated with ISWC 2009, vol. 516 (2009)Google Scholar
  15. 15.
    Nikora, A., Hayes, J., Holbrook, E.: Experiments in Automated Identification of Ambiguous Natural-Language Requirements. In: Proc. 21st IEEE International Symposium on Software Reliability Engineering, San JoseGoogle Scholar
  16. 16.
    Porter, A., Votta, L.: Comparing detection methods for software requirements inspections: A replication using professional subjects. Empirical Software Engineering 3, 355–379 (1998)CrossRefGoogle Scholar
  17. 17.
    Porter, A.A., Votta Jr., L.G., Basili, V.R.: Comparing detection methods for software requirements inspections: A replicated experiment. IEEE Transaction of Software Engineering 21, 563–575 (1995)CrossRefGoogle Scholar
  18. 18.
    Reubenstein, H.B., Waters, R.C.: The requirements apprentice: an initial scenario. SIGSOFT Software Engineering Notes 14, 211–218 (1989)CrossRefGoogle Scholar
  19. 19.
    Roark, B., Charniak, E.: Noun-phrase co-occurrence statistics for semiautomatic semantic lexicon construction. In: Proceedings of the 17th International Conference on Computational Linguistics, Stroudsburg, PA, USA, vol. 2, pp. 1110–1116 (1998)Google Scholar
  20. 20.
    Shull, F., Rus, I., Basili, V.: How perspective-based reading can improve requirements inspections. Computer 33, 73–79 (2000)CrossRefGoogle Scholar
  21. 21.
    Tratz, S., Hovy, D.: Disambiguation of preposition sense using linguistically motivated features. In: HLT-NAACL (Student Research Workshop and Doctoral Consortium), pp. 96–100 (2009)Google Scholar
  22. 22.
    Umber, A., Bajwa, I.S.: Minimizing ambiguity in natural language software requirements specification. In: Digital Information Management (ICDIM), pp. 102–107 (2011)Google Scholar
  23. 23.
    van Lamsweerde, A.: Requirements Engineering: From System Goals to UML Models to Software Specifications. John Wiley & Sons (2009)Google Scholar
  24. 24.
    Zhang, X., Fang, A.: An ATE system based on probabilistic relations between terms and syntactic functions. In: 10th International Conference on Statistical Analysis of Textual Data - JADT 2010 (2010)Google Scholar
  25. 25.
    Zou, X., Settimi, R., Cleland-Huang, J.: Improving automated requirements trace retrieval: a study of term-based enhancement methods. In: Empirical Software Engineering, vol. 15, pp. 119–146 (2010)Google Scholar
  26. 26.
    Zowghi, D., Gervasi, V., McRae, A.: Using default reasoning to discover inconsistencies in natural language requirements. In: Proceedings of the Eighth Asia-Pacific on Software Engineering Conference, Washington, DC, USA, pp. 133–140 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yue Wang
    • 1
  • Irene L. Manotas Gutièrrez
    • 1
  • Kristina Winbladh
    • 1
  • Hui Fang
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of DelawareNewarkUSA

Personalised recommendations