Software Engineering for AI Systems: Challenges of Developing, Testing, And Maintaining Machine Learning Systems Compared to Traditional Software.
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.
Citations
IRE Journals:
Udokporo Jamachi Bernard "Software Engineering for AI Systems: Challenges of Developing, Testing, And Maintaining Machine Learning Systems Compared to Traditional Software." Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 2042-2045
IEEE:
Udokporo Jamachi Bernard
"Software Engineering for AI Systems: Challenges of Developing, Testing, And Maintaining Machine Learning Systems Compared to Traditional Software." Iconic Research And Engineering Journals, 9(3)
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