Machine Learning Software Component Quality: Current Status, Challenges, and Future Directions
Machine Learning Software Component Quality: Current Status, Challenges, and Future Directions |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-9 |
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| Year of Publication : 2025 | ||
| Author : Mohamed Abdullahi Ali, Ng Keng Yap, Hazura Zulzalil, Novia Indriaty Admodisastro, Amin Arab Najafabadi, and Jamal Abdullahi Nuh | ||
| DOI : 10.14445/22315381/IJETT-V73I9P121 | ||
How to Cite?
Mohamed Abdullahi Ali, Ng Keng Yap, Hazura Zulzalil, Novia Indriaty Admodisastro, Amin Arab Najafabadi, and Jamal Abdullahi Nuh,"Machine Learning Software Component Quality: Current Status, Challenges, and Future Directions", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.229-235, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P121
Abstract
Traditional software is developed by writing code. As big data analytics and Artificial Intelligence (AI) technologies advanced, many Machine Learning (ML) based software and applications became widely accepted and used in people's daily lives. Such software is developed from trained data, and this behaviour differs from traditional software development. At this moment, building ML software consumes time and effort and requires knowledge of statistics and ML model training. To overcome this, several recent studies proposed building ML software through an ML software component-based method. Consequently, this approach will increase reusability and reduce development effort in ML software. Presently, there is a high demand for creating a quality model for ML software components, as traditional software component quality models cannot support specific quality aspects of ML software components. For instance, ML software component behaviour differs from conventional software components because they are built from trained data rather than being written in programming code. Thus, the ML software component quality model became essential due to their unique nature. This study offers an outline and insights for researchers to better understand the present condition of machine learning software component quality models, related challenges, future directions, and the advantages of adopting a component-based software development approach for machine learning software (i.e., machine learning software components).
Keywords
ML software, Quality model, ML software component.
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