AI-driven Test Automation for Salesforce and System Integration

Authors

  • Srikanth Perla Charles River Laboratories Inc., USA Author

DOI:

https://doi.org/10.32628/CSEIT251112116

Keywords:

Artificial Intelligence Testing, Test Automation Framework, Self-healing Mechanisms, Predictive Analytics, System Integration Testing

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.

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References

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Published

26-01-2025

Issue

Section

Research Articles

How to Cite

AI-driven Test Automation for Salesforce and System Integration. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1120-1129. https://doi.org/10.32628/CSEIT251112116