A Novel PMFFC -Based Software Effort Estimation Using FMGKF-DENN Algorithm


  • K. Harish Kumar Research Scholar, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, India,500075 and Assisstant Professor, Department of Computer Science & Informatics.,Mahatma Gandhi University, Nalgonda,Telangna,India 508001
  • K. Srinivas Professor, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, India


Software, Effort estimation, project management, feature extraction, Sea Turtle Foraging Optimization (STFO), Neural Network (NN), Deviation


Software Effort Estimation (SEE) is getting more concerned owing to the software industry’s development. An increase in the deadline along with the budget of the project was led by an incorrect estimation. This may fail the project in turn. Using the Fisher and Mish Gaussian Kernel Function based Deep Elman Neural Network (FMGKF-DENN) algorithm, a novel Pearson and Mahalanobis- centered Farthest First Clustering (PMFFC)-centered SEE was proposed in this study. Primarily, through determining specific factors like scope, objectives, Infrastructure, and characteristics, the details regarding the provided historical projects are obtained. For efficient task completion, the projects are grouped utilizing the PMFFC algorithm grounded on the details gathered.  Next, the classes split the code for those specific types of projects. After that, the input data is pre-processed. Certain features are retrieved as of the pre-processed data and the Controlled Sea Turtle Foraging Optimization (CSTFO) approach chooses the crucial features. The deviation value is deemed as a target for efficient SEE in the FMGKF-DENN, where the selected features are fed. The experiential outcomes illustrated that the SEE process was executed more accurately by the proposed framework along with outperforming various top-notch models.


Download data is not yet available.


Sharma and N. Chaudhary, “Linear regression model for agile software development effort estimation,” 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2020 - Proceeding, vol. 2020, pp. 4–7, 2020, doi: 10.1109/ICRAIE51050.2020.9358309.

S. Shukla, S. Kumar, and P. R. Bal, “Analyzing effect of ensemble models on multi-layer perceptron network for software effort estimation,” Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019, vol. 2642–939X, pp. 386–387, 2019, doi: 10.1109/SERVICES.2019.00116.

O. Malgonde and K. Chari, "An ensemble-based model for predicting agile software development effort", Empirical Software Engineering, 2019. doi: 10.1007/s10664-018-9647-0.

Y. Mahmood, N. Kama, A. Azmi, and M. Ali, “Improving estimation accuracy prediction of software development effort: a proposed ensemble model,” 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020, no. June, pp. 12–13, 2020, doi: 10.1109/ICECCE49384.2020.9179279.

R. Sari Dewi and R. Sarno, “Software effort estimation using early COSMIC to substitute use case weight,” Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, ISemantic 2020, X, pp. 214–219, 2020, doi: 10.1109/iSemantic50169.2020.9234227.

D. S. Senevirathne and T. K. Wijayasiriwardhane, “Extending use-case point-based software effort estimation for Open Source freelance software development,” Proceedings - International Research Conference on Smart Computing and Systems Engineering. SCSE 2020, pp. 188–194, 2020, doi: 10.1109/SCSE49731.2020.9313007.

H. D. P. De Carvalho, R. Fagundes, and W. Santos, “Extreme learning machine applied to software development effort estimation,” IEEE Access, vol. 9, pp. 92676–92687, 2021, doi: 10.1109/ACCESS.2021.3091313.

K. Korenaga, A. Monden, and Z. Yucel, “Data smoothing for software effort estimation,” Proceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2019, pp. 501–506, 2019, doi: 10.1109/SNPD.2019.8935841.

M. Fernández-Diego, E. R. Méndez, F. González-Ladrón-De-Guevara, S. Abrahão, and E. Insfran, “An update on effort estimation in agile software development: A systematic literature review,” IEEE Access, vol. 8, pp. 166768–166800, 2020, doi: 10.1109/ACCESS.2020.3021664.

M. Daud and A. A. Malik, “Improving the accuracy of early software size estimation using analysis-to-design adjustment factors (ADAFs),” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3085752.

P. Phannachitta, “On an optimal analogy-based software effort estimation,” Information and Software Technology., vol. 125, no. May, p. 106330, 2020, doi: 10.1016/j.infsof.2020.106330.

T. R. Benala and R. Mall, “DABE: Differential evolution in analogy-based software development effort estimation,” Swarm and Evolutionary Computation., vol. 38, pp. 158–172, 2018, doi: 10.1016/j.swevo.2017.07.009.

T. Xia, R. Shu, X. Shen, and T. Menzies, “Sequential model optimization for software effort estimation,” IEEE Transactions on Software Engineering., vol. 5589, no. c, pp. 1–16, 2020, doi: 10.1109/TSE.2020.3047072.

Minku, L. L., (2019). A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation. Empirical Software Engineering. https://doi.org/10.1007/s10664-019-09686-w

J. T. H. Jose Thiago and A. L. I. Oliveira, “Ensemble effort estimation using dynamic selection,” Journal of Systems and Software., vol. 175, p. 110904, 2021, doi: 10.1016/j.jss.2021.110904.

J. Frank Vijay, “Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics,” Neural Computing. Applications., vol. 31, no. 5, pp. 1633–1639, 2019, doi: 10.1007/s00521-018-3565-3.

V. Resmi, S. Vijayalakshmi, and R. S. Chandrabose, “An effective software project effort estimation system using optimal firefly algorithm,” Cluster Computing., vol. 22, pp. 11329–11338, 2019, doi: 10.1007/s10586-017-1388-0.

A. Fadhil, R. G. H. Alsarraj, and A. M. Altaie, “Software cost estimation based on dolphin algorithm,” IEEE Access, vol. 8, pp. 75279–75287, 2020, doi: 10.1109/ACCESS.2020.2988867.

Singh, J., Bilgaiyan, S., Shankar Prasad Mishra, B., Dehuri, S., (2020), "A journey towards bio-inspired techniques in software engineering", Intelligent Systems Reference Library, https://doi.org/10.1007/978-3-030-40928-9

S. Bilgaiyan, S. Mishra, and M. Das, “Effort estimation in agile software development using experimental validation of neural network models,” International Journal of Information Technology., vol. 11, no. 3, pp. 569–573, 2019, doi: 10.1007/s41870-018-0131-2..

Ms. Madhuri Zambre. (2012). Performance Analysis of Positive Lift LUO Converter . International Journal of New Practices in Management and Engineering, 1(01), 09 - 14. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/3

Sharma, M. K. (2021). An Automated Ensemble-Based Classification Model for The Early Diagnosis of The Cancer Using a Machine Learning Approach. Machine Learning Applications in Engineering Education and Management, 1(1), 01–06. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/1




How to Cite

Kumar , K. H. ., & Srinivas, K. . (2023). A Novel PMFFC -Based Software Effort Estimation Using FMGKF-DENN Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 557–565. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3827



Research Article