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Project assessment in offshore software maintenance outsourcing using deep extreme learning machines

Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality...

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Main Authors: Ikram, Atif, Abdul Jalil, Masita, Ngah, Amir, Raza, Saqib, Khan, Ahmad Salman, Mahmood, Yasir, Kama, Nazri, Azmi, Azri, Alzayed, Assad
Format: Article
Language:English
Published: Tech Science Press 2023
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/106325/1/MohdNazriKama2023_ProjectAssessmentinOffshoreSoftwareMaintenance.pdf
http://eprints.utm.my/106325/
http://dx.doi.org/10.32604/cmc.2023.030818
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spelling my.utm.1063252024-06-29T05:59:20Z http://eprints.utm.my/106325/ Project assessment in offshore software maintenance outsourcing using deep extreme learning machines Ikram, Atif Abdul Jalil, Masita Ngah, Amir Raza, Saqib Khan, Ahmad Salman Mahmood, Yasir Kama, Nazri Azmi, Azri Alzayed, Assad T Technology (General) Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to OSMO vendors, having offices in developing countries while providing services to developed countries. In the current study, Extreme Learning Machine’s (ELM’s) variant called Deep Extreme Learning Machines (DELMs) is used. A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model. The proposed DELM’s based model evaluations achieved 90.017% training accuracy having a value with 1.412 × 10–3 Root Mean Square Error (RMSE) and 85.772% testing accuracy with 1.569 × 10-3 RMSE with five DELMs hidden layers. The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies. The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment. Tech Science Press 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/106325/1/MohdNazriKama2023_ProjectAssessmentinOffshoreSoftwareMaintenance.pdf Ikram, Atif and Abdul Jalil, Masita and Ngah, Amir and Raza, Saqib and Khan, Ahmad Salman and Mahmood, Yasir and Kama, Nazri and Azmi, Azri and Alzayed, Assad (2023) Project assessment in offshore software maintenance outsourcing using deep extreme learning machines. Computers, Materials and Continua, 74 (1). pp. 1871-1886. ISSN 1546-2218 http://dx.doi.org/10.32604/cmc.2023.030818 DOI : 10.32604/cmc.2023.030818
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Ikram, Atif
Abdul Jalil, Masita
Ngah, Amir
Raza, Saqib
Khan, Ahmad Salman
Mahmood, Yasir
Kama, Nazri
Azmi, Azri
Alzayed, Assad
Project assessment in offshore software maintenance outsourcing using deep extreme learning machines
description Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to OSMO vendors, having offices in developing countries while providing services to developed countries. In the current study, Extreme Learning Machine’s (ELM’s) variant called Deep Extreme Learning Machines (DELMs) is used. A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model. The proposed DELM’s based model evaluations achieved 90.017% training accuracy having a value with 1.412 × 10–3 Root Mean Square Error (RMSE) and 85.772% testing accuracy with 1.569 × 10-3 RMSE with five DELMs hidden layers. The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies. The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.
format Article
author Ikram, Atif
Abdul Jalil, Masita
Ngah, Amir
Raza, Saqib
Khan, Ahmad Salman
Mahmood, Yasir
Kama, Nazri
Azmi, Azri
Alzayed, Assad
author_facet Ikram, Atif
Abdul Jalil, Masita
Ngah, Amir
Raza, Saqib
Khan, Ahmad Salman
Mahmood, Yasir
Kama, Nazri
Azmi, Azri
Alzayed, Assad
author_sort Ikram, Atif
title Project assessment in offshore software maintenance outsourcing using deep extreme learning machines
title_short Project assessment in offshore software maintenance outsourcing using deep extreme learning machines
title_full Project assessment in offshore software maintenance outsourcing using deep extreme learning machines
title_fullStr Project assessment in offshore software maintenance outsourcing using deep extreme learning machines
title_full_unstemmed Project assessment in offshore software maintenance outsourcing using deep extreme learning machines
title_sort project assessment in offshore software maintenance outsourcing using deep extreme learning machines
publisher Tech Science Press
publishDate 2023
url http://eprints.utm.my/106325/1/MohdNazriKama2023_ProjectAssessmentinOffshoreSoftwareMaintenance.pdf
http://eprints.utm.my/106325/
http://dx.doi.org/10.32604/cmc.2023.030818
_version_ 1803334992140435456
score 13.211869

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