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Enhancing input value selection in parametric software cost estimation models through second level cost drivers

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Abstract

Parametric cost estimation models are widely used effort prediction tools for software development projects. These models are based on mathematical models that use as inputs specific values for relevant cost drivers. The selection of these inputs is, in many cases, driven by public prescriptive rules that determine the selection of the values. Nonetheless, such selection may in some cases be restrictive and somewhat contradictory with empirical evidence, in other cases the selection procedure is somewhat subject to ambiguity. This paper presents an approach to improve the quality of the selection of adequate cost driver values in parametric models through a process of adjustment to bodies of empirical evidence. The approach has two essential elements. Firstly, it proceeds by analyzing the diverse factors potentially affecting the values a cost driver input might adopt for a given project. And secondly, an aggregation mechanism device for the selection of input variables based on existing data is explicitly devised. This paper describes the rationale for the overall approach and provides evidence of its appropriateness through a concrete empirical study that analyses the COCOMO II DOCU cost driver.

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References

  • Baik, J., Boehm, B., Steece, B. 2000. The Effect of CASE Tools on Software Development Effort. Proceedings of the 15th International Forum on COCOMO and Software Cost Estimation Cost drivers Studies. Los Angeles, California (USA). 24–27/10.

  • Baik, J., Boehm, B., Steece, B. 2002. Disaggregating and calibrating the CASE tool variable in COCOMO II. IEEE Trans. Software Eng. 28(11) pp. 1009–1022.

    Google Scholar 

  • Baylei, J., Basili. V. 1981. A Meta-Model for Software Development Resource Expenditures. In Proceedings of the Fifth International Conference on Software Engineering, pp. 107–116.

  • Boehm, B.W. 1981. Software Engineering Economics. Prentice Hall.

  • Boehm, B., Abts, C., Chulani, S. 2000. Software Development Cost Estimation Approaches-A Survey. Center for Software Engineering, University of California, Technical Report USC-CSE-2000–505.

  • Boehm, B., Clark, B., Horowitz, E., Madachy, R., Selby, R., Westland, C. 1995. Cost Model for Future Software Life Cycle Processes: COCOMO 2.0. Annals of Software Engineering. Special Volume on Software Process and Product Measurement. J. Arthur, S. Henry and J. Baltzer (eds.). Amsterdam: AG Science Publishers, (1): 45–60.

  • Boehm, B., Abts, C., Winsor B., A., Chulani, S., Clark, B., Horowitz, E., Madachy, R., Reifer, D., Steece, B. 2000. Software Cost Estimation with Cocomo II. Prentice Hall.

  • Chulani, S., Clark, B., Boehm, B., Steece, B. 1998. Calibration Approach and Results of the COCOMO II Post–Architecture Model. In Proceedings of the 20th Annual Conference of the International Society of Parametric Analysts (ISPA) and the 8th Annual Conference of the Society of Cost Estimating and Analysis (SCEA).

  • Chulani, S., Boehm, B., Steece, B. 1999. From Multiple Regression to Bayesian Analysis for COCOMO II. In: Proceedings of the 21st Annual Conference of the International Society of Parametric Analysts (ISPA) and the 9th Annual Conference of the Society of Cost Estimating and Analysis (SCEA).

  • Chulani, S., Boehm, B., Steece, B. 1999. Bayesian analysis of empirical software engineering cost models. IEEE Transactions on Software Engineering, 25(4):513–583.

    Article  Google Scholar 

  • Cuadrado-Gallego, J.J., Ernica E., Sánchez, M., Guzmán, J., Amescua, A. 2000. The cost estimator DOCU: An empirical and theoretical study. Proceedings of the 15th International Forum on COCOMO and Software Cost Estimation Cost Drivers Studies. Los Angeles, California (USA), pp. 24–27/10.

  • Cuadrado-Gallego, J.J., Marbán, O., Sánchez, M., Garcín, L. (2005) The importance of rating level selection for input variables to determine accurate estimations in parametric mathematical models. Journal of Cost Analysis and Management, Summer 2004, pp. 12–24.

  • Crespo, J., Sicilia, M.A., Cuadrado, J.J. 2004. On Aggregating Second-Level Software Estimation Cost Drivers: A Usability Cost Estimation Case Study. Proceedings of IPMU, pp. 1255–1260.

  • Department of Defense (DOD), United States 1999. Parametric estimating handbook, 2nd Edition.

  • ESA, 1991. European Space Agency. Software Engineering Standards, ESA PSS-05-0 Issue 2.

  • Farr, L., Zagorski, H. 1965. Quantitative Analysis of Programming Cost Factors: A Progress Report. In A. Frielink (ed.): Proceedings of the ICC Symposium on Economics of Automatic Data Processing. Amsterdam: North-Holland, Holland.

  • Ferens, D., Christensen, D. 1999. Calibrating software cost models to department of defense databases — a review of ten studies. Journal of Parametrics, XIV(1):33–52.

    Google Scholar 

  • Fischman, L. 1997. Calibrating a Software Evaluation Model. In: Proceedings of the ARMS Conference.

  • Herd, J., Postak, J., Russell, W., Stewart, K. 1977. Software Cost Estimation Study – Study Results. Final Technical Report, RADC-TR-77-220. Doty Associates, Inc.

  • Jensen, R. 1983. An Improved Macrolevel Software Development Resource Estimation Model. In: Proceedings of the 5th ISPA Conference, 88–92.

  • Linstone, H., Turoff, M., (eds.). 2002. The Delphi Method: Techniques and Applications. Web edition, available at http://www.is.njit.edu/pubs/delphibook/.

