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An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

Junaid Rashid1, Sumera Kanwal2, Muhammad Wasif Nisar2, Jungeun Kim1,*, Amir Hussain3

1 Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, Korea
2 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, 47040, Pakistan
3 Centre of AI and Data Science, Edinburgh Napier University, Edinburgh, EH11 4DY, UK

* Corresponding Author: Jungeun Kim. Email: email

Computer Systems Science and Engineering 2023, 44(2), 1309-1324. https://doi.org/10.32604/csse.2023.026018

Abstract

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is challenging. Thus, accurate estimation of software development efforts is always a concern for many companies. In this paper, we proposed a novel software development effort estimation model based both on constructive cost model II (COCOMO II) and the artificial neural network (ANN). An artificial neural network enhances the COCOMO model, and the value of the baseline effort constant A is calibrated to use it in the proposed model equation. Three state-of-the-art publicly available datasets are used for experiments. The backpropagation feedforward procedure used a training set by iteratively processing and training a neural network. The proposed model is tested on the test set. The estimated effort is compared with the actual effort value. Experimental results show that the effort estimated by the proposed model is very close to the real effort, thus enhanced the reliability and improving the software effort estimation accuracy.

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Cite This Article

J. Rashid, S. Kanwal, M. Wasif Nisar, J. Kim and A. Hussain, "An artificial neural network-based model for effective software development effort estimation," Computer Systems Science and Engineering, vol. 44, no.2, pp. 1309–1324, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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