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

Rashid, Junaid; Kanwal, Sumera; Wasif Nisar, Muhammad; Kim, Jungeun; Hussain, Amir


Junaid Rashid

Sumera Kanwal

Muhammad Wasif Nisar

Jungeun Kim


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.

Journal Article Type Article
Acceptance Date Feb 21, 2022
Online Publication Date Jun 15, 2022
Publication Date 2023
Deposit Date Jul 15, 2022
Publicly Available Date Jul 15, 2022
Journal Computer Systems Science and Engineering
Print ISSN 0267-6192
Publisher Tech Science Press
Peer Reviewed Peer Reviewed
Volume 44
Issue 2
Pages 1309-1324
Keywords Software cost estimation; neural network; backpropagation; forward neural networks; software effort estimation; artificial neural network
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