A STATISTICAL NEURAL NETWORK FRAMEWORK FOR RISK MANAGEMENT PROCESS - From the Proposal to its Preliminary Validation for Efficiency

Salvatore Alessandro Sarcià, Giovanni Cantone, Victor R. Basili

Abstract

This paper enhances the currently available formal risk management models and related frameworks by providing an independent mechanism for checking out their results. It provides a way to compare the historical data on the risks identified by similar projects to the risk found by each framework Based on direct queries to stakeholders, existing approaches provide a mechanism for estimating the probability of achieving software project objectives before the project starts (Prior probability). However, they do not estimate the probability that objectives have actually been achieved, when risk events have occurred during project development. This involves calculating the posterior probability that a project missed its objectives, or, on the contrary, the probability that the project has succeeded. This paper provides existing frameworks with a way to calculate both prior and posterior probability. The overall risk evaluation, calculated by those two probabilities, could be compared to the evaluations that each framework has found within its own process. Therefore, the comparison is performed between what those frameworks assumed and what the historical data suggested both before and during the project. This is a control mechanism because, if those comparisons do not agree, further investigations could be carried out. A case study is presented that provides an efficient way to deal with those issues by using Artificial Neural Networks (ANN) as a statistical tool (e.g., regression and probability estimator). That is, we show that ANN can automatically derive from historical data both prior and posterior probability estimates. This paper shows the verification by simulation of the proposed approach.

