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Estimating LOC for information systems from their conceptual data models

Published:28 May 2006Publication History

ABSTRACT

Effort and cost estimation is crucial in software management. Estimation of software size plays a key role in the estimation. Line of Code (LOC) is still a commonly used software size measure. Despite the fact that software sizing is well recognized as an important problem for more than two decades, there is still much problem in existing methods. Conceptual data model is widely used in the requirements analysis for information systems. It is also not difficult to construct conceptual data models in the early stage of developing information systems. Much characteristic of an information system is actually reflected from its conceptual data model. We explore into the use of conceptual data model for estimating LOC. This paper proposes a novel method for estimating LOC for an information system from its conceptual data model through the use of multiple linear regression model. We have validated the method through collecting samples from both the industry and open-source systems.

References

  1. Albrecht, A. J., and Gaffney, J. E. Jr. Software function, source lines of code, and development effort prediction: a software science validation. IEEE Trans. Software Eng., vol. SE-9, no. 6, Nov. 1983, 639--648.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Armour, P. Ten unmyths of project estimation: reconsidering some commonly accepted project management practices. Comm. ACM 45,11( Nov. 2002), 15--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Belsley, D. A., Kuh, E., and Welsch, R. E. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley, New York, 2004.Google ScholarGoogle Scholar
  4. Blaha, M., and Premerlani, W. Object-Oriented Modeling and Design for Database Applications. Prentice Hall, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Boehm, B. W., and Fairley, R. E. Software estimation perspectives. IEEE Software, Nov./Dec. 2000, 22--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Boehm, B. W. et al. Software Cost Estimation with COCOMO II.Prentice Hall, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Burgess, R. S. Structured Program Design Using JSP. ELBS, 1988.Google ScholarGoogle Scholar
  8. Canfora, G., Cerulo, L., and Troiano, L. An experience of fuzzy linear regression applied to effort estimation. In Proc. 16th International Conference on Software Engineering & Knowledge Engineering, 2004, 57--61.Google ScholarGoogle Scholar
  9. Chen, P. P. The entity-relationship model - towards a unified view of data. ACM Trans. Database Syst. 1,1 ( Mar. 1976), 9--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. COSMIC-Full Functions - Release 2.0. September 1999.Google ScholarGoogle Scholar
  11. Costagliola, G., Ferrucci, F., Tortora, G. and Vitiello, G. Class point: an approach for the size estimation of object-oriented systems. IEEE Trans. Software Eng., 31, 1(Jan, 2005), 52--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Freshmeat. http://freshmeat.net.Google ScholarGoogle Scholar
  13. Garmus, D., and Herron, D. Function Point Analysis: Measurement Practices for Successful Software Projects. Addison Wesley, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Ghezzi, C., Jazayeri, M. and Mandrioli, D. Fundamentals of Software Engineering. 2nd Edition, Prentice, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jeffery, D. R., Low, G. C., and Barnes, M. A comparison of function point counting techniques. IEEE Trans. Software Eng., May, 1993, 529--532. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jeffery, D. R., and Walkerden, F. An empirical study of analogy-based software effort estimation. Empirical Software Engineering, Kluwer Academic Publishers, 4, 2 (June 1999), 135--158. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kennedy, P. A Guide to Econometrics. Blackwell Publishing, 5th Edition, 2003.Google ScholarGoogle Scholar
  18. Lai, R., and Huang, S. J. A model for estimating the size of a formal communication protocol application and its implementation. IEEE Trans. Software Eng., Jan, 2003, 46--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Laranjeira, L. A. Software size estimation of object-oriented systems. IEEE Trans. Software Eng., May, 1990, 510--522. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. McClave, J. T., and Sincich, T. Statistics. 9th Ed, Prentice Hall, 2003.Google ScholarGoogle Scholar
  21. Miranda, E. An evaluation of the paired comparisons method for software sizing. In Proc. Int. Conf. On Software Eng., 2000, 597--604. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. Applied Linear Regression Models, IRWIN, 1996.Google ScholarGoogle Scholar
  23. Ruthe, M., Jeffery, R., and Wieczorek, I. Cost estimation for web applications. In Proc. Int. Conf. On Software Eng., 2003, 285--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. SAS/STAT User's Guide. http://www.id.unizh.ch/software/unix/statmath/sas/sasdoc/stat/.Google ScholarGoogle Scholar
  25. Smith, J. The estimation of effort based on use cases, Rational Software White Paper.1999.Google ScholarGoogle Scholar
  26. SourceForge.net. http://sourceforge.net/.Google ScholarGoogle Scholar
  27. Stensrud, E., Foss, T., Kitchenham, B., Myrtveit, I. An empirical validation of the relationship between the magnitude of relative error and project size. In Proc. IEEE Symp. Software Metrics, 2002, 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Tan, H. B. K., and Zhao, Y. ER-based software sizing for data-intensive systems. In Proc. Int. Conf. on Conceptual Modeling, 2004, 180--190.Google ScholarGoogle ScholarCross RefCross Ref
  29. Teorey, T. J., Yang, D., and Fry, J. P. A logical design methodology for relational databases using the extended entity-relationship model. ACM Computing Surveys, June, 1986, 197--222. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      ICSE '06: Proceedings of the 28th international conference on Software engineering
      May 2006
      1110 pages
      ISBN:1595933751
      DOI:10.1145/1134285

      Copyright © 2006 ACM

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      Publication History

      • Published: 28 May 2006

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