The COTECOMO: COnstractive Test Effort COst MOdel

  • Ljubomir Lazić
  • Nikos Mastorakis
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 28)


The primary purpose of Software Testing Process and Evaluation (STP&E) is to reduce risk. While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem which is inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. This article compares the approaches taken by three (COCOMO II, FP, UCP) widely used models for software cost and schedule estimation to develop COTECOMO (COnstractive Test Effort COst MOdel). It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability. This paper proposes Software/System Test Point (STP), a new metric for estimating overall software testing process. STP covers so-called black-box testing; an estimate for the test activities, which precede scenarios (threads) testing (white-box testing included), will already have been included in the estimate produced by function point analysis. Software test point is a useful metric for test managers interested in estimating software test effort, and the metric aids in the precise estimation of project effort and addresses the interests of metric group.


Unify Modeling Language Function Point Test Effort Software Project Estimation Methodology 
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.


  1. 1.
    Lazić Lj, Velašević D, Mastorakis N (2003) A framework of integrated and optimized software testing process. In: WSEAS Conference, August 11–13, Crete, Greece, also in WSEAS TRANSACTIONS on COMPUTERS 2(1)Google Scholar
  2. 2.
    Lazić Lj, Velašević D (2004) Applying simulation and design of experiments to the embedded software testing process. Software Testing, Verification and Reliability, John Willey & Sons Ltd., 14(4):257–282CrossRefGoogle Scholar
  3. 3.
    Lazić Lj, Mastorakis N (2005) RBOSTP: Risk-based optimization of software testing process-Part 2. WSEAS Transactions on Information Science and Applications, ISSN 1790-0832, 2(7):902–916Google Scholar
  4. 4.
    Lazić Lj, Mastorakis N (2007) A framework of software testing metrics – Part 1 and 2. In: WSEAS Engineering Education 2007 Multiconference, Agios Nikolaos, Crete Island, Greece, pp 23–28Google Scholar
  5. 5.
    Jones C (1998) Estimating software costs. McGraw-HillGoogle Scholar
  6. 6.
    Nageswaran Suresh (2001) Test effort estimation using use case points. In: Quality Week 2001, San Francisco, Caifornia, USA, < cogcommunity/presentations/Test_ Effort_Estimation.pdf>
  7. 7.
    Carbone M, Santucci G (2002) Fast&&Serious: a UML based metric for effort estimation. In: Proceedings of the 6th ECOOP Workshop on Quantitative Approaches in Object-Oriented Software Engineering (QAOOSE’02), SpainGoogle Scholar
  8. 8.
    Garmus D, Herron D (2001) Function point analysis. Addison-Wesley, ISBN 0-201-69944-3Google Scholar
  9. 9.
    Veenendaal EPWM van, Dekkers T (1999) Test point analysis: a method for test estimation. In: Kusters R, Cowderoy A, Heemstra F, Veenendaal E van (eds) Project control for software quality. Shaker Publishing BV, Maastricht, The NetherlandsGoogle Scholar
  10. 10.
    Boehm B (1981) Software engineering economics. Prentice HallMATHGoogle Scholar
  11. 11.
    Boehm B (2000) Safe and simple software cost analysis. IEEE Software, September/October 2000, pp 14–17, Available from /certification/beta/Boehm Safe.pdf
  12. 12.
    Boehm B, Horowitz E, Madachy R, Reifer D, Clark BK, Steece B, Brown AW, Chulani S, and Abts C (2000) Software cost estimation with cocomo II. Prentice HallGoogle Scholar
  13. 13.
    Hall M, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6):1437–1447CrossRefGoogle Scholar
  14. 14.
    Jorgensen M (2004) A review of studies on expert estimation of software development effort. J Syst Soft 70(1–2):37–60CrossRefGoogle Scholar
  15. 15.
    Menzies Tim, Chen Zhihao, Hihn Jairus, Lum Karen (2006) Selecting best practices for effort estimation. IEEE Trans Soft Eng 32(11)Google Scholar
  16. 16.
    Clemmons Roy K (2006) Project estimation with use case points. CrossTalk Feb .Google Scholar
  17. 17.
    Uemura T, Kusumoto S, Inoue K (1999) Function point measurement tool for UML design specification. In: Proceedings of the 6th Int’l IEEE Software Metrics Symposium, IEEE-CS Press, pp 62–69Google Scholar
  18. 18.
    Karner Gustav (1993) Resource estimation for objectory projects. Objective Systems SF ABGoogle Scholar
  19. 19.
    Anda Bente (2003) Improving estimation practices by applying use case models.
  20. 20.
    Anda Bente et al. (2005) Effort estimation of use cases for incremental large-scale software development. In: 27th International Conference on Software Engineering, St Louis, MO, pp 303–311Google Scholar
  21. 21.
    Carroll Edward R (2005) Estimating software based on use case points. In: 2005 Object-Oriented, Programming, Systems, Languages, and Applications (OOPSLA) Conference, San Diego, CAGoogle Scholar
  22. 22.
    Nagappan N, Williams L, Vouk M, Osborne J (2005) Early estimation of software quality using in-process testing metrics: a controlled case study. In: Third Software Quality Workshop, co-located with the International Conference on Software Engineering (ICSE 2005), pp 46–52Google Scholar
  23. 23.
    Carper Jones (2004) Software project management practices: failure versus success. CrossTalk Oct.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Electrical EngineeringVojvode Stepe 283Serbia

Personalised recommendations