Skip to main content
SpringerLink
Log in

Assessing Project Success Using Subjective Evaluation Factors

  • Published:
Software Quality Journal Aims and scope Submit manuscript

Abstract

Project evaluation is essential to understand and assess the key aspects of a project that make it either a success or failure. The latter is influenced by a large number of factors, and many times it is hard to measure them objectively. This paper addresses this by introducing a new method for identifying and assessing key project characteristics, which are crucial for a project's success. The method consists of a number of well-defined steps, which are described in detail. The method is applied to two case studies from different application domains and continents. It is concluded that patterns are possible to detect from the data sets. Further, the analysis of the two data sets shows that the proposed method using subjective factors is useful, since it provides an increased understanding, insight and assessment of which project factors might affect project success.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allen, M. and Yeh, W. 1979. Introduction to Measurement Theory, Brooks/Cole Publishing.

  • Altman, D. 1991, Practical Statistics for Medical Research, Chapman-Hall.

  • Basili, V., Zelkovitz, M., McGarry, F., Page, J., Waligora, S., and Pajerski, R. 1995. SEL's software process-improvement program, IEEE Software November, pp. 83-87.

  • Boehm, B. 1981. Software Engineering Economics, Englewood Cliffs, NJ, Prentice-Hall.

    Google Scholar 

  • Briand, L., El Emam, K., and Morasca, S. 1996. On the application of Measurement Theory in SoftwareEngineering, J Emp. Software Eng. 1(1): 61-88.

    Google Scholar 

  • El Emam, K. 1999. Benchmarking kappa for software process assessment reliability studies, Emp. Software Eng. Int. J. 4(4): 113-133.

    Google Scholar 

  • Fenton, N. and Pfleeger, S.L. 1996. Software Metrics—A Rigorous and Practical Approach, International Thompson Computer Press.

  • Gray, A.R., MacDonell, S.G., and Shepperd, M.J. 1999. Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgement. Proc. Sixth Int. Software Metrics Symp., Boca Raton, FL, pp. 216-227.

  • Höst, M. and Wohlin, C. 1998. An experimental study of individual subjective effort estimations and combinations of the estimates. Proc. IEEE Int. Conf. Software Eng., Kyoto, Japan, pp. 332-339.

  • Hughes, R. 1996. Expert judgement as an estimation method, Inf. Software Technol. 38: 67-75.

    Google Scholar 

  • Kachigan, S.K. 1986. Statistical Analysis—An Interdisciplinary Introduction to Univariate and Multivariate Methods, Radius Press.

  • Khoshgoftaar, T.M., Allen, E.B., Kalaichelvan, K.S., Goel, N., Hudepohl, J.P., and Mayrand, J. 1995. Detection of fault-prone program modules in a very large telecommunications system. Proc. Int. Symp. Software Reliability Eng. Toulouse, France, pp. 24-33.

  • Khoshgoftaar, T.M. and Lanning, D.L. 1994. Are the principal components of software complexity stable across software products? Proc. Int. Symp. Software Metrics, London, pp. 61-72.

  • von Mayrhauser, A. 1990. Software Engineering: Methods and Management, Academic Press.

  • NASA-SEL. 1992. Software Engineering Laboratory Database Organization and Users Guide, Rev. 2, Goddard Space Flight Center, Greenbelt, MD, NASA-SEL Series, SEL-89-201.

    Google Scholar 

  • Ohlsson, M.C., von Mayrhauser, A., McGuire, B., and Wohlin, C. 1999. Code decay analysis of legacy software through successive releases. Proc. IEEE Aerospace Conf., Snowmass, CO.

  • Ropponen, J. and Lyytinen, K. 2000. Components of software development risk: how to address them? A project manager survey, IEEE Trans. Software Eng. 26(2): 98-112.

    Google Scholar 

  • Valett, J.D. 1993. The (mis)use of subjective process measures in software engineering. Proc. Software Eng. Workshop, NASA/Goddard Space Flight Center, Greenbelt, MD, pp. 161-165.

    Google Scholar 

  • Wohlin, C., and Ahlgren, M. 1995. Soft factors and their impact on time to market, Software Qual. J. 4: 189-205.

    Google Scholar 

  • Wohlin, C., von Mayrhauser, A., Host, M., and Regnell, B. 2000. Subjective evaluation as a tool for learning from software project success, Inf. Software Technol. 42(14): 983-992.

    Google Scholar 

  • Zhao, M., Wohlin, C., Ohlsson, N., and Xie, M. 1998. A Comparison between Software Design and Code Metrics for the Prediction of Software Fault Content, Inf. Software Technol. 40(14): 801-809.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering & Computer Science, Blekinge Institute of Technology, Box 520, SE-372 25, Ronneby, Sweden

    Claes Wohlin

  2. Computer Science Department, Colorado State University, Fort Collins, CO, 80523-1873, USA

    Anneliese Amschler Andrews

Authors
  1. Claes Wohlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anneliese Amschler Andrews
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wohlin, C., Andrews, A.A. Assessing Project Success Using Subjective Evaluation Factors. Software Quality Journal 9, 43–70 (2001). https://doi.org/10.1023/A:1016673203332

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1016673203332

Search

Navigation