International Journal of Performance Analysis in Sport, 2025, 21(1): 10-23 doi: 10.23940/ijpe.25.01.p2.1023

Original article

A DNN Anti-Predatory Algorithm-Based Model to Enhance the Efficiency of Software Effort Estimation

Sharma Archana,*, Singh Rajpoot Dharmveer

Jaypee Institute of Information Technology, Noida, India

*Corresponding Author(s): Corresponding author. E-mail address: 19403031@mail.jiit.ac.in Corresponding author. E-mail address: 19403031@mail.jiit.ac.in

Revised:  ;  Submitted on  ;  Accepted:  ; 

Abstract

Estimating effort early in the software development life cycle is essential for proper planning. It enables better allocation of resources, time, and budget, helping to avoid project delays and cost overruns. Inaccurate estimation often leads to project failures, which is a pervasive issue nowadays for software project managers. Machine learning approaches have generally shown significant success in addressing estimation challenges across various engineering domains. This study introduces a novel method, combining a Dense neural network (DNN) with a metaheuristic adaptive anti-predatory (AP) Algorithm known as AP-DNN. This method is effectively used to address the challenges of estimating software effort. The adaptive anti-predatory (AP) algorithm is utilized to optimize the parameters of the DNN, improving its capacity to explore the parameter space thoroughly and avoid getting trapped in local optima. The proposed anti-predatory dense neural network (AP-DNN) method was tested on several benchmark SEE datasets, and its performance was compared with various contemporary algorithms from existing literature. The comparative results indicate that AP-DNN outperforms other methods across most evaluation metrics and datasets.

Keywords： estimation of software effort ; anti-predatory NIA ; memetic ; optimization ; levy flight ; DNN

PDF (649KB) Metadata Related articles Export EndNote| Ris| Bibtex

Cite this article

Sharma Archana, Singh Rajpoot Dharmveer. A DNN Anti-Predatory Algorithm-Based Model to Enhance the Efficiency of Software Effort Estimation. International Journal of Performance Analysis in Sport, 2025, 21(1): 10-23 doi:10.23940/ijpe.25.01.p2.1023

Reference

Rahman M., Goncalves T., and Sarwar H., 2023.

Review of existing datasets used for software effort estimation

International Journal of Advanced Computer Science and Applications.

Gautam S.S., and Singh V., 2018.

The state‐of‐the‐art in software development effort estimation

Journal of Software: Evolution and Process, 30(12), e1983.

Yang Y., Xia X., Lo D., Bi T., Grundy J., and Yang X., 2022.

Predictive models in software engineering: challenges and opportunities

ACM Transactions on Software Engineering and Methodology (TOSEM), 31(3), pp. 1-72.

Shukla S., and Kumar S., 2023.

Know-UCP: locally weighted linear regression based approach for UCP estimation

Applied Intelligence, 53(11), pp. 13488-13505.

Ramessur M.A., and Nagowah S.D., 2021.

A predictive model to estimate effort in a sprint using machine learning techniques

International Journal of Information Technology, 13(3), pp. 1101-1110.

Pospieszny P., Czarnacka-Chrobot B., and Kobylinski A., 2018.

An effective approach for software project effort and duration estimation with machine learning algorithms

Journal of Systems and Software, 137, pp. 184-196.

Kumar S., Krishna B.A., and Satsangi P.S., 1994.

Fuzzy systems and neural networks in software engineering project management

Applied Intelligence, 4, pp. 31-52.

Sharma A., and Rajpoot D.S., 2024.

A memetic approach for optimizing software effort estimation using anti-predatory NIA.

International Journal of Information Technology, 16(2), pp. 641-649.

Karimi A., and Gandomani T.J., 2021.

Software development effort estimation modeling using a combination of fuzzy-neural network and differential evolution algorithm

International Journal of Electrical and Computer Engineering, 11(1), 707.

Wen J., Li S., Lin Z., Hu Y., and Huang C., 2012.

Systematic literature review of machine learning based software development effort estimation models

Information and Software Technology, 54(1), pp. 41-59.

Mahmood Y., Kama N., Azmi A., Khan A.S., and Ali M., 2022.

Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

Software: Practice and Experience, 52(1), pp. 39-65.

Lopez-Martin C., 2011.

Applying a general regression neural network for predicting development effort of short-scale programs

Neural Computing and Applications, 20, pp. 389-401.

Nassif A.B., Ho D., and Capretz L.F., 2013.

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Journal of Systems and Software, 86(1), pp. 144-160.

López-Martín C., 2015.

Predictive accuracy comparison between neural networks and statistical regression for development effort of software projects

Applied Soft Computing, 27, pp. 434-449.

Nassif A.B., Capretz L.F., and Ho D., 2012. Software effort estimation in the early stages of the software life cycle using a cascade correlation neural network model. In 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 589-594.

Hammouri A.I., Braik M.S., Al-Betar M.A., and Awadallah M.A., 2020.

