SOFTWARE COST ESTIMATION USING ARTIFICIAL NEURAL NETWORKS WITH INPUTS SELECTION

Efi Papatheocharous, Andreas S. Andreou

Abstract

Software development is an intractable, multifaceted process encountering deep, inherent difficulties. Especially when trying to produce accurate and reliable software cost estimates, these difficulties are amplified due to the high level of complexity and uniqueness of the software process. This paper addresses the issue of estimating the cost of software development by identifying the need for countable entities that affect software cost and using them with artificial neural networks to establish a reliable estimation method. Input Sensitivity Analysis (ISA) is performed on predictive models of the Desharnais and ISBSG datasets aiming at identifying any correlation present between important cost parameters at the input level and development effort (output). The degree to which the input parameters define the evolution of effort is then investigated and the selected attributes are employed to establish accurate prediction of software cost in the early phases of the software development life-cycle.

References

  1. Aggarwal, K., Singh, Y., Chandra, P. and Puri, M., 2005. Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points. Journal of Computer Sciences 1 (4), pp. 504-508.
  2. Boehm, B.W., 1981. Software Engineering Economics. Prentice Hall.
  3. Boehm, B.W., Abts, C., and Chulani, S., 2000. Software development cost estimation approaches - A survey. In Annals of Software Engineering 10, p. 177-205.
  4. Boehm, B.W., Abts, C., Clark, B., and Devnani-Chulani. S., 1997. COCOMO II Model Definition Manual. The University of Southern California.
  5. Boehm, B.W., Clark B., Horowitz E. and Westland C., 1995. Cost Models for Future Software Life Cycle Processes: COCOMO 2.0. Annals of Software Engineering, Vol. 1, pp. 57-94.
  6. Briand, L. C., Emam K. E., Surmann D., Wieczorek I., Maxwell K., 1999. An Assessment and Comparison of Common Software Cost Estimation Modeling Techniques. Proceedings International Conference Software Engineering, pp. 313-322.
  7. Coombs, P., 2003. IT Project Estimation: A Practical Guide to the Costing of Software, Cambridge University Press.
  8. Desharnais, J. M., 1988. Analyse Statistique de la Productivite des Projects de Development en Informatique a Partir de la Technique de Points de Fonction. Université du Québec: MSc. Thesis, Montréal.
  9. Fenton, N.E. and Pfleeger, S.L., 1997. Software Metrics: A Rigorous and Practical Approach. International Thomson Computer Press.
  10. Finnie, G. R., Wittig G. E. and Desharnais J. M., 1997. A comparison of software effort estimation techniques using function points with neural networks, case based reasoning and regression models. J. of Systems Software, Vol. 39, pp. 281-89.
  11. Haykin, S., 1999. Neural Networks: A Comprehensive Foundation, Prentice Hall.
  12. Idri, A., Khoshgoftaar T. M. and Abran A., 2002. Can Neural Networks be easily interpreted in Software Cost Estimation? In Proc. of the 2002 IEEE Intern.
  13. International Software Benchmarking Standards Group, Repository Data Release 9,t
  14. Jeffery, R., Ruhe M. and Wieczorek I., 2000. A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Information and Software Technology, Vol. 42, No. 14, pp. 1009-1016.
  15. Laird, L. M. and Brennan, M. C., 2006. Software Measurement and Estimation: A Practical Approach. John Wiley & Sons, Inc.
  16. Lederer, A. L. and Prasad J., 1992. Nine management guidelines for better cost estimating. Comm. of the ACM, Vol. 35, No. 2, pp. 51-59.
  17. Leung, H. and Fan Z., 2002. Software Cost Estimation. In Handbook of Software Engineering and Knowledge Engineering, Vol. 2, World Scientific.
  18. MacDonell S. G. and Gray A. R., 1997. Applications of Fuzzy Logic to Software Metric Models for Development Effort Estimation. Proc. of 1997: Annual Meeting of the North American Fuzzy Information Processing Society - NAFIPS, Syracuse NY, USA, IEEE, pp. 394-399.
  19. Putnam, L. H. and Myers W., 1992. Measures for Excellence, Reliable Software on Time, Within Budget. Yourdan Press, Englewood Cliffs N.J.
  20. Saltelli, A., 2004. Global Sensitivity Analysis: An Introduction. In Proc. 4th Intern. Conf. on Sensitivity Analysis of Model Output (SAMO 7804), pp. 27-43.
  21. Santillo, L., Lombardi, S. and Natale D., 2005. Advances in statistical analysis from the ISBSG benchmarking database. Proceedings of SMEF, pp.39-48.
  22. The Standish Group, CHAOS Chronicles, Standish Group Internal Report, 1995, Available at <http://www.standishgroup.com/>.
Download


Paper Citation


in Harvard Style

Papatheocharous E. and S. Andreou A. (2007). SOFTWARE COST ESTIMATION USING ARTIFICIAL NEURAL NETWORKS WITH INPUTS SELECTION . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-972-8865-88-7, pages 398-407. DOI: 10.5220/0002380803980407


in Bibtex Style

@conference{iceis07,
author={Efi Papatheocharous and Andreas S. Andreou},
title={SOFTWARE COST ESTIMATION USING ARTIFICIAL NEURAL NETWORKS WITH INPUTS SELECTION},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2007},
pages={398-407},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002380803980407},
isbn={978-972-8865-88-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - SOFTWARE COST ESTIMATION USING ARTIFICIAL NEURAL NETWORKS WITH INPUTS SELECTION
SN - 978-972-8865-88-7
AU - Papatheocharous E.
AU - S. Andreou A.
PY - 2007
SP - 398
EP - 407
DO - 10.5220/0002380803980407