Machine Learning-Based Software Development Challenges Focusing on using Best Practices of Software Engineering Standards

The AI era established significant challenges for software developers, especially those working on Machine Learning (ML)-based software. This article presents the findings of a systematic literature review (SLR) focused on identifying software engineering practices for ML-based software development. We identified 16 primary studies highlighting the challenges scientists face in lacking software engineering training when developing ML-based software. The results emphasize the importance of documentation, standardized processes, and skills acquisition to overcome these challenges effectively in the AI era.