A GENETIC PROGRAMMING APPROACH TO SOFTWARE COST MODELING AND ESTIMATION

Efi Papatheocharous, Angela Iasonos, Andreas S. Andreou

Abstract

This paper investigates the utilization of Genetic Programming (GP) as a method to facilitate better software cost modeling and estimation. The aim is to produce and examine candidate solutions in the form of representations that utilize operators and operands, which are then used in algorithmic cost estimation. These solutions essentially constitute regression equations of software cost factors, used to effectively estimate the dependent variable, that is, the effort spent for developing software projects. The GP application generates representative rules through which the usefulness of various project characteristics as explanatory variables, and ultimately as predictors of development effort is investigated. The experiments conducted are based on two publicly available empirical datasets typically used in software cost estimation and indicate that the proposed approach provides consistent and successful results.

References

  1. Albrecht, A. J., 1979. Measuring Application Development Productivity. Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, pp. 92.
  2. Boehm, B. W., 1981. Software Engineering Economics, Prentice Hall.
  3. Boehm, B. W., Abts, C., Brown, A., Chulani, S., Clark B., Horowitz, E., Madachy, R., Reifer, D., Steece, B., 2000. Software Cost Estimation with COCOMO II, Pearson Publishing.
  4. Burgess, C. J., Leftley, M., 2001. Can Genetic Programming Improve Software Effort Estimation? A Comparative Evaluation. Inform. and Soft. Tech., 43 (14), pp. 863-873.
  5. Desharnais, J. M., 1989. Analyse Statistique de la Productivite des Projects de Development en Informatique a Partir de la Technique de Points de Fonction. MSc. Thesis, Université du Québec, Montréal.
  6. Heiat, A., 2002. Comparison of Artificial Neural Network and regression models for estimating software development effort. Information and Software Technology, 44, pp. 911-922.
  7. Holland, J. H., 1992. Genetic Algorithms, Scientific American, Vol. 267, No. 1, pp. 66-72, New York.
  8. Huang, S.-J., Chiu N.-H., 2008. Optimization of analogy weights by genetic algorithm for software effort estimation. Information and Software Technology, 48, pp. 1034-1045.
  9. Jørgensen, M., Shepperd, M., 2007. A Systematic Review of Software Development Cost Estimation Studies. IEEE Transactions on Software Engineering, 33, No. 1, IEEE Computer Press, pp. 33-53.
  10. Koza, J. R., 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Massachusetts.
  11. Lefley, M., Shepperd, M.J., 2003. Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets, Proceedings of GECCO, pp. 2477-2487.
  12. Michalewicz, Z., 1994. Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin.
  13. Papatheocharous, E., Andreou, A., 2007. Software Cost Estimation Using Artificial Neural Networks with Inputs Selection. Proceedings of the 9th ICEIS, pp. 398-407.
  14. Putnam, L. H., 1978. A General Empirical Solution to the Macro Software Sizing and Estimating Problem, IEEE Transactions on Software Engineering, 4 (4), pp. 345- 361.
  15. Razmi, J., Ghodsi, R., M. Jokar, 2009. Cost estimation of software development: improving the COCOMO model using a genetic algorithm approach. Inter. Journal of Management Practice, 3, pp. 346-368.
  16. Silva, S., 2007. A genetic programming toolbox for Matlab, Version 3, ECOS - Evolutionary and Complex Systems Group University of Coimbra Portugal.
  17. Silva, S., Almeida, J., 2003. Dynamic maximum tree depth - a simple technique for avoiding bloat in treebased GP, Proceedings of GECCO, pp. 1776-1787.
  18. Silva, S., Costa, E., 2005. Resource-Limited Genetic Programming: The Dynamic Approach. Proceedings of GECCO. ACM Press, pp. 1673-1680.
  19. Xu, Z., Khoshgoftaar, T. M., 2004. Identification of Fuzzy Models of Software Cost Estimation. Fuzzy Sets and Systems, 145, No. 1, Elsevier, pp.141-163.
Download


Paper Citation


in Harvard Style

Papatheocharous E., Iasonos A. and S. Andreou A. (2010). A GENETIC PROGRAMMING APPROACH TO SOFTWARE COST MODELING AND ESTIMATION . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-04-1, pages 281-287. DOI: 10.5220/0002911602810287


in Bibtex Style

@conference{iceis10,
author={Efi Papatheocharous and Angela Iasonos and Andreas S. Andreou},
title={A GENETIC PROGRAMMING APPROACH TO SOFTWARE COST MODELING AND ESTIMATION},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2010},
pages={281-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002911602810287},
isbn={978-989-8425-04-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A GENETIC PROGRAMMING APPROACH TO SOFTWARE COST MODELING AND ESTIMATION
SN - 978-989-8425-04-1
AU - Papatheocharous E.
AU - Iasonos A.
AU - S. Andreou A.
PY - 2010
SP - 281
EP - 287
DO - 10.5220/0002911602810287