Hyperautomation leverages AI and ML algorithms to automate the resolution of complex issues. These technologies enable test automation to learn from historical data and adapt to new scenarios, improving the efficiency and accuracy of the automation process.
Hyperautomation uses RPA to automate repetitive, rule-based tasks, such as data entry, report generation, and data validation.
Hyperautomation uses process mining to discover, monitor, and optimize business processes. It provides valuable insights into how processes are executed, where bottlenecks occur, and how processes can be optimized to improve efficiency and reduce costs.
Hyperautomation enables seamless integration with existing systems and tools, including test management tools, defect tracking systems, and CI/CD pipelines.
Hyperautomation enables the automation of large-scale, complex processes that were previously difficult to automate. This scalability enables organizations to optimize their processes and achieve higher levels of efficiency and productivity.
Hyperautomation enables continuous improvement by constantly monitoring and optimizing processes. By identifying and addressing issues in real-time, hyperautomation enables organizations to continually improve their processes and achieve better outcomes.