Software Cost Estimation Using Similarity Difference Between Software Attributes

  • Divya Kashyap
  • A. K. Misra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


The apt estimate of the software cost in advance is one of the most challenging, difficult and mandatory task for every project manager. Software development is a critical activity which requires various considerable resources and time. A prior assessment of software cost directly depends on the expanse of these resources and time, which in turn depends in the software attributes and its characteristics. Since there are many precarious and dynamic attributes attached to every software project, the accuracy in prediction of the cost will rely on the prudential treatment of these attributes. This paper deals with the methods of selection, quantification and comparison of different attributes related to different projects. We have tried to find the similarity difference between project attributes and then consequently used this difference measurement for creating the initial cost proposals of any software project that has some degree of correspondence with the formerly completed projects whose total cost is fairly established and well known.


Software development cost Software attributes Cost estimation k-nearest neighbor classifier Analogy and similarity difference 


  1. 1.
    Gray, R., MacDonell, S.G., Shepperd, M.J.: Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgment, IEEE 6\(^{th}\) Metrics Symposium, IEEE Computer Society, pp. 216–227, Los Alamitos (1999)Google Scholar
  2. 2.
    Nolan, A., Abrahao, S.: Dealing with cost estimation in software product lines: experiences and future directions. Software Product Lines: Going Beyond, pp. 121–135. LNCS, Springer, Berlin (2010)Google Scholar
  3. 3.
    Leung, H., Fan, Z.: Software Cost Estimation. Hong Kong Polytechnic University, Handbook of Software Engineering (2002)Google Scholar
  4. 4.
    Ma, J., and Mu, L.: Comparison Study on methods of software cost estimation, supported by Hebei Provincial Construction of Science and Technology Research Program, 2010 IEEEGoogle Scholar
  5. 5.
    Boehm, B.W.: Software engineering economics. IEEE Trans. Softw. Eng. 10(1), 4–21 (1981)Google Scholar
  6. 6.
    Boehm, B. W., Valerdi, R.: Achievements and challenges in cocomo-based software resource estimation, IEEE Softw 25(5), 74–83. doi:10.1109/MS, 2008Google Scholar
  7. 7.
    Zheng, Y., Wang, B.: Estimation of software projects effort based on function point, Proceedings of 4th International Conference on Computer, Science and Education 2009Google Scholar
  8. 8.
    Fu, Y. -F., Liu, X.-D., Yang, R. -N., Du, Y. -L., Li, Y. -J.: A software size estimation method based on improved FPA. Second WRI World Congress on, Software Engineering (2010)Google Scholar
  9. 9.
    Kemerer, C.F.: An empirical validation of software cost estimation models. Commun. ACM 30(5), 416–429 (1987)CrossRefGoogle Scholar
  10. 10.
    GalorathandM, D.D., Evans, W.: Software Sizing, Estimation, and Risk Management: When Performance is Measured Performance Improves. Auerbach, Boca Raton (2006)CrossRefGoogle Scholar
  11. 11.
    Pressman, R.S.: Software Engineering: A Practioner’s Approach, 6th edn. McGraw-Hill, New York (2005). ISBN 13: 9780073019338Google Scholar
  12. 12.
    Herd, J.R., Postak, J.N., Russell, W.E., Steward, K.R.: Software cost estimation study–Study results, Final technical report, RADC-TR77-220, vol. I. Doty Associates Inc., Rockville (1977)Google Scholar
  13. 13.
    The PRICE software model user’s manual. Moorestown, PRICE Systems (1993)Google Scholar
  14. 14.
    Walston, C.E., Felix, C.P.: A method of programming measurement and estimation. IBM Syst. J. 16(1), 54–73 (1977)CrossRefGoogle Scholar
  15. 15.
    Rubin, H.A.: Macroestimation of software development parameters: the ESTIMACS system, In: SOFTFAIR Conference on Software Development Tools, Techniques and Alternatives (Arlington, July 25–28) , pp. 109–118. IEEE Press, New York (1983)Google Scholar
  16. 16.
    Robin, H.A.: Using ESTIMACS E. Management and Computer Services, Valley Forge (1984)Google Scholar
  17. 17.
    Checkpoint for Windows User’s Guide, version 2.3.1. Burlington, Software Productivity Research (1996)Google Scholar
  18. 18.
    Walkerden, F., Jeffery, R.: An empirical study of analogy-based software effort estimation. Empirical Softw. Eng. 4, 135–158 (1999). Kluwer Academic Publishers, BostonGoogle Scholar
  19. 19.
    Kocaguneli, E., Bener, A.B.: Exploiting the essential assumptions of analogy-based effort estimation. IEEE Trans. Softw. Eng. 38(2), 425–438 (2012)CrossRefGoogle Scholar
  20. 20.
    Keung, J.: Software development cost estimation using analogy: A review. Australian, software engineering conference, 2009Google Scholar
  21. 21.
    Cuadrado-Gallego, J. J., Rodríguez-Soria, P., Martín-Herrera, B.: Analogies and differences between machine learning and expert based software project effort estimation. 11th ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed, computing (2010)Google Scholar
  22. 22.
    Jorgensen, M.: Practical guidelines for expert-judgment-based software effort estimation. IEEE Softw. 22(3), 57–63 (2005)CrossRefGoogle Scholar
  23. 23.
