Skip to main content

Abstract

Project management activities such as scheduling and project progress management are important to avoid project failure. As a basis of project management, effort estimation plays a fundamental role. To estimate software development effort by mathematical models, variables which are fixed before the estimation are used as independent variables. Some studies used team size and project duration as independent variables. Although they are sometimes fixed because of the limitation of human resources or business schedule, they may change by the end of the project. For instance, when delivery is delayed, actual duration and estimated duration is different. So, although using team size and project duration may enhance estimation accuracy, the error may also lower the accuracy. To help practitioners to select independent variables, we analyzed whether team size and duration should be used or not, when we consider the error included in the team size and the duration. In the experiment, we assumed that duration and team size include errors when effort is estimated. To analyze influence of the errors, we add n% errors to duration and team size. As a result, using duration as an independent variable was not very effective in many cases. In contrast, using maximum team size as an independent variable was effective when the error rate is equal or less than 50%.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Azzeh, M., Neagu, D., Cowling, P.: Fuzzy grey relational analysis for software effort estimation. Empir. Softw. Eng. 15(1), 60–90 (2010)

    Article  Google Scholar 

  2. Boehm, B.: Software engineering economics. Prentice Hall (1981)

    Google Scholar 

  3. Boetticher, G. Menzies, T., Ostrand, T.: PROMISE repository of empirical software engineering data. West Virginia University, Department of Computer Science (2007)

    Google Scholar 

  4. Conte, S., Dunsmore, H., Shen, V.: Software Engineering, Metrics and Models. Benjamin/Cummings, Redwood City (1986)

    Google Scholar 

  5. Desharnais, J.: Analyse Statistique de la Productivitie des Projets Informatique a Partie de la Technique des Point des Function. Master thesis, University of Montreal, 1989 (1989)

    Google Scholar 

  6. International Software Benchmarking Standards Group (ISBSG): ISBSG Estimating: Benchmarking and research suite, ISBSG (2004)

    Google Scholar 

  7. Jeffery, R., Ruhe, M., Wieczorek, I.: Using public domain metrics to estimate software development effort. In: Proceedings of the International Symposium on Software (METRICS), pp. 16–27 (2001)

    Google Scholar 

  8. Keung, J., Kitchenham, B., Jeffery, R.: Analogy-X: providing statistical inference to analogy-based software cost estimation. IEEE. Trans. Softw. Eng. 34(4), 471–484 (2008)

    Article  Google Scholar 

  9. Kirsopp, C., Mendes, E., Premraj, R., Shepperd, M.: An empirical analysis of linear adaptation techniques for case-based prediction. In: Proceedings of International Conference on Case-Based Reasoning, pp. 231–245 (2003)

    Google Scholar 

  10. Kitchenham, B., Mendes, E.: Why comparative effort prediction studies may be invalid. In: Proceedings of International Conference on Predictor Models in Software Engineering (PROMISE), art 4, p. 5 (2009)

    Google Scholar 

  11. Kitchenham, B., Pfleeger, S., McColl, B., Eagan, S.: An empirical study of maintenance and development estimation accuracy. J. Syst. Softw. 64(1), 57–77 (2004)

    Article  Google Scholar 

  12. Li, Y., Xie, M., Goh, T.: A study of the non-linear adjustment for analogy based software cost estimation. Empir. Softw. Eng. 14(6), 603–643 (2009)

    Article  Google Scholar 

  13. Li, J., Ruhe, G.: Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+. Empir. Softw. Eng. 13(1), 63–96 (2008)

    Article  Google Scholar 

  14. Lokan, C.: What should you optimize when building an estimation model? In: Proceedings of International Software Metrics Symposium (METRICS), p. 34. Como, Italy (2005)

    Google Scholar 

  15. Lokan, C., Mendes, E.: Cross-company and single-company effort models using the ISBSG Database: a further replicated study. In: Proceedings of the International Symposium on Empirical Software Engineering (ISESE), pp. 75–84 (2006)

    Google Scholar 

  16. Lokan, C., Wright, T., Hill, P., Stringer, M.: Organizational benchmarking using the ISBSG data repository. IEEE Softw. 18(5), 26–32 (2001)

    Article  Google Scholar 

  17. Maxwell, K., Forselius, P.: Benchmarking software development productivity. IEEE Softw. 17(1), 80–88 (2000)

    Article  Google Scholar 

  18. Maxwell, K., Wassenhove, L., Dutta, S.: Software development productivity of european space, military, and industrial applications. IEEE Trans. Softw. Eng. 22(10), 706–718 (1996)

    Article  Google Scholar 

  19. Mendes, E., Mosley, N., Counsell, S.: A replicated assessment of the use of adaptation rules to improve web cost estimation. In: Proceedings of the International Symposium on Empirical Software Engineering (ISESE), pp. 100–109 (2003)

    Google Scholar 

  20. Miyazaki, Y., Terakado, M., Ozaki, K., Nozaki, H.: Robust regression for developing software estimation models. J. Syst. Softw. 27(1), 3–16 (1994)

    Article  Google Scholar 

  21. Mølokken-Østvold, K., Jørgensen, M.: A comparison of software project overruns-flexible versus sequential development models. IEEE Trans. Softw. Eng. 31(9), 754–766 (2005)

    Article  Google Scholar 

  22. Premraj, R., Shepperd, M., Kitchenham, B., Forselius, P.: An empirical analysis of software productivity over time. In: Proceedings of International Software Metrics Symposium (METRICS), p. 37 (2005)

    Google Scholar 

  23. Srinivasan, K., Fisher, D.: Machine learning approaches to estimating software development effort. IEEE Trans. Softw. Eng. 21(2), 126–137 (1995)

    Article  Google Scholar 

  24. Strike, K., Eman, K., Madhavji, N.: Software cost estimation with incomplete data. IEEE Trans. Softw. Eng. 27(10), 890–908 (2001)

    Article  Google Scholar 

  25. Tosun, A., Turhan, B., Bener, A.: Feature weighting heuristics for analogy-based effort estimation models. Expert. Syst. Appl. 36(7), 10325–10333 (2009)

    Article  Google Scholar 

  26. Tsunoda, M., Monden, A., Keung, J., Matsumoto, K.: Incorporating expert judgment into regression models of software effort estimation. In: Proceedings of Asia-Pacific Software Engineering Conference (APSEC), pp. 374–379 (2012)

    Google Scholar 

  27. Tsunoda, M., Monden, A., Matsumoto, K., Takahashi, A.: Software development effort estimation models stratified by productivity factors. SEC J (in Japanese) 58–67 (2009)

    Google Scholar 

  28. Tsunoda, M., Monden, A., Yadohisa, H., Kikuchi, N., Matsumoto, K.: Software development productivity of Japanese enterprise applications. Inf. Technol. Manage. 10(4), 193–205 (2009)

    Article  Google Scholar 

  29. Walkerden, F., Jeffery, R.: An empirical study of analogy-based software effort estimation. Empir. Softw. Eng. 4(2), 135–158 (1999)

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially supported by the Japan Ministry of Education, Science, Sports, and Culture [Grant-in-Aid for Scientific Research (C) (No. 16K00113)].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masateru Tsunoda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Kakimoto, T., Tsunoda, M., Monden, A. (2018). Should Duration and Team Size Be Used for Effort Estimation?. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2017. Studies in Computational Intelligence, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-319-62048-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62048-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62047-3

  • Online ISBN: 978-3-319-62048-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics