Advertisement

Chunking Complexity Measurement for Requirements Quality Knowledge Representation

  • David C. Rine
  • Anabel FragaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 454)

Abstract

In order to obtain a most effective return on a software project investment, then at least one requirements inspection shall be completed. A formal requirement inspection identifies low quality knowledge representation content in the requirements document. In software development projects where natural language requirements are produced, a requirements document summarizes the results of requirements knowledge analysis and becomes the basis for subsequent software development. In many cases, the knowledge content quality of the requirements documents dictates the success of the software development. The need for determining knowledge quality of requirements documents is particularly acute when the target applications are large, complicated, and mission critical. The goal of this research is to develop knowledge content quality indicators of requirements statements in a requirements document prior to informal inspections. To achieve the goal, knowledge quality properties of the requirements statements are adopted to represent the quality of requirements statements. A suite of complexity metrics for requirements statements is used as knowledge quality indicators and is developed based upon natural language knowledge research of noun phrase (NP) chunks. A formal requirements inspection identifies low quality knowledge representation content in the requirements document. The knowledge quality of requirements statements of requirements documents is one of the most important assets a project must inspect. An application of the metrics to improve requirements understandability and readability during requirements inspections can be built upon the metrics shown and suggested to be taken into account.

Keywords

Requirements inspections Chunking and cognition Complexity metrics Cohesion Coupling NP chunk Requirements Software quality Information retrieval Natural language understanding and processing 

