Cargando…
Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach
Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553425/ https://www.ncbi.nlm.nih.gov/pubmed/36238674 http://dx.doi.org/10.1155/2022/3145956 |
_version_ | 1784806467595927552 |
---|---|
author | ul Hassan, Ch Anwar Khan, Muhammad Sufyan Irfan, Rizwana Iqbal, Jawaid Hussain, Saddam Sajid Ullah, Syed Alroobaea, Roobaea Umar, Fazlullah |
author_facet | ul Hassan, Ch Anwar Khan, Muhammad Sufyan Irfan, Rizwana Iqbal, Jawaid Hussain, Saddam Sajid Ullah, Syed Alroobaea, Roobaea Umar, Fazlullah |
author_sort | ul Hassan, Ch Anwar |
collection | PubMed |
description | Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets. |
format | Online Article Text |
id | pubmed-9553425 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95534252022-10-12 Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach ul Hassan, Ch Anwar Khan, Muhammad Sufyan Irfan, Rizwana Iqbal, Jawaid Hussain, Saddam Sajid Ullah, Syed Alroobaea, Roobaea Umar, Fazlullah Comput Intell Neurosci Research Article Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets. Hindawi 2022-10-04 /pmc/articles/PMC9553425/ /pubmed/36238674 http://dx.doi.org/10.1155/2022/3145956 Text en Copyright © 2022 Ch Anwar ul Hassan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article ul Hassan, Ch Anwar Khan, Muhammad Sufyan Irfan, Rizwana Iqbal, Jawaid Hussain, Saddam Sajid Ullah, Syed Alroobaea, Roobaea Umar, Fazlullah Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach |
title | Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach |
title_full | Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach |
title_fullStr | Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach |
title_full_unstemmed | Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach |
title_short | Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach |
title_sort | optimizing deep learning model for software cost estimation using hybrid meta-heuristic algorithmic approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553425/ https://www.ncbi.nlm.nih.gov/pubmed/36238674 http://dx.doi.org/10.1155/2022/3145956 |
work_keys_str_mv | AT ulhassanchanwar optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT khanmuhammadsufyan optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT irfanrizwana optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT iqbaljawaid optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT hussainsaddam optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT sajidullahsyed optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT alroobaearoobaea optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach AT umarfazlullah optimizingdeeplearningmodelforsoftwarecostestimationusinghybridmetaheuristicalgorithmicapproach |