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Software Engineering for AI Systems: Challenges of Developing, Testing, And Maintaining Machine Learning Systems Compared to Traditional Software.
Subject area: Science,Engineering and Technology · Area of research: Software Engineering
Abstract
The emergence of Artificial Intelligence (AI) and Machine Learning (ML), dates back between 1940?s and 1950?s. In recent years, there has been a surge in the quest/demand for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software developing and testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software developers and testers face in maintaining and dealing with Artificial Intelligence (AI) / Machine Learning (ML) systems/applications compared to traditional software. The challenges in developing, testing, and maintaining machine learning (ML) systems compared to traditional software engineering, are due to the data-driven and adaptive nature of ML. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.
References
[1] Fumihiro Kumeno et’al (2019). Software Engineering Challenges for Machine Learning Applications: A Literature Review. Sage Journals - First Published online, November 1, 2019. Vol. 13, Issue 4. https://doi.org/10.3233/IDT-190160.
[2] Gorken Giray (2021). A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges. ScienceDirect Elsevier Journal of Systems and Software. Vol, 180. October 2021, 111031. https://doi.org/10.1016/j.jss.2021.111031.
[3] Kishore Sugali, Chris Sprunger and Venkata N Inukollu (2021). Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.1, January 2021 DOI: 10.5121/ijaia.2021.12107 101
[4] Lev Craig (2023). Compare Machine Learning vs Software Engineering. TechTarget - O’Reilly Media. Published 16 August, 2023.
[5] Robbie Allen (2020). How Machine Learning Differs from Traditional Software. Medium Automated Consulting Group. January 21, 2020.
[6] Silverio Martinez-Fernandez et’al (2022). Software Engineering for AI-based Systems: A Survey. ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 31, Issue 2. Article No.: 37e. Pages 1-59, https://doi.org/10.1145/3487043. 01 April, 2022.
How to cite this paper
@article{1710938,
author = {Udokporo Jamachi Bernard},
title = {Software Engineering for AI Systems: Challenges of Developing, Testing, And Maintaining Machine Learning Systems Compared to Traditional Software.},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {2042-2045},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710938.pdf},
abstract = {The emergence of Artificial Intelligence (AI) and Machine Learning (ML), dates back between 1940?s and 1950?s. In recent years, there has been a surge in the quest/demand for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software developing and testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software developers and testers face in maintaining and dealing with Artificial Intelligence (AI) / Machine Learning (ML) systems/applications compared to traditional software. The challenges in developing, testing, and maintaining machine learning (ML) systems compared to traditional software engineering, are due to the data-driven and adaptive nature of ML. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.},
month = {September},
}