Comparison of hard limiting and soft computing methods for predicting software effort estimation: In reference to Small Scale Visualization Projects

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    It is a well known fact that software effort estimation is exceptionally critical in every software industry, particular during the development of projects. It is hard to estimate the parameters involved due to ambiguity and uncertainty associated with the parameters. It is exactly here the hard limiting techniques, soft computing techniques comes to play. In this unique circumstance, this paper, presents an attempt to that compare the two paradigms for effort estimation. For this, we have considered fifty real time small visualization projects thrive by post graduate students.  The prototype development involves following stages:

             i)            Elicitation of seven novel parameters namely Lines of Code, Cumulative Grade Point Average, New and changed code, Reused code, Cyclomatic Complexity, Algorithmic Complexity and Functional Points.

           ii)            Developing of hard limiting methods and soft computing methods for prediction of software effort involved in terms of duration in minutes.

    For the validation of the models error metrics namely: Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), Mean of Magnitude of error Relative to the Estimate (MMER) and Root Mean Square Error (RMSE) have been used. The result showed that the models compared very well with marginal difference in terms of predict values of error matrix.

     

     


  • Keywords


    Feed-Forward neural networks; Fuzzy Logic; Multiple linear regression models; multiple non-linear regression models; Software effort estimation.

  • References


      [1] E. Mendes, N. Mosley, I. Watson, A comparison of case-based reasoning approaches to web hypermedia project cost estimation, in: Proceedings of the 11th international conference on World Wide Web, ACM, (2002), pp.272-280.

      [2] Ali Bou Nassif, Danny Ho, Luiz Fernando Capretz, Towards an early software estimation using log-linear regression and a multilayer perceptron model, Journal of Systems and Software, Volume 86, Issue 1, January (2013), Pages 144-160, ISSN 0164-1212,10.1016/j.jss.2012.07.050.

      [3] Lopez-Martin C.,( 2011). A fuzzy logic model for predicting the development effort of short scale programs based upon two independent variables. Applied Soft Computing. Pp 724-732.

      [4] Bathla R., Singh S., Vishav M., Gupta R.,( 2010). Innovative scenario in software development effort estimation based on a new fuzzy logic model. International Journal of Information Technology and Knowledge Management. July-December 2010, Volume 2, No. 2, pp. 361-364.

      [5] Muzaffar Z. And Ahmed M.A., (2010). Software development effort prediction: A study on the factors impacting the accuracy of fuzzy logic systems. Information and Software Technology 52 (2010) 92–109.

      [6] Ahmed M.A., Muzaffar Z., (2009) Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework. Information and Software Technology 51. Pp. 640–654.

      [7] Attarzadeh I. and Hock O. S., (2009). Software Development Effort Estimation Based on a New Fuzzy Logic Model. International Journal of Computer Theory and Engineering, Vol. 1, No. 4.

      [8] Nisar M.W., Wang Y-J., Elahi M., Khan I.A., (2009).Software development effort estimation using fuzzy logic. Information Technology Journal.

      [9] M. A. Ahmed, M. O. Saliu, and J. AlGhamdi, “Adaptive fuzzy logic-based framework for software development effort prediction”, Information and Software Technology Journal, Volume 47 (2005), pp. 31-48.

      [10] G. D. Boetticher, “An assessment of metric contribution in the construction of a neural network-based effort estimator”, Proceedings of Second International Workshop on Soft Computing Applied to Software Engineering,( 2001).

      [11] A. C. Hodgkinson, and P. W. Garratt, “A neuro fuzzy cost estimator”, Proceedings of the Third International Conference on Software Engineering and Applications—SAE (1999), pp. 401–406.

      [12] Cuauthemoc Lopez-Martin, A Fuzzy Logic model for predicting the development effort of short scale programs based upon two independent variables, Applied Soft Computing 11(2011) 724-732.

      [13] PichaiJodpimai, PeraphonSophatsathit, and ChidchanokLursin- sap, ”Analysis of Effort Estimation based on Software Project Models”, IEEE, 2009.

      [14] S.D. Conte, H.E. Dunsmore and V.Y. shen, “Software engineering metrics and models” ,European Journal of Operational Research 28(2), (1987), PP.235-236.

      [15] M.A.Jayaram,T.M.KiranKumar,H.V.Raghavendra. “Models for predicting development effort of small – scale visualization projects”, Journal of Intelligent Systems, 20160247, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2016-0247.

      [16] E. Mendes, N. Mosley, I. Watson, A comparison of case-based reasoning approaches to web hypermedia project cost estimation, in: Proceedings of the 11th international conference on World Wide Web, pp.272-280,ACM(2002)

      [17] S.G. MacDonell, A.R. Gray, Alternatives to regression models for estimating software projects, in: Proceedings of the IFPUG Fall Conference, Dallas TX, IFPUG,(1996)

      [18] Crespo, F.J., Sicicila, M.A., Cuadrado, J.J., On the use of fuzzy regression in parametric software estimation models: integrating imprecision in COCOMO cost drivers. WSEAS Transactions on Systems, (2004).

      [19] Yunsik Ahn, Jungseok Suh, Seungryeol Kim, Hyunsoo Kim, “The software maintenance projects effort estimation model based on function points”, Journal of Software maintenance and evolution, Volume 15, Issue 2, pp. 71–85, ( 2003)

      [20] Heiat, A.: Comparison of artificial neural network and regression models for estimating software development effort. Journal of Information and Software Technology, 44(15), pp.911-922, (2002).

      [21] W. Humphrey W., “A Discipline for Software Engineering. Addison Wesley”(1995).


 

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Article ID: 20715
 
DOI: 10.14419/ijet.v7i4.6.20715




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