Software Quality Engineering: Salesforce, AI, and Test Automation

Abstract

Software quality engineering plays a critical role in ensuring that customer relationship management (CRM) applications like Salesforce are reliable, efficient, and scalable. Test automation, powered by artificial intelligence (AI), has revolutionized the way software quality is maintained, especially in complex systems such as Salesforce. Salesforce, as one of the leading CRM platforms globally, requires rigorous testing to maintain its functionality, scalability, and security across multiple modules. AI and test automation technologies such as machine learning (ML), natural language processing (NLP), and robotic process automation (RPA) are increasingly being employed to automate the testing process, reducing human error, improving test coverage, and accelerating release cycles. This research article explores the integration of AI and test automation in Salesforce, assessing their impact on software quality engineering. By reviewing current frameworks, methodologies, and industry trends, this study highlights the advantages and challenges associated with implementing AI-driven test automation for Salesforce. The research evaluates various tools and frameworks, including Salesforce's own automation features and third-party AI-powered tools like Testim.io. Key performance indicators such as defect detection, testing speed, and maintenance effort are used to analyze the effectiveness of test automation in ensuring high software quality. This paper concludes by identifying research gaps, challenges in implementation, and offering recommendations for organizations to optimize software quality using AI and automation in Salesforce environments.