AI-augmented, software-testing tools provide capabilities for advanced, self-optimizing and adaptive automated testing through the use of AI technologies, such as machine learning (ML), self-healing heuristics or computer vision. They assist humans in their testing efforts and reduce the need for human intervention in the following areas: Test case and test data generation Test suite optimization and coverage detection Test efficacy and robustness Test analysis and defect prediction Test effort estimation and decision making AI-augmented testing tools streamline, accelerate and improve the test workflow, as models can be retrained based on the data collected from the activities they perform — thereby enhancing human productivity.
Task mining is a technique by which enterprises can infer meaningful information by scraping desktop-level event data. This data may be from individual users or a cohort of individuals (e.g., in a call center) and takes the form of screen recordings, keystrokes, mouse clicks and data entries. Additional mining capabilities interpret the data by applying natural language processing and optical character recognition to correlate data in different ways. Task mining helps an enterprise identify inefficiencies and automation or AI potential, improve task execution and enhance the employee experience.