ABSTRACT
During the process of software development, senior managers often find indications that projects are risky and take appropriate actions to recover them from this dangerous status. If senior managers fail to detect such risks, it is possible that such projects may collapse completely.
In this paper, we propose a new scheme for the characterization of risky projects based on an evaluation by the project manager. In order to acquire the relevant data to make such an assessment, we first designed a questionnaire from five viewpoints within the projects: requirements, estimations, team organization, planning capability and project management activities. Each of these viewpoints consisted of a number of concrete questions. We then analyzed the responses to the questionnaires as provided by project managers by applying a logistic regression analysis. That is, we determined the coefficients of the logistic model from a set of the questionnaire responses. The experimental results using actual project data in Company A showed that 27 projects out of 32 were predicted correctly. Thus we would expect that the proposed characterizing scheme is the first step toward predicting which projects are risky at an early phase of the development.
- 1.V. R. Basili, L. C. Briand and W. L. Melo, "A validation of object-oriented metrics as quality indicators," IEEE Trans. on Software Eng., vol. 22, no.10, pp.751- 761, 1996. Google ScholarDigital Library
- 2.L. C. Briand, V. R. Basili and C. Hetmanski, "Developing interpretable models with optimized set reduction for identifying high risk software components," IEEE Trans. Software Eng., vol.19, no. 11, pp.1028- 1044, 1993. Google ScholarDigital Library
- 3.E. H. Conrow and P. S. Shishido, "Implementing risk management on software intensive projects," IEEE Software, Vol.14, No.3, pp.83-89, 1997. Google ScholarDigital Library
- 4.R. Fairley and P. Rook, "Risk management for software development," In Software Engineering, IEEE CS Press, pp.387-400, 1997.Google Scholar
- 5.N. E. Fenton and S. L. Pfleeger, Software Metrics A Rigorous & Practical Approach, PWS Publishing, 1997. Google ScholarDigital Library
- 6.W. S. Humphrey, Managing the Software Process, Addison Wesley, Reading, MA, 1989. Google ScholarDigital Library
- 7.W. S. Humphrey, A Discipline for Software Engineering, Addison Wesley, Reading, MA, 1995. Google ScholarDigital Library
- 8.D. W. Karolak, Software Engineering Risk Management, IEEE CS Press, CA, 1996. Google ScholarDigital Library
- 9.O. Mizuno, T. Kikuno, K. Inagaki, Y. Takagi and K. Sakamoto, "Analyzing effects of cost estimation accuracy on quality and productivity," In Proc. 20th International Conference on Software Engineer-ing( ICSE'98), pp.410-419, 1998. Google ScholarDigital Library
- 10.O. Mizuno and T. Kikuno, "Empirical evaluation of review process improvement activities with respect to post-release failure," In Summary of Empirical Studies on Software Development Engineering(ICSE'99 Workshop), pp.50-53, 1999.Google Scholar
- 11.J. Munson and T. Khoshgoftaar, "The detection of fault-prone programs," IEEE Trans. Software Eng., vol.18, no.5, 1992. Google ScholarDigital Library
- 12.F. J. Sisti and S. Joseph, "Software risk evaluation method version 1.0," Technical Report CMU/SEI-94- TR-19, Software Engineering Institute, 1994.Google ScholarCross Ref
- 13.Y. Takagi, T. Tanaka, N. Niihara, K. Sakamoto, S. Kusumoto and T. Kikuno, "Analysis of review's effectiveness based on software metrics," In Proc. 6th International Symposium on Software Reliability Engineering(ISSRE'95), pp.34-39, 1995.Google ScholarCross Ref
- 14.T. Tanaka, K. Sakamoto, S. Kusumoto and T. Kikuno, "Improvement of software process by process visualization and benefit estimation," In Proc. 17th International Conference on Software Engineer-ing( ICSE'95), pp.123-132, 1995. Google ScholarDigital Library
- 15.E. Yourdon, Death March : The Complete Software Developer's Guide to Surviving 'Mission Impossible' Projects, Prentice Hall Computer Books, 1997. Google ScholarDigital Library
Index Terms
- Characterization of risky projects based on project managers' evaluation
Recommendations
An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis
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 ...
Project managers: can we make them or just make them better?
SIGITE '05: Proceedings of the 6th conference on Information technology educationAs the documented importance of project management grows for all organizations world wide, skilled successful project managers have become a valuable asset to have in an organization. In Information Technology these project managers need a good ...
Discriminating risky software project using neural networks
Early and accurate discrimination of risky software projects is critical to project success. Researchers have proposed many predictive approaches based on traditional modeling techniques, but the high misclassification rate of risky projects is common. ...
Comments