Interview Review: An Empirical Study on Detecting Ambiguities in Requirements Elicitation Interviews

  • Paola Spoletini
  • Alessio Ferrari
  • Muneera Bano
  • Didar Zowghi
  • Stefania Gnesi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10753)


[Context and Motivation] Ambiguities identified during requirements elicitation interviews can be used by the requirements analyst as triggers for additional questions and, consequently, for disclosing further – possibly tacit – knowledge. Therefore, every unidentified ambiguity may be a missed opportunity to collect additional information. [Question/problem] Ambiguities are not always easy to recognize, especially during highly interactive activities such as requirements elicitation interviews. Moreover, since different persons can perceive ambiguous situations differently, the unique perspective of the analyst in the interview might not be enough to identify all ambiguities. [Principal idea/results] To maximize the number of ambiguities recognized in interviews, this paper proposes a protocol to conduct reviews of requirements elicitation interviews. In the proposed protocol, the interviews are audio recorded and the recordings are inspected by both the analyst who performed the interview and another reviewer. The idea is to use the identified cases of ambiguity to create questions for the follow-up interviews. Our empirical evaluation of this protocol involves 42 students from Kennesaw State University and University of Technology Sydney. The study shows that, during the review, the analyst and the other reviewer identify 68% of the total number of ambiguities discovered, while 32% were identified during the interviews. Furthermore, the ambiguities identified by analysts and other reviewers during the review significantly differ from each other. [Contribution] Our results indicate that interview reviews allow the identification of a considerable number of undetected ambiguities, and can potentially be highly beneficial to discover unexpressed information in future interviews.


