Cloud-Enabled Intelligent Testing of Robotic Process Automation Systems Using Dynamic BERT-LSTM and FCNN Fusion Models

Robotic Process Automation is transforming business operations through automation of rule-based processes in finance, healthcare, logistics and customer care. As RPA systems grow in complexity, intelligent, scalable and context-aware testing solutions become necessity of the hour. Static embedding-based, keyword frequency-based or manual rule-based testing approaches all falter when it comes to semantic understanding and dynamic test environments, resulting in inaccurate priority assignment, poor duplicate detection and poor scalability. Cloud-based intelligent testing paradigm based on the integration of Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory networks and Fully Connected Neural Networks is proposed. Methodology proposed consists of preprocessing semi-structured RPA test-case datasets, retrieving contextual semantics through BERT, temporal pattern modeling through LSTM and FCNN for final classification and anomaly detection. This combined system is run on a scalable cloud platform (e.g., AWS, Azure) to facilitate enterprise-scale continuous integration and parallel testing. Performance was measured on a Kaggle test-case dataset in comparison to standard methods. Findings show higher performance on several testing utility metrics of 95 percent improvement in test coverage, 85 percent decrease in test running time, 82 percent redundancy reduction, 90 percent gain in automation coverage and 98 percent defect detection effectiveness. These results validate that BERT-LSTM-FCNN fusion model not only improves RPA test quality and operational reliability but also greatly lowers human effort to maintain large-scale automation systems.