References

  1. 1.
    Abney, S.: Parsing by chunks. In: Berwick, R., Abney, S., Tenny, C. (eds.) Principle-Based Parsing. Kluwer Academic Publishers, Dordrecht (1991)Google Scholar
  2. 2.
    Basili, V.R.: Qualitative Software Complexity Models: A Summary, Tutorial on Models and Methods for Software Management and Engineering. IEEE Computer Society Press, Los Alamitors (1980)Google Scholar
  3. 3.
    Bøegh, J.: A new standard for quality requirements. IEEE Softw. 25(2), 57–63 (2008)CrossRefGoogle Scholar
  4. 4.
    Briand, L.C., Daly, J.W., Wust, J.K.: A unified framework for cohesion measurement in object-oriented systems. IEEE Trans. Softw. Eng. 3(1), 65–117 (1998)Google Scholar
  5. 5.
    Briand, L.C., Daly, J.W., Wust, J.K.: A unified framework for coupling measurement in object-oriented systems. IEEE Trans. Softw. Eng. 25, 91–121 (1999)CrossRefGoogle Scholar
  6. 6.
    Cant, S., Jeffery, D.R., Henderson-Sellers, B.: A conceptual model of cognitive complexity of elements of the programming process. Inf. Softw. Technol. 37(7), 351–362 (1995)CrossRefGoogle Scholar
  7. 7.
    Chung, L., do Prado Leite, J.C.S.: On non-functional requirements in software engineering. In: Borgida, A.T., Chaudhri, V.K., Giorgini, P., Yu, E.S. (eds.) Conceptual Modeling: Foundations and Applications. LNCS, vol. 5600, pp. 363–379. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Costello, R.J., Liu, D.-B.: Metrics for requirements engineering. J. Syst. Softw. 29(1), 39–63 (1995)CrossRefGoogle Scholar
  9. 9.
    Darcy, D.P., Kemerer, C.F., Software Complexity: Toward a Unified Theory of Coupling and Cohesion, 8 February 2002Google Scholar
  10. 10.
    Davis, A., Overmyer, S., Caruso, J., Dandashi, F., Dinh, A.: Identifying and measuring quality in a software requirements specification. In: Proceedings of the First International Software Metrics Symposium, 21–22 May, pp. 141–152 (1993)Google Scholar
  11. 11.
    Demarco, T.: Controlling Software Projects. Yourdon Press, Englewood Cliffs (1982)Google Scholar
  12. 12.
    Din, C.Y.: Requirements content goodness and complexity measurement based on NP chunks. Ph.D. thesis, George Mason University, Fairfax, VA, 2007, Reprinted by VDM Verlag Dr. Muller (2008)Google Scholar
  13. 13.
    Din, C.Y., Rine, D.C.: Requirements content goodness and complexity measurement based on NP chunks. In: Proceedings, Complexity and Intelligence of the Artificial Systems: Bio-inspired Computational Methods and Computational Methods Applied in Medicine, WMSCI 2008 Conference (2008)Google Scholar
  14. 14.
    Din, C.Y., Rine, D.C.: Requirements metrics for requirements statements stored in a database. In: Proceedings of the 2012 International Conference on Software Engineering Research and Practice, SERP 2012, July 16–19, pp. 1–7 (2012)Google Scholar
  15. 15.
    Din, C.Y., Rine, D.C.: Requirements Statements Content Goodness and Complexity Measurement. International Journal of Next-Generation Computing. 4(1) (2013)Google Scholar
  16. 16.
    Evangelist, W.: Software complexity metric sensitivity to program structuring rules. J. Syst. Softw. 3(3), 231–243 (1983)CrossRefGoogle Scholar
  17. 17.
    Fagan, M.: Advances in Software Inspections. IEEE Trans. Softw. Eng. 12(7), 744–751 (1986)CrossRefGoogle Scholar
  18. 18.
    Fanmuy, G., Fraga, A., Llorens, J.: Requirements Verification in the Industry. CSDM, Paris, France (2011)Google Scholar
  19. 19.
    Farbey, B.: Software quality metrics: considerations about requirements and requirement specifications. Inf. Softw. Technol. 32(1), 60–64 (1990)CrossRefGoogle Scholar
  20. 20.
    Fenton, N.E., Neil, M.: Software metrics: roadmap. In: Proceedings of the International Conference on Software Engineering (ICSE), pp. 357–370 (2000)Google Scholar
  21. 21.
    Fenton, N.E., Pleeger, S.L.: Software Metrics: A Rigorous and Practical Approach, 2nd edn. International Thomson Computer Press, Boston (1997)Google Scholar
  22. 22.
    Genova, G., et al.: A framework to measure and improve the quality of textual requirements. Requirements Eng. 18(1), 25–41 (2013). doi: 10.1007/s00766-011-0134-z. Url: http://dx.doi.org/10.1007/s00766-011-0134-z CrossRefMathSciNetGoogle Scholar
  23. 23.
    Graesser, A.C., Mcnamara, D.S., Louwerse, M.M., Cai, Z.: Coh-Metrix: analysis of text on cohesion and language. Behav. Res. Methods Instrum. Comput. 36(2), 193–202 (2004)CrossRefGoogle Scholar
  24. 24.
    Henderson-Sellers, B.: Object-Oriented Metrics textendash Measures of Complexity. Prentice Hall PTR, New Jersey (1996)Google Scholar
  25. 25.
    Kemerer, C.F.: Progress, obstacles, and opportunities in software engineering economics. Commun. ACM 41, 63–66 (1998)CrossRefGoogle Scholar
  26. 26.
    Kitchenham, B.A., Pleeger, S.L., Fenton, N.E.: Towards a framework for software measurement validation. IEEE Trans. Softw. Eng. 21, 929–943 (1995)CrossRefGoogle Scholar
  27. 27.
    Klemola, T.: A cognitive model for complexity metrics, vol. 13 (2000)Google Scholar
  28. 28.
    Mcnamara, D.S.: Reading both high and low coherence texts: effects of text sequence and prior knowledge. Can. J. Exp. Psychol. 55, 51–62 (2001)CrossRefGoogle Scholar
  29. 29.
    Mcnamara, D.S., Kintsch, E., Songer, N.B., Kintsch, W.: Are good texts always better? Text coherence, background knowledge, and levels of understanding in learning from text. Cogn. Instr. 14, 1–43 (1996)CrossRefGoogle Scholar
  30. 30.
    Pleeger, S.L.: Lessons learned in building a corporate metrics program. IEEE Softw. 10(3), 67–74 (1993)CrossRefGoogle Scholar
  31. 31.
    Purao, S., Vaishnavi, V.: Product Metrics for Object-Oriented Systems. ACM Comput. Surv. 35(2), 191–221 (2003)CrossRefGoogle Scholar
  32. 32.
    Rakitin, S.: Software verification and validation: a practitioner’s guide (Artech House Computer Library). Artech House Publishers, Norwood (1997). ISBN-10: 0890068895 ISBN-13: 978-0890068892Google Scholar
  33. 33.
    Ricker, M.: Requirements specification understandability evaluation with cohesion, context, and coupling. Ph.D. thesis, George Mason University, Fairfax, VA (1995)Google Scholar
  34. 34.
    Schneider, R.E., Buede D.,: Criteria for selecting properties of a high quality informal requirements document. In: Proceedings of the International Conference on Systems Engineering, Mid-Atlantic Regional Conference, INCOSE-MARC, 5–8 April 2000a, pp. 7.2-1–7.2-5 (2000)Google Scholar
  35. 35.
    Schneider, R.E., Buede D.: Properties of a high quality informal requirements document. In: Proceedings of the Tenth Annual International Conference on Systems Engineering, INCOSE, 16–20 July, 2000b, pp. 377–384 (2000)Google Scholar
  36. 36.
    Weyuker, E.: Evaluating software complexity measures. IEEE Trans. Softw. Eng. 14(9), 1357–1365 (1988)CrossRefMathSciNetGoogle Scholar
  37. 37.
    Wnuk, K., Regnell, B., Berenbach, B.: Scaling up requirements engineering – exploring the challenges of increasing size and complexity in market-driven software development. In: Berry, D. (ed.) REFSQ 2011. LNCS, vol. 6606, pp. 54–59. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.George Mason UniversityFairfaxUSA
  2. 2.Carlos III of Madrid UniversityMadridSpain

Personalised recommendations