2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 29-40, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Zainab Rustum Mohsin 1 * , Firoj Khan 2
Doi: https://doi.org/10.54216/JISIoT.150203
Software effort estimation is highly significant for project management regarding the bidding process since underestimation leads to financial losses, while overestimation may bring the chance of losing a competitive bid. Whereas numerous models have been designed up until now, those developed upon machine learning, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN) have emerged as preeminent technologies. The proposed research will explore the effectiveness of using the ANN and ANFIS approaches in the estimation of effort for NASA datasets by 13 observations used for training and the rest for the test. To check the precision of models, several measures are used to evaluate the accuracy of the developed model, including the correlation coefficient, RMSE, and MMRE. The findings demonstrate that ANN and ANFIS exhibit superior performance, yielding much higher prediction accuracy compared to conventional Models including Walston-Felix, Doty, Bailey-Basili, and Halstead. It emphasizes ANN and ANFIS as reliable and straightforward software effort estimating methodologies, hence yielding significant enhancements in estimation precision and competitiveness. Their high performance underlines their usefulness to project managers who seek accurate predictions. This study strongly recommends the application of data-driven approaches like ANN and ANFIS to enhance the overall estimation accuracy in software project bidding.
NASA , Data-Driven Models , ANN , ANFIS , Software Effort
[1] S. McConnell, Rapid Development: Taming Wild Software Schedules. Pearson Education, 1996.
[2] A. Idri and I. Abnane, "Fuzzy analogy-based effort estimation: An empirical comparative study," in Proc. 2017 IEEE Int. Conf. on Computer and Information Technology (CIT), 2017, pp. 114–121.
[3] S. H. S. Moosavi and V. K. Bardsiri, "Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation," Eng. Appl. of Artif. Intell., vol. 60, pp. 1–15, 2017.
[4] B. Boehm, Software Engineering Economics. Prentice-Hall, Englewood Cliffs, NJ, 1981.
[5] G. Cantone, A. Cimitile, and U. De Carlini, "A comparison of models for software cost estimation and management of software projects," in Computer Systems: Performance and Simulation, Elsevier, 1986.
[6] L. H. Putnam, "A general empirical solution to the macro software sizing and estimating problem," IEEE Trans. Softw. Eng., vol. SE-4, pp. 345–361, 1978.
[7] F. N. Parr, "An alternative to the Rayleigh curve model for software development effort," IEEE Trans. Softw. Eng., vol. SE-6, pp. 291–296, 1980.
[8] P. S. Sandhu, M. Prashar, P. Bassi, and A. Bisht, "A model for estimation of efforts in development of software systems," Int. J. Comput. Syst. Eng., vol. 3, no. 8, pp. 1931–1935, 2009.
[9] S. Devnani-Chulani, "Modeling software defect introduction," in Proc. California Softw. Symp., 1999.
[10] B. Clark, S. Devnani-Chulani, and B. Boehm, "Calibrating the COCOMO II post-architecture model," in Proc. 20th Int. Conf. Softw. Eng., 1998, pp. 477–480.
[11] A. M. R. Ahmed, M. A. Ali, N. Ahmed, M. F. B. Zamal, and F. M. J. M. Shamrat, "Software defect prediction with Bayesian approaches," Mathematics, vol. 11, no. 11, pp. 2524, 2022.
[12] R. E. D. Reverter, L. L. D. G. Nunes, and P. M. C. K. L. Nunes, "Software defect prediction using Bayesian networks," Empirical Softw. Eng., vol. 19, no. 2, pp. 419–456, 2014.
[13] Z. R. Mohsin, "Investigating the use of an adaptive neuro-fuzzy inference system in software development effort estimation," Iraqi J. Comput. Sci. Math., vol. 2, no. 2, pp. 18–24, 2021.
[14] Z. R. Mohsin, "Application of artificial neural networks in prediction of software development effort," Turkish J. Comput. Math. Educ. (TURCOMAT), vol. 12, no. 14, pp. 4186–4202, 2021.
[15] P. Rijwani and S. Jain, "Enhanced software effort estimation using multi-layered feedforward artificial neural network technique," Procedia Comput. Sci., vol. 89, pp. 307–312, 2016.
[16] A. B. Nassif, M. Azzeh, L. F. Capretz, and D. Ho, "Neural network models for software development effort estimation: A comparative study," Neural Comput. Appl., vol. 27, no. 8, pp. 2369–2381, 2016.
[17] A. Heiat, "Comparison of artificial neural network and regression models for estimating software development effort," Inf. Softw. Technol., vol. 44, no. 15, pp. 911–922, 2002.
[18] A. Salam, A. Khan, and S. Baseer, "A comparative study for software cost estimation using COCOMO-II and Walston-Felix models," in Proc. 1st Int. Conf. Innovations in Comput. Sci. Softw. Eng., ICONICS, 2016, pp. 15–16.
[19] S. Nanda and B. Soewito, "Modeling software effort estimation using hybrid PSO-ANFIS," in 2016 Int. Sem. Intell. Technol. Appl. (ISITIA), 2016, pp. 219–224.
[20] Z. R. Mohsin, "Comparative study for software effort estimation by soft computing models," J. Educ. Pure Sci.-Univ. Thi-Qar, vol. 11, no. 2, pp. 108–120, 2021.
[21] S. Kamal and J. A. Nasir, "A fuzzy logic-based software cost estimation model," Int. J. Softw. Eng. Appl., vol. 7, no. 2, pp. 7–18, 2013.
[22] E. P. Edinson and L. Muthuraj, "Performance analysis of FCM-based ANFIS and ELMAN neural network in software effort estimation," Int. Arab J. Inf. Technol., vol. 15, no. 1, pp. 94–102, 2018.
[23] H. Tanarslan, M. Secer, and A. Kumanlioglu, "An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks," Constr. Build. Mater., vol. 30, pp. 556–568, 2012.
[24] S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall, 1999.
[25] D. R. Baughman and Y. A. Liu, Neural Networks in Bioprocessing and Chemical Engineering. Academic Press, 2014.
[26] A. M. Melesse and R. S. Hanley, "Artificial neural network application for multi-ecosystem carbon flux simulation," Ecol. Model., vol. 189, no. 3–4, pp. 305–314, 2005.
[27] M. Bilgili, B. Sahin, and A. Yasar, "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renew. Energy, vol. 32, no. 14, pp. 2350–2360, 2007.
[28] J.-S. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, 1993.
