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DOI: 10.14569/IJACSA.2022.0131045
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Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

Author 1: Vikas Chahar
Author 2: Pradeep Kumar Bhatia

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

  • Abstract and Keywords
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Abstract: In present scenario, the software companies are frequently involving software test effort estimation to allocate the resources efficiently during the software development process. Different machine learning models are developed to estimate the total effort that would be required before the software product could be delivered. These computational models are used to use the past data to estimate the efforts. In the current studies, test effort estimation for software is predicted using the Genetic algorithm and Neural Network. The attributes are selected using the Genetic algorithm and similarity measure between the attribute values has been computed using the Cosine Similarity measure. The simulation experiments were done using the PROMISE and Kaggle repository and implementation was done using the MATLAB software. The performance metrics namely, precision, recall, and accuracy are computed to evaluate against the existing techniques. The accuracy of the proposed model is 91.3% and results are improved by 8.9% in comparison to existing technique and comparison has been made for superiority to predict the test effort for software development.

Keywords: Test effort estimation; software testing; machine learning; computational intelligence; neural network

Vikas Chahar and Pradeep Kumar Bhatia, “Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131045

@article{Chahar2022,
title = {Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131045},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131045},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Vikas Chahar and Pradeep Kumar Bhatia}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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