References

  1. Alberts, C.J., 2006. Common Element of Risk. TN-014, pp. 1-26, CMU/SEI.
  2. Basili, V., Caldiera, G., and Cantone, G., 1992. A Reference Architecture for the Component Factory. Transactions on Software Engineering and Methodology, vol. 1(1): 53-80, ACM.
  3. Basili, V. R., Caldiera, G., Rombach, H. D., 1994. Goal Question Metric Paradigm. In Encyclopedia of Software Engineering, Ed. J.J. Marciniak, John Wiley & Sons.
  4. Basili, V. R., Caldiera, G., Rombach, H. D., 1994. The Experience Factory. In Encyclopedia of Software Engineering, Ed. J.J. Marciniak, John Wiley & Sons.
  5. Berntsson-Svensson, R., Aurum, A., 2006. Successful Software Project and Product: An Empirical Investigation. In ISESE06, Intl. Synposium on Empirical Software Engineering, IEEE CS Press.
  6. Bishop C., 1995. Neural Network for Pattern Recognition, Oxford University Press.
  7. Boehm, B.W., 1989. Tutorial on Software Risk Management, IEEE CS Press.
  8. Boehm, B.W., 1991. Software Risk Management: Principles and Practices, IEEE Software, No. 1, pp. 32-41, IEEE CS Press.
  9. Briand, L.C., Basili, V.R., Thomas, W.M., 1992. A Pattern Recognition Approach for Software Engineering Data Analysis. Transactions on Software Engineering, Vol. 8, No. 1, pp. 931-942, IEEE CS Press.
  10. Cantone, G., Donzelli, P., 2000. Production and Maintenance of Goal-oriented Measurement Models. Intl. Journal of Software Engineering & Knowledge Engineering, Vol. 10, No. 5, pp. 605-626. World Scientific Publishing Company.
  11. Charette, R.N., 1989. Software Engineering Risk Analysis and Management, McGraw-Hill.
  12. Chen, Z., Mezies, T., Port, D., Boehm, B. W., 2005. Feature Subset Selection Can Improve Software Cost Estimation Accuracy. PROMISE06, Conference on Predictor Models in Software Engineering. ACM.
  13. CMMI Product Team, 2002. Capability Maturity Model Integration (CMMI), Version 1.1. TR-012, pp. 397- 416, CMU/SEI.
  14. Dreyfus G., Neural Networks Methodology and Applications, Springer, 2005.
  15. Efron B. and Tibshirani R. J.. An Introduction to the Bootstrap, volume 57 of Monographs on Statistics and Applied Probability. Chapman & Hall, 1993.
  16. Fussel, L., Field, S., 2005. The Role of the Risk Management Database in the Risk Management Process. ISCEng05, International Conference on Systems Engineering, IEEE CS Press.
  17. Higuera, R.P. , 1996. Software Risk Management. TR-012, pp. 1-48, CMU/SEI.
  18. John G., Kohavi R., Pfleger K., 1994. Irrelevant features and the subset selection problem. 11th Intl. Conference on Machine Learning, pp. 121-129. Morgan Kaufmann.
  19. Jollife I.T., 1986. Principal Component Analysis, Springer.
  20. Jones, C., 2002. Patterns of large software systems: failure and success. Computer, N.o 23, pp. 86-87, IEEE CS Press.
  21. Madachy, R.J., 1997. Heuristic Risk Assessment Using Cost Factors. Software, pp. 51-59, IEEE CS Press.
  22. Piattini, M., Genero, M., Jiménez, L., 2001. A MetricBased Approach for Predicting Conceptual Data Models Maintainability. Intl. Journal of Software Engineering and Knowledge Engineering 11(6): 703- 729, World Scientific.
  23. Rumelhart, D.E., Hilton, G.E., Williams, R.J., 1986. Learning internal representations by error propagation, Nature, pp. 318-364, Nature Publishing Group.
  24. Karolak, D.W., 1997. Software Engineering Risk Management, IEEE CS Press.
  25. Kontio, J., 1996. The Riskit Method for Software Risk management, Version 1.00. TR-3782, UMDCS.
  26. Lanubile, F., Visaggio, G., 1997. Evaluating Predictive Quality Models Derived from Software Measures: Lessons Learned. JSS p. 38:225-234, Journal of Systems and Software, Elsevier.
  27. Linberg, K.R., 1999. Software developer perceptions about software project failure: a case study. JSS, pp. 177-192, Journal of Systems and Software, Elsevier.
  28. Procaccino, J. D., Verner, J. M., Lorenzet, S. J., 2006. Defining and contributing to software development success. Commununications of the ACM, No. 49, ACM.
  29. Roy, G. G., 2004. A Risk management Framework for Software Engineering Practice. ASWEC04, Australian Software Engineering Conference. IEEE CS Press.
  30. Sarcià A. S., Cantone, G., 2006. Using Artificial Neural Networks to Improve Risk Management Process. TR06, ESEG-DISP, Univ. of Roma Tor Vergata.
  31. Srinivasan, K., Fisher, D., 1996. Machine Learning Approaches to Estimating Development Effort. Transactions on Software Engineering, IEEE CS Press.
  32. Standish Group, 1994. CHAOS Report 1994-99. http://www.standishgroup.com, last access Feb. 2006.
  33. Tversky A., and D. Kahneman, 1974. Judgment under Uncertainty: Heuristic and Biases, pp. 1124-1131, Science, AAAS Press.
  34. Verner J.M., Cerpa N., 2005. Australian Software Development: What Software Project Management Practices Lead to Success? ASWEC05, Australian Software Engineering Conference. IEEE CS Press.
  35. Wallin, C., Larsson, S., Ekdahl, F., Crnkovic, I., 2002. Combining Models for Business Decisions and Software Development. Euromicro02, Euromicro Intl. Conf., pp. 266-271, IEEE CS Press.
  36. Williams, R.C., Pandelios, G.J., Behrens, S.G., 1999. Software Risk Evaluation (SRE) Method Description (Version 2.0). TR-029, pp. 1-99, CMU/SEI.
Download


Paper Citation


in Harvard Style

Alessandro Sarcià S., Cantone G. and R. Basili V. (2007). A STATISTICAL NEURAL NETWORK FRAMEWORK FOR RISK MANAGEMENT PROCESS - From the Proposal to its Preliminary Validation for Efficiency . In Proceedings of the Second International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-989-8111-06-7, pages 168-177. DOI: 10.5220/0001335701680177


in Bibtex Style

@conference{icsoft07,
author={Salvatore Alessandro Sarcià and Giovanni Cantone and Victor R. Basili},
title={A STATISTICAL NEURAL NETWORK FRAMEWORK FOR RISK MANAGEMENT PROCESS - From the Proposal to its Preliminary Validation for Efficiency},
booktitle={Proceedings of the Second International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2007},
pages={168-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001335701680177},
isbn={978-989-8111-06-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - A STATISTICAL NEURAL NETWORK FRAMEWORK FOR RISK MANAGEMENT PROCESS - From the Proposal to its Preliminary Validation for Efficiency
SN - 978-989-8111-06-7
AU - Alessandro Sarcià S.
AU - Cantone G.
AU - R. Basili V.
PY - 2007
SP - 168
EP - 177
DO - 10.5220/0001335701680177