Integrating AI and Machine Learning in Quality Assurance for Automation Engineering

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Quality assurance for automation engineering integrates machine learning, enhancing efficiency and effectiveness across industries.
Key concerns in the field include model robustness and bias, which influence the reliability of AI systems in QA applications.
The assessment framework reviews existing methodologies to ensure ethical standards are met in machine learning deployment within QA processes. As technologies evolve, the need for continual adaptation and rigorous testing frameworks grows, indicating future challenges in AI governance.

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Reviews challenges and solutions in assessing ML programs within Automation Engineering, highlighting implications for quality assurance practices.

Kothamali et al. (2024) studied this question.

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Reviews challenges and solutions in assessing ML programs within Automation Engineering, highlighting implications for quality assurance practices.