A Unified Approach to QA Automation in Salesforce Using AI, ML, and Cloud Computing

Abstract

This study investigates the integration of Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing into Quality Assurance (QA) automation within Salesforce environments. The primary aim was to address the limitations of traditional QA methods by evaluating the impact of these advanced technologies on software quality, scalability, and operational efficiency. Employing a mixed-methods design, the study utilized simulated data to assess test coverage, defect detection rates, and resource management across three QA automation approaches: traditional, AI-enhanced, and cloud-based unified systems. The analysis revealed that AI significantly improved test coverage and defect detection, ML enhanced test generation and optimization, and cloud computing facilitated scalable and efficient testing processes. The unified approach integrating AI, ML, and cloud computing demonstrated superior performance compared to traditional methods, offering a more robust solution for managing complex Salesforce environments. These findings suggest that advanced technologies can greatly enhance QA automation, leading to improved software reliability and operational efficiency. The study underscores the importance of integrating these technologies for future QA practices.