Artificial Intelligent Systems and Machine Learning

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Software Cost Estimation Based on the Hybrid Model of Input Selection Procedure and Artificial Neural Network


Affiliations
1 Department of Computer Science, University of Kashmir, J&K, India
2 Department of Computer Science and Hony. Addl., FTK-Centre for Information Technology, Jamia Millia Islamia, Central University, Jamia Nagar, New Delhi, India
     

   Subscribe/Renew Journal


Software effort estimation is the forecasting of development effort and development time needed to develop any software project. It is considered to be the very primary step of software development process and at the same time considered to be the key task as accurate assessments of growth of the current project, its delivery exactness and its cost control can only be achieved once desired estimation is accurate. And at broader perspective an accurate estimation of a currently developing software product will result in landing the organization in a better schedule of its futuristic software projects too. With due above reason, software effort estimation has received a considerable amount of attention of many researchers from past so many decades. In this paper, software cost estimation is done by first performing a proposed input selection procedure to get the relevant set of cost drivers and leaving behind the irrelevant attributes. In the next step, it is now only these relevant set of attributes that are being assigned to Artificial Neural Network as its input for the purpose of getting the accurate estimation of software development effort and cost. Removing the irrelevant cost drivers at the very first step directly leads to attain accurate software cost estimation results. Besides this the proposed model results in a significant decrease of complexities associated with traditional Artificial Neural Network based Software cost estimation models. For the purpose of evaluation of proposed model, Magnitude of Relative Error and Median of Magnitude of Relative Error are used as a measure of performance index to weigh the obtained quality of estimation which becomes more evident when later compared with two existing models. After an extensive evaluation of results, it showed that the proposed model performs well in software cost estimation.

Keywords

Artificial Neural Network, Functional Link Artificial Neural Network, Genetic Algorithms, Input Selection Procedure, Software Cost Estimation.
User
Subscription Login to verify subscription
Notifications
Font Size

  • B.W. Boehm, Software Engineering Economics, Prentice-Hall, Englewood Cliffs, New Jersy, 1981.

  • B.W. Boehm, Software Cost Estimation with COCOMO II, Prentice Hall PTR, Englewood Cliffs, New Jersy, 2000.

  • Kitchenham, B., Pickard, L.M., Linkman, S., Jones, P.W., 2003. Modeling software bidding risks. IEEE Transactions on Software Engineering 29 (6), 542-554.

  • Fenton, N.E., Pleeger, S.L., 1997. Software Metrics: A Rigorous and Practical Approach, second ed. PWS, Boston, MA, USA.

  • Pressman, R.S., 1997. Software Engineering: A Practitioner’s Approach, fourth ed. McGraw-Hill, New York, NY, USA.

  • Miyazaki, Y., Terakado, Y., Ozaki, K., Nozaki, N., 1994. Robust regression for developing Software estimation models. Journal of System and Software 27 (1), 16-35.

  • Gray, A.R., 1999. A simulation-based comparison of empirical modeling techniques for software metric models of development effort. In: Proceedings of ICONIP, Sixth International Conference on Neural Information Processing, Perth, WA, Australia, vol. 2, pp. 526-531.

  • Kemerer, C., 1987. An empirical validation of software cost estimation models. Communications of the ACM 30 (5), 416-429.

  • Verkatachalm, A.R., 1993. Software cost estimation using artificial neural networks. International Joint Conference on Neural Networks, vol. 1.IEEE, Nagoya, pp. 987-990.

  • Xu, Z., Khoshgoftaar, T.M., 2004. Identification of fuzzy models of cost estimation. Fuzzy Sets and Systems 145, 141-163.

  • Hughes, R.T., 1996. An evaluation of machine learning techniques for software effort estimation. University of Brighton.

  • Jorgerson, M., 1995. Experience with accuracy of software maintenance task effort prediction models. IEEE Transaction on Software Engineering 21 (8), 674-681.

  • Samson, B., Ellison, D., Dugard, P., 1997. Software cost estimation using an Albus perceptron (CMAC). Information and Software Technology 39 (1), 55-60.

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

  • Seluca, C., 1995. An investigation into software effort estimation using a back-propagation neural network, M.Sc.Thesis, Bournemouth University,UK.

  • Srinivasan, K., Fisher, D., 1995. Machine learning approaches to estimating software development effort. IEEE Transaction on Software Engineering 21 (2), 126-136.

  • Wittig, G., Finnie, G., 1997. Estimating software development effort with connectionist models. Information and Software Technology 39 (7),469-476.

  • Schofield, C., 1998. Non-algorithmic effort estimation techniques. Technical Report TR98-01.

  • Chiu, N.H., Huang, S.J., 2007. The adjusted analogy-based software effort estimation based on similarity distances. Journal of Systems and Software 80 (4), 628-640.

  • Vinay Kumar K.; Ravi V.; Mahil Carr; Raj Kiran N., (2008). Software development cost estimation using wavelet neural networks, The Journal of Systems and Software 81(11), 1853-1867.

