AI-driven Test Automation for Salesforce and System Integration - Srikanth Perla (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Integrating artificial intelligence into test automation frameworks has transformed quality assurance practices in Salesforce environments and system integrations. AI-driven solutions have revolutionized testing approaches through smart test selection, risk-based analysis, and dynamic element identification capabilities. These advancements enable organizations to detect defects earlier, reduce false positives, and significantly decrease test maintenance efforts. Self-healing locators and context-aware selection mechanisms have enhanced test stability across dynamic web applications, while pattern recognition and anomaly detection capabilities proactively identify potential issues. Real-world implementations demonstrate substantial improvements in testing efficiency, reliability, and cost-effectiveness. Despite the challenges of data requirements and implementation complexity, AI-powered testing solutions have proven particularly effective in handling complex Salesforce configurations and multi-system integrations. The continuous evolution of these technologies promises enhanced predictive capabilities, improved integration support, and more sophisticated automated testing approaches, marking a significant shift in how organizations approach quality assurance in modern software development.