Requirements elicitation Interviews Ambiguities Tacit knowledge Reviews 


  1. 1.
    Davis, A., Dieste, O., Hickey, A., Juristo, N., Moreno, A.M.: Effectiveness of requirements elicitation techniques: empirical results derived from a systematic review. In: RE 2006, pp. 179–188. IEEE (2006)Google Scholar
  2. 2.
    Hadar, I., Soffer, P., Kenzi, K.: The role of domain knowledge in requirements elicitation via interviews: an exploratory study. REJ 19(2), 143–159 (2014)Google Scholar
  3. 3.
    Coughlan, J., Macredie, R.D.: Effective communication in requirements elicitation: a comparison of methodologies. Requir. Eng. 7(2), 47–60 (2002)CrossRefGoogle Scholar
  4. 4.
    Zowghi, D., Coulin, C.: Requirements elicitation: a survey of techniques, approaches, and tools. In: Aurum, A., Wohlin, C. (eds.) Engineering and Managing Software Requirements, pp. 19–46. Springer, Heidelberg (2005). CrossRefGoogle Scholar
  5. 5.
    Gervasi, V., Gacitua, R., Rouncefield, M., Sawyer, P., Kof, L., Ma, L., Piwek, P., De Roeck, A., Willis, A., Yang, H., et al.: Unpacking tacit knowledge for requirements engineering. In: Maalej, W., Thurimella, A. (eds.) Managing Requirements Knowledge, pp. 23–47. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  6. 6.
    Sutcliffe, A., Sawyer, P.: Requirements elicitation: towards the unknown unknowns. In: RE 2013, pp. 92–104. IEEE (2013)Google Scholar
  7. 7.
    Ferrari, A., Spoletini, P., Gnesi, S.: Ambiguity cues in requirements elicitation interviews. In: RE 2016, pp. 56–65. IEEE (2016)Google Scholar
  8. 8.
    Rugg, G., McGeorge, P., Maiden, N.: Method fragments. Expert Syst. 17(5), 248–257 (2000)CrossRefGoogle Scholar
  9. 9.
    Friedrich, W.R., Van Der Poll, J.A.: Towards a methodology to elicit tacit domain knowledge from users. IJIKM 2(1), 179–193 (2007)CrossRefGoogle Scholar
  10. 10.
    Ferrari, A., Spoletini, P., Gnesi, S.: Ambiguity as a resource to disclose tacit knowledge. In: RE 2015, pp. 26–35. IEEE (2015)Google Scholar
  11. 11.
    Salger, F.: Requirements reviews revisited: residual challenges and open research questions. In: RE 2013, pp. 250–255. IEEE (2013)Google Scholar
  12. 12.
    IEEE Std 1028–2008: IEEE Standard for Software Reviews and Audits (2008)Google Scholar
  13. 13.
    Laitenberger, O., DeBaud, J.M.: An encompassing life cycle centric survey of software inspection. JSS 50(1), 5–31 (2000)Google Scholar
  14. 14.
    Shull, F., Rus, I., Basili, V.: How perspective-based reading can improve requirements inspections. Computer 33(7), 73–79 (2000)CrossRefGoogle Scholar
  15. 15.
    Bacchelli, A., Bird, C.: Expectations, outcomes, and challenges of modern code review. In: ICSE 2013, pp. 712–721. IEEE (2013)Google Scholar
  16. 16.
    Rigby, P.C., Bird, C.: Convergent contemporary software peer review practices. In: FSE 2013, pp. 202–212. ACM (2013)Google Scholar
  17. 17.
    Fagan, M.E.: Design and code inspections to reduce errors in program development. IBM Syst. J. 15(3), 182–211 (1976)CrossRefGoogle Scholar
  18. 18.
    Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Are the perspectives really different? Further experimentation on scenario-based reading of requirements. In: Experimentation in Software Engineering, pp. 175–200. Springer, Heidelberg (2012).
  19. 19.
    Femmer, H., Hauptmann, B., Eder, S., Moser, D.: Quality assurance of requirements artifacts in practice: a case study and a process proposal. In: Abrahamsson, P., Jedlitschka, A., Nguyen Duc, A., Felderer, M., Amasaki, S., Mikkonen, T. (eds.) PROFES 2016. LNCS, vol. 10027, pp. 506–516. Springer, Cham (2016). CrossRefGoogle Scholar
  20. 20.
    Rosadini, B., Ferrari, A., Gori, G., Fantechi, A., Gnesi, S., Trotta, I., Bacherini, S.: Using NLP to detect requirements defects: an industrial experience in the railway domain. In: Grünbacher, P., Perini, A. (eds.) REFSQ 2017. LNCS, vol. 10153, pp. 344–360. Springer, Cham (2017). CrossRefGoogle Scholar
  21. 21.
    Massey, A.K., Rutledge, R.L., Anton, A.I., Swire, P.P.: Identifying and classifying ambiguity for regulatory requirements. In: RE 2014, pp. 83–92. IEEE (2014)Google Scholar
  22. 22.
    Ferrari, A., Spoletini, P., Donati, B., Zowghi, D., Gnesi, S.: Interview review: detecting latent ambiguities to improve the requirements elicitation process. In: RE 2017, pp. 400–405. IEEE (2017)Google Scholar
  23. 23.
    Kof, L.: From requirements documents to system models: a tool for interactive semi-automatic translation. In: RE 2010 (2010)Google Scholar
  24. 24.
    Ambriola, V., Gervasi, V.: On the systematic analysis of natural language requirements with CIRCE. ASE 13(1), 107–167 (2006)Google Scholar
  25. 25.
    Mich, L.: NL-OOPS: from natural language to object oriented requirements using the natural language processing system LOLITA. NLE 2(2), 161–187 (1996)Google Scholar
  26. 26.
    Mavin, A., Wilkinson, P., Harwood, A., Novak, M.: Easy approach to requirements syntax (ears). In: RE 2009, pp. 317–322. IEEE (2009)Google Scholar
  27. 27.
    Pohl, K., Rupp, C.: Requirements Engineering Fundamentals. Rocky Nook Inc., Santa Barbara (2011)Google Scholar
  28. 28.
    Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Automated checking of conformance to requirements templates using natural language processing. TSE 41(10), 944–968 (2015)Google Scholar
  29. 29.
    Berry, D.M., Kamsties, E., Krieger, M.M.: From contract drafting to software specification: linguistic sources of ambiguity (2003)Google Scholar
  30. 30.
    Gnesi, S., Lami, G., Trentanni, G.: An automatic tool for the analysis of natural language requirements. IJCSSE 20(1), 53–62 (2005)Google Scholar
  31. 31.
    Tjong, S.F., Berry, D.M.: The design of SREE — a prototype potential ambiguity finder for requirements specifications and lessons learned. In: Doerr, J., Opdahl, A.L. (eds.) REFSQ 2013. LNCS, vol. 7830, pp. 80–95. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  32. 32.
    Gleich, B., Creighton, O., Kof, L.: Ambiguity detection: towards a tool explaining ambiguity sources. In: Wieringa, R., Persson, A. (eds.) REFSQ 2010. LNCS, vol. 6182, pp. 218–232. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  33. 33.
    Femmer, H., Fernández, D.M., Wagner, S., Eder, S.: Rapid quality assurance with requirements smells. JSS 123, 190–213 (2017)Google Scholar
  34. 34.
    Chantree, F., Nuseibeh, B., de Roeck, A.N., Willis, A.: Identifying nocuous ambiguities in natural language requirements. In: RE 2006, pp. 56–65 (2006)Google Scholar
  35. 35.
    Yang, H., de Roeck, A.N., Gervasi, V., Willis, A., Nuseibeh, B.: Analysing anaphoric ambiguity in natural language requirements. Requir. Eng. 16(3), 163–189 (2011)CrossRefGoogle Scholar
  36. 36.
    Katasonov, A., Sakkinen, M.: Requirements quality control: a unifying framework. REJ 11(1), 42–57 (2006)Google Scholar
  37. 37.
    Aurum, A., Petersson, H., Wohlin, C.: State-of-the-art: software inspections after 25 years. Softw. Testing Verification Reliab. 12(3), 133–154 (2002)CrossRefGoogle Scholar
  38. 38.
    Karras, O., Kiesling, S., Schneider, K.: Supporting requirements elicitation by tool-supported video analysis. In: RE 2016, pp. 146–155. IEEE (2016)Google Scholar
  39. 39.
    Sharp, H., Rogers, Y., Preece, J.: Interaction Design: Beyond Human Computer Interaction, 4th edn. Wiley, New York (2015)Google Scholar
  40. 40.
    Höst, M., Regnell, B., Wohlin, C.: Using students as subjects, a comparative study of students and professionals in lead-time impact assessment. ESE 5(3), 201–214 (2000)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Kennesaw State UniversityKennesawUSA
  2. 2.CNR-ISTIPisaItaly
  3. 3.Swinburne University of TechnologyMelbourneAustralia
  4. 4.University of Technology SydneyUltimoAustralia

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