Integrating AI and Machine Learning in Quality Assurance for Automation Engineering

Abstract

The integration of AI and Machine Learning (ML) into Quality Assurance (QA) for Automation Engineering represents a transformative shift, leveraging data-driven decision-making and automation across industries. Despite their promising benefits, the reliability, fairness, and generalizability of ML models remain significant concerns. This paper addresses these challenges by exploring the complexities inherent in assessing and validating ML programs. Firstly, it identifies obstacles such as bias, model robustness, and adaptability to new data, emphasizing the necessity for rigorous testing frameworks. Secondly, the paper reviews existing methodologies and solutions proposed in scholarly literature to enhance the assessment of ML programs, ensuring they perform as intended and meet ethical standards.This comprehensive manual serves as a guiding resource for professionals and scholars navigating the dynamic convergence of QA and ML. It underscores the need for continual learning and adaptation in an era where AI's potential is matched by the responsibilities of ethical and resilient model development. By offering profound insights and methodologies, the paper equips QA practitioners and AI enthusiasts alike to navigate the intricate terrain of quality assurance in the era of machine learning effectively.