  • McCall, J., et al. 1977. Factors in software quality. Vol. 1,2,3. AD/A-049-014/015/055. Nat. Tech. Inf. Service. Springfield.

  • Mertes, K., Ferens, D., Christensen. 1999. An empirical validation of the checkpoint software cost estimation model. Journal of Cost Analysis and Management, 35–44.

  • NASA, 1990. Manager’s Handbook for Software Development. Revision 1. Software Engineering Laboratory Series. SEL-84-101.

  • NASA, 1995. Software Measurement Guide Book. Revision 1. Software Engineering Laboratory Series. NASA-GB-001–94.

  • NASA, 1996. Software Process Improvement Book. Revision 1. Software Engineering Laboratory Series. NASA-GB-001-95.

  • Nielsen, J. 1993. Usability Engineering. Morgan Kaufmann. Morgan Kaufmann Publishers, San Francisco, USA.

  • Parametric Estimating Initiative (PEI) 1999. Parametric Estimating Handbook. 2nd edition.

  • Prather, P. 1995. Design and analysis of hierarchical software metrics. ACM Computing Surveys, 27(1): 497–518.

  • Putnam, L. 1978. A general empirical solution to the macro software sizing and estimation problem. IEEE Transactions on Software Engineering, 4:345–361.

    Google Scholar 

  • Putnam, L., Mayers, W. 1992. Measures for Excellence. Reliable Software on Time, Within Budget. Englewood Cliffs, NJ: Yourdon Press.

  • Rubin, H. 1983. Macroestimation of Software Development Parameters: the Estimacs System. In: Proceedings of the SOFTFAIR Conference Development Tools, Techniques and Alternatives. Arlington: IEEE Press.

  • Shrum, T. 1997. Calibration and Validation of the CHECKPOINT Model to the Air Force Electronic Systems Center Software Database. Unpublished masters thesis. Dayton, OH, Air Force Institute of Technology.

  • Sicilia, M., Cuadrado-Gallego, J.J., Crespo, J., García-Barriocanal E. 2005. Software cost estimation with fuzzy inputs: Fuzzy modelling and aggregation of cost drivers. Kybernetika, 41(2):249–264.

    Google Scholar 

  • Van Welie, M., van der Veer, G.C., Eliëns A. 1999. Breaking down usability: In: Proceedings of Interact 99, pp. 613–620.

  • Yager, R.R. 1988. Ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. on Systems, Man and Cybernetics, 18:183–190.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. E.U. Politécnica, Campus Universitario, Carretera Madrid-Barcelona, Km. 33,600, C.P. 28871, Alcalá de Henares (Madrid)

    Juan J. Cuadrado-Gallego & Miguel-Ángel Sicilia

  2. Dep. Sistemas Informaticos, Universidad Europea de Madrid, C/Tajo s/n, 28670, Villaviciosa de Odon, Madrid, Spain

    Luis Fernández-Sanz

Authors
  1. Juan J. Cuadrado-Gallego
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  2. Luis Fernández-Sanz
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  3. Miguel-Ángel Sicilia
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Correspondence to Luis Fernández-Sanz.

Additional information

Juan J. Cuadrado-Gallego obtained his Ph. D. degree in Computer Science from the Carlos III University in 2001. From 2001 to 2004 he worked as a full-time lecturer at the Carlos III University, after which he joined the Universities of Valladolid and Alcalá. Also he has taught lectures on Software Engineering at the foreign University of Rome Tre in Rome, Italy. His research interests are primarily in the areas of Software Engineering and Software Metrics. He is the Software Engineering leader of the Information Engineering Research Unit at the University of Alcalá and collaborates as editorial board member in national and international journals related to Software Engineering and Software Metrics.

Luis Fernández-Sanz obtained degree in Computer Science from the Technical University of Madrid (Spain) in 1988 and a Ph. D. degree in Computer Science from the University of the Basque Country in 1997 (with a extraordinary award mention). From 1989 to 1996 he worked as a full-time lecturer at Technical University of Madrid, after which he joined Universidad Europea de Madrid where, since 2000, is director of the department of Computer Systems. His research interests are primarily related to the areas of Software Engineering and Quality as well as educational methods and IT labour market analysis. Since 2000 is the chairman of the Software Quality Group of ATI (the main Spanish association of IT professionals) in charge of the Spanish national conference on Software Quality and Innovation (JICS). He is acted as editor, guest editor or editorial board member in different national and international journals (REICIS, NOVATICA, UPGRADE, etc.) in the area of Software Engineering.

Miguel-Ángel Sicilia obtained a Ms.C. degree in Computer Science from the Pontifical University of Salamanca, Madrid (Spain) in 1996 and a Ph.D. degree from the Carlos III University in 2003. He worked as a software architect in e-commerce consulting firms, being part of the development team of a Web personalization framework at Intelligent Software Components (iSOCO). From 2002 to 2003 he worked as a full-time lecturer at the Carlos III University, after which he joined the University of Alcalá. His research interests are primarily in the areas of adaptive hypermedia and Web personalization, learning technology, and Software Engineering. He is the leader of the AIS SIG on Reusable Learning Objects and collaborates as editorial board member in several international journals related to learning technology, metadata and the Semantic Web.

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Cuadrado-Gallego, J.J., Fernández-Sanz, L. & Sicilia, MÁ. Enhancing input value selection in parametric software cost estimation models through second level cost drivers. Software Qual J 14, 339–357 (2006). https://doi.org/10.1007/s11219-006-0039-0

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