  • Tirimula Rao B.; Sameet B.; Kiran Swathi G.; Vikram Gupta K.; Ravi Teja;Ch, Sumana S., (2009), A Novel Neural Network Approach for Software Cost Estimation using Functional Link Artificial Neural Network (FLANN), International Journal of Computer Science and Network Society 9(6), 126-131.

  • Reddy C.S.; Raju KVSN, (2009). An Improved Fuzzy Approach for COCOMO’s Effort Estimation using Gaussian Membership Function. Journal of software 4(5), 452-459.

  • F.S. Gharehchopogh, “Neural Networks Application in Software Cost Estimation: A Case Study”, 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2011), pp. 69-73, IEEE, Istanbul, Turkey, 15-18 June 2011.

  • A.F. Sheta, “Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects”, Journal of Computer Science, Vol. 2, No. 2, pp.118-123, 2006.

  • Ziauddin, Sh. K.Tipu, Kh. Zaman, Sh. Zia, “Software Cost Estimation Using Soft Computing Techniques”, Advances in Information Technology and Management (AITM), Vol. 2, No. 1, pp. 233-238, 2012.

  • Mair, G. Kadoda, M. Lefley, K. Phalp, Ch. Schofield, M. Shepperd, S. Webster, “An Investigation of Machine Learning Based Prediction Systems”, Journal of systems and software, Elsevier, Vol. 53, Issue 1, pp. 23-29, 2000.

  • Prasad Reddy P.V.G.D, Hari CH.V.M.K, S. Rao.T, “Multi Objective Particle Swarm Optimization for Software Cost Estimation”, International Journal of Computer Applications, Vol. 32, No.3, pp. 13-17, October 2011.

  • Z.A. KHALIFELU, F.S. GHAREHCHOPOGH, "Comparison and Evaluation Data Mining Techniques with Algorithmic Models in Software Cost Estimation", Elsevier, Procedia-Technology Journal, Vol. 1, pp. 65-71, 2012.

  • Mair, M. Shepperd, and M. Jorgensen, “An Analysis of Datasets Used to Train and Validate Cost Prediction Systems,” ACM SIGSOFT Software Eng. Notes, vol. 4, pp. 1-6, 2005.

  • T. Menzies, Z. Chen, J. Hihn, and K. Lum, “Selecting Best Practices for Effort Estimation,” IEEE Trans. Software Eng., vol. 32, no. 11, pp. 883-895, Nov. 2006.

  • S.D. Conte, H.E. Dunsmore, and V.Y. Shen, Software Engineering Metrics and Models. The Benjamin/Cummings Publishing Company, Inc., 1986.

  • Port and M. Korte, “Comparative Studies of the Model Evaluation Criterions MMRE and PRED in Software Cost Estimation Research,” Proc. Second ACM-IEEE Int’l Symp. Empirical Software Eng. and Measurement, pp. 51-60, Oct. 2008.

  • B. Tirimula Rao, B. Sameet, G. Kiran Swathi, K. Vikram Gupta, Ch. RaviTeja, S.Sumana, “A Novel Neural Network Approach For Software Cost Estimation Using Functional Link Artificial Neural Network (FLANN), ” IJCSNS International Journal of Computer Science and Network Security, Vol. 9, No.6, June 2009.


Abstract Views: 248

PDF Views: 5




  • Software Cost Estimation Based on the Hybrid Model of Input Selection Procedure and Artificial Neural Network

Abstract Views: 248  |  PDF Views: 5

Authors

Zahid Hussain Wani
Department of Computer Science, University of Kashmir, J&K, India
S. M. K. Quadri
Department of Computer Science and Hony. Addl., FTK-Centre for Information Technology, Jamia Millia Islamia, Central University, Jamia Nagar, New Delhi, India

Abstract


Software effort estimation is the forecasting of development effort and development time needed to develop any software project. It is considered to be the very primary step of software development process and at the same time considered to be the key task as accurate assessments of growth of the current project, its delivery exactness and its cost control can only be achieved once desired estimation is accurate. And at broader perspective an accurate estimation of a currently developing software product will result in landing the organization in a better schedule of its futuristic software projects too. With due above reason, software effort estimation has received a considerable amount of attention of many researchers from past so many decades. In this paper, software cost estimation is done by first performing a proposed input selection procedure to get the relevant set of cost drivers and leaving behind the irrelevant attributes. In the next step, it is now only these relevant set of attributes that are being assigned to Artificial Neural Network as its input for the purpose of getting the accurate estimation of software development effort and cost. Removing the irrelevant cost drivers at the very first step directly leads to attain accurate software cost estimation results. Besides this the proposed model results in a significant decrease of complexities associated with traditional Artificial Neural Network based Software cost estimation models. For the purpose of evaluation of proposed model, Magnitude of Relative Error and Median of Magnitude of Relative Error are used as a measure of performance index to weigh the obtained quality of estimation which becomes more evident when later compared with two existing models. After an extensive evaluation of results, it showed that the proposed model performs well in software cost estimation.

Keywords


Artificial Neural Network, Functional Link Artificial Neural Network, Genetic Algorithms, Input Selection Procedure, Software Cost Estimation.

References