ISA: a hybridization between iterated local search and simulated annealing for multiple-runway aircraft landing problem

Neural Computing and Applications, 32, pp. 11745-11765.

Al-Betar M.A., Alyasseri Z.A.A., Awadallah M.A., and Abu Doush I., 2021.

Coronavirus herd immunity optimizer (CHIO)

Neural Computing and Applications, 33, pp. 5011-5042.

Jiang J., Chen Z., Wang Y., Peng T., Zhu S., and Shi L., 2019.

Parameter estimation for PMSM based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm

International Journal of Computers Communications & Control, 14(6), pp. 615-632.

Shen X., Zheng Y., and Zhang R., 2020.

A hybrid forecasting model for the velocity of hybrid robotic fish based on back-propagation neural network with genetic algorithm optimization

IEEE Access, 8, pp. 111731-111741.

Sun W., and Huang C., 2020.

A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network

Journal of Cleaner Production, 243, 118671.

Ardiansyah A., Ferdiana R., and Permanasari A.E., 2022.

MUCPSO: A modified chaotic particle swarm optimization with uniform initialization for optimizing software effort estimation

Applied Sciences, 12(3), 1081.

Rhmann W., Pandey B., and Ansari G.A., 2022.

Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithms

Innovations in Systems and Software Engineering, 18(2), pp. 309-319.

Resmi V., Vijayalakshmi S., and Chandrabose R.S., 2019.

An effective software project effort estimation system using optimal firefly algorithm

Cluster Computing, 22(Suppl 5), pp. 11329-11338.

Ullah A., Wang B., Sheng J., Long J., Asim M., and Sun Z., 2020.

Optimization of software cost estimation model based on biogeography-based optimization algorithm

Intelligent Decision Technologies, 14(4), pp. 441-448.

Khan M.S., Jabeen F., Ghouzali S., Rehman Z., Naz S., and Abdul W., 2021.

Metaheuristic algorithms in optimizing deep neural network model for software effort estimation

IEEE Access, 9, pp. 60309-60327.

Arora M., Verma S., Kavita, Wozniak M., Shafi J., and Ijaz M.F., 2022.

An efficient ANFIS-EEBAT approach to estimate effort of scrum projects

Scientific Reports, 12(1), 7974.

Sharma A., and Rajpoot D.S., 2022. A frog based nature inspired algorithm for solving optimization problem. In 2022 1st International Conference on Informatics (ICI), pp. 1-6.

Mantegna R.N., 1994.

Fast, accurate algorithm for numerical simulation of levy stable stochastic processes

Physical Review E, 49(5), 4677.

Chawla M., and Duhan M., 2018.

Levy flights in metaheuristics optimization algorithms-a review

Applied Artificial Intelligence, 32(9-10), pp. 802-821.

Sheta A.F., Kassaymeh S., and Rine D., 2014.

Estimating the number of test workers necessary for a software testing process using artificial neural networks

International Journal of Advanced Computer Science and Applications, 5(7).

Ding S., Su C., and Yu J., 2011.

An optimizing BP neural network algorithm based on genetic algorithm

Artificial Intelligence Review, 36, pp. 153-162.

Wang Y., Wang L., Chang Q., and Yang C., 2020. Effects of direct input-output connections on multilayer perceptron neural networks for time series prediction: L.

Wang

Soft Computing, 24, pp. 4729-4738.

Albrecht A.J., and Gaffney J.E., 1983.

Software function, source lines of code, and development effort prediction: a software science validation

IEEE Transactions on Software Engineering, (6), pp. 639-648.

Qi F., Jing X.Y., Zhu X., Xie X., Xu B., and Ying S., 2017.

Software effort estimation based on open source projects: case study of github

Information and Software Technology, 92, pp. 145-157.

Desharnais J.M., 1989.

Analyse statistique de la productivitie des projets informatique a partie de la technique des point des function

Masters Thesis, University of Montreal.

Kitchenham B., Pfleeger S.L., McColl B., and Eagan S., 2002.

An empirical study of maintenance and development estimation accuracy

Journal of Systems and Software, 64(1), pp. 57-77.

Bland M., 2015. An Introduction to Medical Statistics. Oxford university press.

Kassaymeh S., Abdullah S., Al-Laham M., Alweshah M., Al-Betar M.A., and Othman Z., 2021.

Salp swarm optimizer for modeling software reliability prediction problems

Neural Processing Letters, 53, pp. 4451-4487.

AG P.V., K, A.K., and Varadarajan V., 2021.

Estimating software development efforts using a random forest-based stacked ensemble approach

Electronics, 10(10), 1195.

Najm A., Zakrani A., and Marzak A., 2022.

Cluster-based fuzzy regression trees for software cost prediction

Indonesian Journal of Electrical Engineering and Computer Science, 27(2), pp. 1138-1150.

/

〈 〉

Copyright © 2016-2020 International Journal of Performability Engineering, All Rights Reserved.
Maintained by Beijing Magtech Co. Ltd