Foundations and Frontiers: Bridging Core Computer Science with Machine Learning and AI Innovations.

Abstract

This study investigates the effectiveness of AI-driven solutions in overcoming the challenges of automated testing within Salesforce Lightning environments, characterized by dynamic content and frequent updates. The research aimed to evaluate how AI technologies such as machine learning, natural language processing, and computer vision can enhance testing efficiency and accuracy compared to traditional methods. Using simulated Salesforce Lightning scenarios, the study compared the performance of AI-driven and conventional testing approaches across key metrics including defect detection rates, test execution time, and test coverage. The analysis revealed that AI-driven tools significantly outperform traditional methods, offering improved adaptability to dynamic content and reducing manual script maintenance. AI techniques led to higher testing quality and efficiency, confirming their superiority in handling Salesforce Lightning’s complexities. The study concludes that integrating AI solutions into testing frameworks can substantially enhance testing processes, reduce operational costs, and improve overall quality. These findings underscore the value of adopting AI-driven testing approaches for dynamic application environments.