Using Expert-based Bayesian Networks as Decision Support Systems to Improve Project Management of Healthcare Software Projects

Emilia Mendes

Abstract

One of the pillars for sound Software Project Management is reliable effort estimation. Therefore it is important to fully identify what are the fundamental factors that affect an effort estimate for a new project and how these factors are inter-related. This paper describes a case study where a Bayesian Network model to estimate effort for healthcare software projects was built. This model was solely elicited from expert knowledge, with the participation of seven project managers, and was validated using data from 22 past finished projects. The model led to numerous changes in process and also in business. The company adapted their existing effort estimation process to be in line with the model that was created, and the use of a mathematically-based model also led to an increase in the number of projects being delegated to this company by other company branches worldwide.

References

  1. Azhar, D., Mendes, E., and Riddle, P. 2012. A Systematic Review of Web Resource Estimation, Proceedings of Promise'12.
  2. Druzdzel, M. J., & van der Gaag, L. C. (2000). Building Probabilistic Networks: Where Do the Numbers Come From?. IEEE Trans. on Knowledge and Data Engineering, 12(4), 481-486.
  3. Jensen, F. V. (1996). An Introduction to Bayesian Networks. UCL press, London.
  4. Fenton, N., Marsh, W., Neil, M., Cates, P., Forey, S., and Tailor, M. 2004: Making Resource Decisions for Software Projects, Proc. ICSE'04, pp. 397-406.
  5. Ferrucci, F. Gravino, C., Di Martino, S. 2008, A Case Study Using Web Objects and COSMIC for Effort Estimation of Web Applications. EUROMICROSEAA, p. 441-448.
  6. Korb, K. B., and Nicholson, A. E. 2004, Bayesian Artificial Intelligence, CRC Press, USA.
  7. Jørgensen, M., and Grimstad, S. 2009. Software Development Effort Estimation: Demystifying and Improving Expert Estimation, In: Simula Research Laboratory - by thinking constantly about it, ed. by Aslak Tveito, Are Magnus Bruaset, Olav Lysne. Springer, Heidelberg, chap. 26, pp. 381-404. (ISBN: 978-3642011559).
  8. Jorgensen, M. and Shepperd, M. 2007. A systematic review of software development cost estimation studies, IEEE Trans. Softw. Eng., vol. 33, no. 1, pp. 33-53.
  9. Mendes, E., and Mosley, N., 2008, Bayesian Network Models for Web Effort Prediction: a Comparative Study, Transactions on Software Engineering, Vol. 34, Issue: 6, Nov/Dec 2008, pp. 723-737.
  10. Mendes, E., Mosley, N., and Counsell, S. 2001. Web metrics - Metrics for estimating effort to design and author Web applications. IEEE MultiMedia, JanuaryMarch, 50-57.
  11. Mendes, E., Mosley, N. and Counsell, S. 2005. The Need for Web Engineering: an Introduction, Web Engineering, Springer-Verlag, Eds: E. Mendes, N. Mosley, pp. 1-26, ISBN 3-540-281 96-7.
  12. Mendes, E., Polino, C., and Mosley, N. 2009, Building an Expert-based Web Effort Estimation Model using Bayesian Networks, 13th International Conference on Evaluation & Assessment in Software Engineering.
  13. Nauman, A. B., and Lali, M. I., 2012, Productivity Inference with Dynamic Bayesian Models in Software Development Projects, International Journal of Computer and Electronics, 1(2), 50-57.
  14. Nonaka, I., Toyama, R. 2003. The knowledge-creating theory revisited: knowledge creation as a synthesizing process. Knowledge Management Research & Practice, 1:2-10.
  15. Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Mateo, CA.
  16. Pollino, C., White, A., and Hart, B.T., 2007, Development and application of a Bayesian decision support tool to assist in the management of an endangered species. Ecological Modelling 201, 37-59.
  17. Studer, R., Benjamins, V.R., & Fensel, D. 1998. Knowledge engineering: principles and methods. Data & Knowledge Engineering, 25, 161-197.
  18. Tang, Z., & McCabe, B. 2007. Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks, Journal of Computing in Civil Engineering, 21(4), 265-276.
  19. Reifer, D. J. 2000, Web Development: Estimating Quickto-Market Software, IEEE Software, Nov.-Dec., 57- 64.
  20. Ruhe, M., Jeffery, R., and Wieczorek., I. 2003, Cost estimation for Web applications, Proceedings ICSE 2003, 285-294, 2003.
  21. Woodberry, O., Nicholson, A., Korb, K., & Pollino, C. 2004. Parameterising Bayesian Networks. Proceedings of the Australian Conference on Artificial Intelligence (pp. 1101-1107).
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Paper Citation


in Harvard Style

Mendes E. (2013). Using Expert-based Bayesian Networks as Decision Support Systems to Improve Project Management of Healthcare Software Projects . In Proceedings of the 8th International Joint Conference on Software Technologies - Volume 1: ICSOFT-EA, (ICSOFT 2013) ISBN 978-989-8565-68-6, pages 389-399. DOI: 10.5220/0004434103890399


in Bibtex Style

@conference{icsoft-ea13,
author={Emilia Mendes},
title={Using Expert-based Bayesian Networks as Decision Support Systems to Improve Project Management of Healthcare Software Projects },
booktitle={Proceedings of the 8th International Joint Conference on Software Technologies - Volume 1: ICSOFT-EA, (ICSOFT 2013)},
year={2013},
pages={389-399},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004434103890399},
isbn={978-989-8565-68-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Software Technologies - Volume 1: ICSOFT-EA, (ICSOFT 2013)
TI - Using Expert-based Bayesian Networks as Decision Support Systems to Improve Project Management of Healthcare Software Projects
SN - 978-989-8565-68-6
AU - Mendes E.
PY - 2013
SP - 389
EP - 399
DO - 10.5220/0004434103890399