    Jorgenson, M., Shepperd, M.: A Systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33(1), 33–53 (2007)CrossRefGoogle Scholar
  24. 24.
    Hughes, R.T.: Expert judgment as an estimating method. Inf. Softw. Technol. 38, 67–75 (1996)CrossRefGoogle Scholar
  25. 25.
    Parkinson, G.N.: Parkinson’s Law and Other Studies in Administration. Houghton-Miffin, Boston (1957)Google Scholar
  26. 26.
    Leung, H.: Fan . Software Cost Estimation, IEEE Transactions on Software Engineering, Z. (1984)Google Scholar
  27. 27.
    Heemstra, F.J.: Software cost estimation. Inf. Softw. Technol. 34(10), 627–639 (1992)CrossRefGoogle Scholar
  28. 28.
    Putnam, L. H., Fitzsimmons, A.: Estimating software cost. Datamation (1979)Google Scholar
  29. 29.
    Boehm, B.W.: Software Cost Estimation Using COCOMO II. Prentice-Hall, Englewood Cliffs (2000)Google Scholar
  30. 30.
    Albrecht, A.J., Gaffney, J.E.: Software function, source lines of codes and development effort prediction: a software science validation. IEEE Trans. Softw. Eng. 9, 639–648 (1983)CrossRefGoogle Scholar
  31. 31.
    Alkoffash, M.S., Bawanehand, M.J.: Al Rabea, Ai: Which software cost estimation model to choose in a particular project. J. Comput. Sci. 4(7), 606–612 (2008)CrossRefGoogle Scholar
  32. 32.
    Khatibi, V., Jawawi, D.N.A.: Software cost estimation methods: a review. J. Emerg. Trends Comput. Inf. Sci. 2(1), 21–29 (2011)Google Scholar
  33. 33.
    Idri, A., Abran, A., Khoshgoftaar, T. M.: Estimating software project effort by analogy based on linguistic values metrics. Eighth IEEE international symposium on software metrics (METRICS’02), pp. 21, 2002Google Scholar
  34. 34.
    Idri, A., Abran, A., Khoshgoftaar, T.M.: Fuzzy case-based reasoning models for software cost estimation. Soft Computing in Software Engineering, pp. 64–96. Springer-Verlag, Berlin (2004)Google Scholar
  35. 35.
    Morasca, S., Briand, L. C.: Towards a theoretical framework for measuring software attributes. In: Proceedings of the Fourth International Symposium on Software Metrics, Albuquerque, November 1997, pp. 119–126. IEEE Computer Society (1997)Google Scholar
  36. 36.
    Sommerville, I.: Software Engineering, 7th edn. Addison-Wesley, Boston (2004)Google Scholar
  37. 37.
    Clarke, P., O’Connor, R.V.: The situational factors that affect the software development process: Towards a comprehensive reference framework. J. Inf. Softw. Technol. 54(5), 433–447 (2012)CrossRefGoogle Scholar
  38. 38.
    Lagerström, R., von Würtemberg, L.V., Holm, H., Luczak, O.: Identifying factors affecting software development cost. Proceedings of the Fourth International Workshop on Software Quality and Maintainability (SQM), March, In (2010)Google Scholar
  39. 39.
    Raschia, G., Mouaddib, N.: SAINTETIQ: a fuzzy set-based approach to database summarization. Fuzzy Sets Syst. 129, 137–162 (2002)CrossRefMATHMathSciNetGoogle Scholar
  40. 40.
    Zadeh, L.A.: Fuzzy set. Inf. Control 8, 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar
  41. 41.
    Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Comput. Math. 9, 149–184 (1983)MATHMathSciNetGoogle Scholar
  42. 42.
    Khatibi, V., Jawawi, D.N.A.: Software cost estimation methods: A review. CIS J. 2, 21–29 (2011)Google Scholar
  43. 43.
    McCall, J. A., Richards, P. K., Walters, G. F.: Factors in software quality. Technical report RADC-TR-77-369. U.S. Department of Commerce, Washington, DC (1977)Google Scholar
  44. 44.
    Pressman, R.S.: Software Engineering: A Practitioner’s Approach, 5th edn. McGraw-Hill series in computer science, New York (2001)Google Scholar
  45. 45.
    Shepperd, M., Schofield, C.: Estimating software project effort using analogies. IEEE Trans. Softw. Eng. 23(12), 736–743 (1997)CrossRefGoogle Scholar
  46. 46.
    Mukhopadhyay, T., Vicinanza, S., Prietula, M.J.: Examining the feasibility of a case-based reasoning model for software effort estimation. MIS Quart. 16(2), 155–171 (1992)CrossRefGoogle Scholar
  47. 47.
    Kosti, M. V., Mittas, N., Angelis, L.: DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions, 3rd artificial intelligence techniques in software engineering workshop, Larnaca, 7 October 2010Google Scholar
  48. 48.
    Keung, J.W., Kitchenham, B.A., Jeffery, D.R.: Analogy-X: Providing statistical inference to analogy-based software cost estimation. IEEE Trans. Softw. Eng. 34(4), 471–484 (2008)CrossRefGoogle Scholar
  49. 49.
    Shepperd, M., Kadoda, G.: Using simulation to evaluate predictions systems. In: Proceedings of the 7th International Symposium on Software Metrics, England, pp. 349–358. IEEE Computer Society (2001)Google Scholar
  50. 50.
    Coomans, D., Massart, D.L.: Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-nearest neighbour classification by using alternative voting rules. Anal. Chim. Acta 136, 15–27 (1982). doi: 10.1016/S0003-2670(01)95359-0 CrossRefGoogle Scholar
  51. 51.
    Shin, M., Goel, A.L.: Emprirical data modeling in software engineering using radial basis functions. IEEE Trans. Softw. Eng. 26, 567–576 (2000)CrossRefGoogle Scholar
  52. 52.
    ISBG: Online data repository. Accessed 28 Feb 2012

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMNNITAllahabadIndia
  2. 2.Department of Computer Science and EngineeringMNNITAllahabadIndia

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