Abstract
During software development, projects often experience risky situations. If projects fail to detect such risks, they may exhibit confused behavior. In this paper, we propose a new scheme for characterization of the level of confusion exhibited by projects based on an empirical questionnaire. First, we designed a questionnaire from five project viewpoints, requirements, estimates, planning, team organization, and project management activities. Each of these viewpoints was assessed using questions in which experience and knowledge of software risks are determined. Secondly, we classify projects into “confused” and “not confused,” using the resulting metrics data. We thirdly analyzed the relationship between responses to the questionnaire and the degree of confusion of the projects using logistic regression analysis and constructing a model to characterize confused projects. The experimental result used actual project data shows that 28 projects out of 32 were characterized correctly. As a result, we concluded that the characterization of confused projects was successful. Furthermore, we applied the constructed model to data from other projects in order to detect risky projects. The result of the application of this concept showed that 7 out of 8 projects were classified correctly. Therefore, we concluded that the proposed scheme is also applicable to the detection of risky projects.
Similar content being viewed by others
References
Basili, V. R., Briand, L. C., and Melo, W. L. 1996. A validation of object oriented metrics as quality indicators. IEEE Transactions on Software Engineering 22(10): 751–761.
Boehm, B. W. 1987. Industrial software metrics top 10 list. IEEE Software 4(5): 84–85.
Briand, L. C., Basili, V. R., and Hetmanski, C. 1993. Developing interpretable models with optimized set reduction for identifying high risk software components. IEEE Transactions on Software Engineering 19(11): 1028–1044.
Conrow, E. H., and Shishido, P. S. 1997. Implementing risk management on software intensive projects. IEEE Software 14(3): 83–89.
Fairley, R., and Rook, P. 1997. Risk management for software development. In: Software Engineering. Los Alamitos, CA: IEEE CS Press, pp. 387–400.
Fenton, N. E., and P?eeger, S. L. 1997. Software Metrics : A Rigorous & Practical Approach, PWS Publishing.
Humphrey, W. S. 1995. A Discipline for Software Engineering. Maryland: Addison Wesley.
Humphrey, W. S. 2001. Winning with Software: An Executive Strategy. Maryland: Addison-Wesley.
Jiang, J., and Klein, G. 2000. Software development risks to project effectiveness. Journal of Systems and Software 52: 3–10.
Jones, C. 1993. Assessment and Control of Software Risks. Englewood Cliffs, NJ: Prentice Hall, Inc.
Karolak, D. W. 1996. Software Engineering Risk Management. California: IEEE CS Press.
Kasser, J., and Williams, V. R. 1998. What do you mean you can't tell me if my project is in trouble? DoD Software Tech News 2(2). http://www.dacs.dtic.mil/awareness/newsletter/technews2-2/trouble.html
Mizuno, O., Kikuno, T., Inagaki, K., Takagi, Y., and Sakamoto, K. 1998. Analyzing effects of cost estimation accuracy on quality and productivity. In: Proc. of 20th International Conference on Software Engineering. pp. 410–419.
Mizuno, O., Kikuno, T., Inagaki, K., Takagi, Y., and Sakamoto, K. 2000a. Statistical analysis of deviation of actual cost from estimated cost using actual project data. Information and Software Technology 42: 465–473.
Mizuno, O., Kikuno, T., Takagi, Y., and Sakamoto, K.: 2000b, ‘Characterization of risky projects based on project managers’ evaluation. In: Proc. of 22nd International Conference on Software Engineering. pp. 387–395.
Munson, J., and Khoshgoftaar, T. 1992. The detection of faultprone programs. IEEE Transactions on Software Engineering 18(5): 423–433.
Ropponen, J., and Lyytinen, K. 2000. Components of software development risk: How to address them? A project manager survey. IEEE Transactions on Software Engineering 26(2): 98–112.
Sisti, F. J., and Joseph, S. 1994. Software risk evaluation method version 1.0. Technical Report CMU/SEI94TR19, Software Engineering Institute.
Williams, R. C., Pandelios, G. J., and Behrens, S. G. 1999. Software risk evaluation (SRE) Method Description (Version 2.0). Technical Report CMU/SEI99TR029, Software Engineering Institute.
Yourdon, E. 1997. Death March : The Complete Software Developer's Guide to Surviving ‘Mission Impossible’ Projects. Englewood Cliffs, NJ: Prentice-Hall Computer Books.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Takagi, Y., Mizuno, O. & Kikuno, T. An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis. Empir Software Eng 10, 495–515 (2005). https://doi.org/10.1007/s10664-005-3864-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10664-005-3864-z