"Test progress" in test monitoring and control refers to assessing the overall progress of the testing phase within the project. It involves tracking the completion of test design, execution, and defect resolution to ensure that testing aligns with project timelines and objectives.

Load testing is an example of non-functional testing. It evaluates how the system performs under specific conditions, such as heavy loads or concurrent users.

UFT, or Unified Functional Testing, is primarily used for Test Automation. It allows testers to automate the execution of functional and regression test cases, enhancing efficiency and repeatability in the testing process.

Risk analysis in functional testing aids in identifying critical areas and potential risks in the software, which helps in determining test scenarios and prioritizing testing efforts accordingly.

Selenium Grid, combined with TestNG annotations, is a robust approach for implementing parallel testing across multiple browsers. Selenium Grid allows the distribution of test execution across different nodes, each running a specific browser, while TestNG annotations facilitate easy parallel test execution. This approach improves testing efficiency and reduces the overall execution time for a test suite.

The Global Variables feature in Postman enables testers to set up variables globally, making them accessible across multiple requests within a collection. This capability is valuable for managing dynamic data, such as authentication tokens or dynamic URLs, consistently throughout the entire collection of API requests.

Test scenarios are derived from user stories and requirements, providing a basis for creating detailed test cases that cover various aspects of the software functionality.

The Response Viewer feature in Postman provides a detailed breakdown of API responses, facilitating easier analysis during testing. Testers can view the complete response, including headers and body, in a structured manner. This enhances the understanding of API behavior and aids in identifying issues or discrepancies in the response, improving the testing process.

Equivalence Partitioning reduces test redundancy by selecting a single representative test case from each partition, ensuring that each group is adequately tested.

Equivalence Partitioning divides the input data into disjoint partitions, ensuring that each partition is tested comprehensively. It simplifies testing and enhances test coverage.