AI-driven Test Automation for Salesforce and System Integration

Srikanth Perla

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 1120-1129

Abstract: 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.

Keywords: Artificial Intelligence Testing; Test Automation Framework; Self-healing Mechanisms; Predictive Analytics; System Integration Testing (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112116
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:772

DOI: 10.32628/CSEIT251112116

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