Skip to main content
SpringerLink
Log in
Book cover

International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management

IC3K 2013: Knowledge Discovery, Knowledge Engineering and Knowledge Management pp 245–259Cite as

Chunking Complexity Measurement for Requirements Quality Knowledge Representation

  • Conference paper
  • First Online:

  • 992 Accesses

Part of the book series: Communications in Computer and Information Science ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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. 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. Bøegh, J.: A new standard for quality requirements. IEEE Softw. 25(2), 57–63 (2008)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  8. Costello, R.J., Liu, D.-B.: Metrics for requirements engineering. J. Syst. Softw. 29(1), 39–63 (1995)

    Article  Google Scholar 

  9. Darcy, D.P., Kemerer, C.F., Software Complexity: Toward a Unified Theory of Coupling and Cohesion, 8 February 2002

    Google Scholar 

  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. Demarco, T.: Controlling Software Projects. Yourdon Press, Englewood Cliffs (1982)

    Google Scholar 

  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. 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. 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. 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. Evangelist, W.: Software complexity metric sensitivity to program structuring rules. J. Syst. Softw. 3(3), 231–243 (1983)

    Article  Google Scholar 

  17. Fagan, M.: Advances in Software Inspections. IEEE Trans. Softw. Eng. 12(7), 744–751 (1986)

    Article  Google Scholar 

  18. Fanmuy, G., Fraga, A., Llorens, J.: Requirements Verification in the Industry. CSDM, Paris, France (2011)

    Google Scholar 

  19. Farbey, B.: Software quality metrics: considerations about requirements and requirement specifications. Inf. Softw. Technol. 32(1), 60–64 (1990)

    Article  Google Scholar 

  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. Fenton, N.E., Pleeger, S.L.: Software Metrics: A Rigorous and Practical Approach, 2nd edn. International Thomson Computer Press, Boston (1997)

    Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  24. Henderson-Sellers, B.: Object-Oriented Metrics textendash Measures of Complexity. Prentice Hall PTR, New Jersey (1996)

    Google Scholar 

  25. Kemerer, C.F.: Progress, obstacles, and opportunities in software engineering economics. Commun. ACM 41, 63–66 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  27. Klemola, T.: A cognitive model for complexity metrics, vol. 13 (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  30. Pleeger, S.L.: Lessons learned in building a corporate metrics program. IEEE Softw. 10(3), 67–74 (1993)

    Article  Google Scholar 

  31. Purao, S., Vaishnavi, V.: Product Metrics for Object-Oriented Systems. ACM Comput. Surv. 35(2), 191–221 (2003)

    Article  Google Scholar 

  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: 978-0890068892

    Google Scholar 

  33. Ricker, M.: Requirements specification understandability evaluation with cohesion, context, and coupling. Ph.D. thesis, George Mason University, Fairfax, VA (1995)

    Google Scholar 

  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. 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. Weyuker, E.: Evaluating software complexity measures. IEEE Trans. Softw. Eng. 14(9), 1357–1365 (1988)

    Article  MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. George Mason University, Fairfax, VA, USA

    David C. Rine

  2. Carlos III of Madrid University, Madrid, Spain

    Anabel Fraga

Authors
  1. David C. Rine
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anabel Fraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anabel Fraga .

Editor information

Editors and Affiliations

  1. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  2. Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands

    Jan L.G. Dietz

  3. Henley Business School, University of Reading, Reading, United Kingdom

    Kecheng Liu

  4. INSTICC, Setubal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rine, D.C., Fraga, A. (2015). Chunking Complexity Measurement for Requirements Quality Knowledge Representation. In: Fred, A., Dietz, J., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2013. Communications in Computer and Information Science, vol 454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46549-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46549-3_16

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46548-6

  • Online ISBN: 978-3-662-46549-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation