{"title":"Estimating Development Time of Software Projects Using a Neuro Fuzzy Approach","authors":"Venus Marza, Amin Seyyedi, Luiz Fernando Capretz","volume":22,"journal":"International Journal of Computer and Information Engineering","pagesStart":3422,"pagesEnd":3427,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/5733","abstract":"Software estimation accuracy is among the greatest\r\nchallenges for software developers. This study aimed at building and\r\nevaluating a neuro-fuzzy model to estimate software projects\r\ndevelopment time. The forty-one modules developed from ten\r\nprograms were used as dataset. Our proposed approach is compared\r\nwith fuzzy logic and neural network model and Results show that the\r\nvalue of MMRE (Mean of Magnitude of Relative Error) applying\r\nneuro-fuzzy was substantially lower than MMRE applying fuzzy\r\nlogic and neural network.","references":"[1] H. Park, S. Baek, \"An empirical validation of a neural network model\r\nfor software effort estimation\", Expert Systems with Applications, 2007.\r\n[2] C. Lopez-Martin, C.Yanez-Marquez, A.Gutierrez-Tornes, \"Predictive\r\naccuracy comparison of fuzzy models for software development effort of\r\nsmall programs, The journal of systems and software\", Vol. 81, Issue 6,\r\n2008, pp. 949-960.\r\n[3] J. Jantzen, \"Neuro-fuzzy modeling\", Report no 98-H-874, 1998.\r\n[4] W. Xia, L.F. Capretz, D. Ho, F.Ahmed, \"A new calibration for function\r\npoint complexity weights\", Information and Software Technology,\r\nVol.50, Issue 7-8, 2007, pp.670-683.\r\n[5] M. Jorgensen, B. Faugli, T. Gruschke, \"Characteristics of software\r\nengineers with optimistic prediction\", Journal of Systems and Software,\r\nVol. 80, Issue. 9, 2007, pp. 1472-1482.\r\n[6] C.L. Martin, J.L. Pasquier, M.C. Yanez, T.A. Gutierrez, \"Software\r\nDevelopment Effort Estimation Using Fuzzy Logic: A Case Study\",\r\nIEEE Proceedings of the Sixth Mexican International Conference on\r\nComputer Science (ENC-05), 2005, pp. 113-120.\r\n[7] M.T. Su, T.C.Ling, K.K.Phang, C.S.Liew, P.Y.Man, \"Enhanced\r\nSoftware Development Effort and Cost Estimation Using Fuzzy Logic\r\nModel\", Malaysian Journal of Computer Science, Vol. 20, No. 2, 2007,\r\npp. 199-207.\r\n[8] A. Heiat, \"Comparison of artificial neural network and regression\r\nmodels for estimating software development effort\", Information and\r\nSoftware Technology, Vol. 44, Issue 15, 2002, pp. 911-922.\r\n[9] X. Huang, Danny Ho, J. Ren, L.F. Capretz, \"Improving the COCOMO\r\nmodel using a neuro-fuzzy approach\", Applied Soft Computing , Vol.7,\r\nIssue 1, 2007, pp. 29-40.\r\n[10] A. Idri, A.Abran, \"A Fuzzy Logic Based Set of Measures for Software\r\nProject Similarity: Validation and Possible Improvements\", Proceedings\r\nof the seventh international software metrics symposium (METRICS\r\n-01), 2001, pp.85-96.\r\n[11] S.N. Sivanandam, S. Sumathi, S.N. Deepa, \"Introduction to fuzzy logic\r\nusing MATLAB\", Springer, 2007.\r\n[12] A. Lotfi Zadeh, \"From Computing with Numbers to Computing with\r\nWords - From Manipulation of Measurements to Manipulation of\r\nPerceptions\", IEEE Transactions on Circuits and Systems, Fundamental\r\nTheory and Applications, Vol. 45, No 1, 1999, pp 105-119.\r\n[13] M.R.Braz & S.R.Vergilio, \"Using Fuzzy Theory for Effort Estimation of\r\nObject-Oriented Software\", Proceedings of the 16th IEEE international\r\nConference on Tools with Artificial Intelligence (ICTAI 2004), 2004,\r\npp. 196-201.\r\n[14] K.K.Aggarwal, Y.Singh, P.Chandra, M.Puri, \"Sensitivity Analysis of\r\nFuzzy and Neural Network Models\", ACM SIGSOFT Software\r\nEngineering Notes, Vol. 30, Issue 4, 2005, pp. 1-4.\r\n[15] A.A. Moataz, O.S.Moshood, A.Jarallah, \"Adaptive fuzzy-logic-based\r\nframework for software development effort prediction\", Information and\r\nSoftware Technology, Vol. 47, Issue 1, 2005, pp. 31-48.\r\n[16] W.S. Humphrey, \"A Discipline for Software Engineering\", Addison\r\nWesley, 2002.\r\n[17] S. Mitra, Y.Hayashi, \"Neuro-Fuzzy Rule Generation: Survey in Soft\r\nComputing Framework\", IEEE Transactions on Neural Networks,\r\nVol.11, No.3, 2000, pp. 748-768.\r\n[18] D. Nauck, F. Klawonn, R. Kruse, \"Foundations of Neuro-Fuzzy\r\nSystems\", Wiley, Chichester, 1997.\r\n[19] D. Nauck, \"A Fuzzy Perceptron as a Generic Model for Neuro-Fuzzy\r\nApproaches\", In Proceedings of Fuzzy-Systeme-94, 2nd GI-Workshop,\r\nMunich, Semen Corporation, 1994.\r\n[20] M.O. Saliu, \"Adaptive Fuzzy Logic Based Framework for Software\r\nDevelopment Effort Prediction\", A Thesis Presented to the DEANSHIP\r\nOF GRADUATE STUDIES, King Fahd University of Petroleum &\r\nMinerals Dhahran, April 2003.\r\n[21] A. Abraham, \"Adaptation of Fuzzy Inference System Using Neural\r\nLearning\", Springer-Verlag Berlin Heidelberg, 2005, pp. 59-83.\r\n[22] Y. Shi, M. Mizumoto, N.Yubazaki, M. Otani, \"A Learning Algorithm\r\nfor Tuning Fuzzy Rules Based on the Gradient Descent Method\",\r\nProceedings of Fifth IEEE International Conference on Fuzzy Systems\r\n(FUZZ-IEEE'96), New Orleans, USA, Vol.1, 1996, pp.55-61.\r\n[23] V. Xia, L. F. Capretz, D. Ho, \"A Neuro-Fuzzy Model for Function Point\r\nCalibration\", WSEAS Transactions on Information Science &\r\nApplications, Vol. 5, Issue 1, 2008, pp. 22-30.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 22, 2008"}