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Evolving Estimators for Software Project Development

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 128))

Abstract

In this research, an application of a computational intelligence approach for effort estimation in software projects is presented. More specifically, the authors examine a genetic programming system for symbolic regression; the main goal is to derive equations for estimating the development effort that are highly accurate. These mathematical formulas are expected to reveal relationships between the available input features and the estimated project work. The application of the proposed methodology is performed in two software engineering domains. The proposed model is shown capable to produce short and handy formulas that are more precise than the existent in literature.

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References

  1. Boehm, B.: Software Engineering Economics. Prentice-Hall, Englewood Cliffs (1981)

    MATH  Google Scholar 

  2. Price, S. (2007), http://www.pricesystems.com

  3. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  4. Rodríguez, D., Cuadrado, J.J., Sicilia, M.A., Ruiz, R.: Segmentation of Software Engineering Datasets Using the M5 Algorithm. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3994, pp. 789–796. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Menzies, T., Di Stefano, J.S.: How Good is your Blind Spot Sampling Policy? In: Proc. of 8th IEEE Int’l Symp. on High Assurance Systems Eng., Tampa, FL, USA (2004)

    Google Scholar 

  6. Shepperd, M., Schofield, C.: Estimating software project effort using analogies. IEEE Trans. Soft. Eng. 23(12) (1997)

    Google Scholar 

  7. Aguilar- Ruiz, J.S., Ramos, I., Riquelme, J.C., Toro, M.: An evolutionary approach to estimating software development projects. Information and Software Technology 43, 875–882 (2001)

    Article  Google Scholar 

  8. Boetticher, G., Lokhandwala, N., Helm, J.C.: Understanding the Human Estimator. In: 2nd Int’l. Predictive Models in Soft. Eng (PROMISE) Workshop, 22nd IEEE Int’l. Conf. on Soft. Maintenance, PA, USA (2006)

    Google Scholar 

  9. Lum, K., Bramble, M., Hihn, J., Hackney, J., Khorrami, M., Monson, E.: Handbook of Software Cost Estimation, Jet Propulsion Laboratory, Pasadena, CA, USA (2003)

    Google Scholar 

  10. Hihn, J., Habib-agathi, H.: Cost estimation of Software Intensive Projects: A survey of Current Practices. In: Proc. of the 13th IEEE Int’l. Conf. Soft. Eng. (1991)

    Google Scholar 

  11. Singleton, A.: Genetic Programming with C++. BYTE Magazine, February Issue (1991)

    Google Scholar 

  12. Menzies, T., Port, D., Chen, Z., Hihn, J., Stukes, S.: Validation Methods for Calibrating Software Effort Models. In: Proc. ICSE 2005, St. Louis, MI, USA (2005)

    Google Scholar 

  13. Chen, Z., Menzies, T., Port, D., Boehm, B.: Feature Subset Selection Can Improve Software Cost Estimation Accuracy. In: Proc. 1st Int’l. Predictive Models in Soft. Eng. (PROMISE) Workshop St. Louis, MI, USA (2005)

    Google Scholar 

  14. Menzies, T., Chen, D.P.Z., Hihn, H.: Simple Software Cost Analysis: Safe or Unsafe? In: Proc. 1st Int’l. Predictive Models in Soft. Eng. (PROMISE) Workshop, St. Louis, MI, USA (2005)

    Google Scholar 

  15. Srinivasan, K., Fisher, D.: Machine Learning Approaches to Estimating Software Development Effort. IEEE Trans. Soft. Eng. 21(2), 126–137 (1995)

    Article  Google Scholar 

  16. Rogers, A., Prügel-Bennett, A.: Modeling the dynamics of steady-state genetic algorithms. In: Banzhaf, W., Reeves, C. (eds.) Foundations of Genetic Algorithms, pp. 57–68. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  17. Blickle, T., Theile, L.: A mathematical analysis of tournament selection. In: Eshelman, L.J. (ed.) Proc. of the 6thInternational Conference on Genetic Algorithms, pp. 9–16. Lawrence Erlbaum Associates, Hillsdale (1995)

    Google Scholar 

  18. Tsakonas, A., Dounias, G.: Evolving Neural-Symbolic Systems Guided by Adaptive Training Schemes: Applications in Finance. Applied Artificial Intelligence 21(7), 681–706 (2007)

    Article  Google Scholar 

  19. Eads, D., Hill, D., Davis, S., Perkins, S., Ma, J., Porter, R., Theiler, J.: Genetic Algorithms and Support Vector Machines for Time Series Classification. In: Proc. SPIE, vol. 4787, pp. 74–85 (2002)

    Google Scholar 

  20. Quinlan, J.R.: Bagging, boosting, and C4.5. In: Proc. 13th Nat. Conf. Art. Intell., pp. 725–730 (1996)

    Google Scholar 

  21. Conte, S.D., Dunsmore, H.E., Shen, V.: Software Engineering Metrics and Models. Benjamin-Cummings (1986)

    Google Scholar 

  22. Dolado, J.J.: On the problem of the software cost function. Information and Software Technology 43, 61–72 (2001)

    Article  Google Scholar 

  23. Dreger, J.: Function Point Analysis. Prentice Hall, Englewood Cliffs (1989)

    Google Scholar 

  24. Putnam, L.H.: A general empirical solution to the macro software sizing and estimating problem. IEEE Trans. Soft. Eng. 4(4), 345–361 (1978)

    Article  MATH  Google Scholar 

  25. Boehm, B., Horowitz, E., Madachy, R., Reifer, D., Clark, B.K., Steece, B., Brown, A.W., Chulani, S., Abts, C.: Software Cost Estimation (2000)

    Google Scholar 

  26. Menzies, T., Chen, Z., Hihn, J., Lum, K.: Selecting Best Practices for Effort Estimation. IEEE Transactions Software Engineering 32(11) (November 2006)

    Google Scholar 

  27. Montana, D.J.: Strongly Typed Genetic Programming. Evolutionary Computation 3(2) (1995)

    Google Scholar 

  28. Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and its Application to Modeling and Control. IEEE Trans. On Systems, Man and Cybernetics 17, 295–301 (1985)

    MATH  Google Scholar 

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Tsakonas, A., Dounias, G. (2011). Evolving Estimators for Software Project Development. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowlege Engineering and Knowledge Management. IC3K 2009. Communications in Computer and Information Science, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19032-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-19032-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19031-5

  • Online ISBN: 978-3-642-19032-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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