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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Atif Ikram1,2,*, Masita Abdul Jalil1, Amir Bin Ngah1, Saqib Raza6, Ahmad Salman Khan3, Yasir Mahmood3,4, Nazri Kama4, Azri Azmi4, Assad Alzayed5

1 Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, Kuala Nerus, Malaysia
2 Department of Computer Science & Information Technology, Faculty of Information Technology, The University of Lahore, Lahore, 54000, Pakistan
3 Department of Software Engineering, Faculty of Information Technology, The University of Lahore, Lahore, 54000, Pakistan
4 Advanced Informatics Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia
5 Computer Science and Information Systems Department, College of Business Studies Public Authority for Applied Education and Training, (PAAET), Adailiyah, Kuwait
6 Department of Computer Science, Sharif College of Engineering & Technology, Lahore, 54000, Pakistan

* Corresponding Author: Atif Ikram. Email:

Computers, Materials & Continua 2023, 74(1), 1871-1886.


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.


Cite This Article

A. Ikram, M. A. Jalil, A. B. Ngah, S. Raza, A. S. Khan et al., "Project assessment in offshore software maintenance outsourcing using deep extreme learning machines," Computers, Materials & Continua, vol. 74, no.1, pp. 1871–1886, 2023.

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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