Integrating AI and Machine Learning in Quality Assurance for Automation Engineering

Parameshwar Reddy Kothamali; Vinod Kumar Karne; Sai Surya Mounika Dandyala

doi:10.36676/jrps.v15.i3.1445

Authors

Parameshwar Reddy Kothamali QA Automation Engineer at Northeastern University Masters in computer science Parameshwar.kothamali@gmail.com Location: Allentown, PA, USA, 18031
Vinod Kumar Karne QA Automation Engineer at Northeastern University Masters in computer science
Sai Surya Mounika Dandyala Data Engineer Masters in Informatics

DOI:

https://doi.org/10.36676/jrps.v15.i3.1445

Keywords:

AI, Machine Learning, Quality Assurance, Automation Engineering, Ethical Model Development, Artificial Intelligence

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.

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