AI and Machine Learning can automate various aspects of functional testing, including test data generation, predictive analysis for defect prevention, and dynamic test case prioritization. However, they do not involve manual test script creation, as the goal is to automate and enhance testing processes.

Informing the development team about the issue is crucial for fixing the problem. Creating a comprehensive defect report provides detailed information about the issue, aiding developers in understanding and addressing it. Modifying the test plan to exclude the problematic use case is not a solution; instead, the goal should be to rectify the issue so that comprehensive testing can continue. Notifying the project manager and halting testing temporarily may be excessive unless the issue severely impacts the entire testing process.

The primary objective of model-based testing is to automatically generate test cases based on the model of the system. This approach streamlines the testing process, reduces manual effort, and ensures comprehensive test coverage, especially in complex systems.

Deployment frequency is a crucial metric in CI/CD, measuring how often changes are successfully deployed into production. A high deployment frequency indicates a streamlined and efficient CI/CD pipeline, enabling rapid delivery of new features and updates. It reflects the ability of a development team to quickly and reliably push changes into production, which is a key goal in adopting CI/CD practices.

In Agile and DevOps, fostering effective collaboration involves forming cross-functional teams where development, testing, and operations work together throughout the development lifecycle. This contrasts with siloed work responsibilities. Continuous integration and continuous testing practices further enhance collaboration by ensuring that changes are integrated and tested frequently, facilitating faster feedback loops and smoother collaboration.

Test execution tools play a crucial role in test reporting and analysis by capturing detailed test results. This enables teams to analyze test outcomes, identify defects, and make informed decisions about the software's quality. Automated reporting enhances visibility into testing progress and helps teams prioritize areas that require attention during the development lifecycle.

Risk management benefits the testing process by improving test planning. It helps in identifying potential obstacles and developing strategies to mitigate them, reducing the likelihood of project delays. Effective risk management also enhances communication among team members, fostering a collaborative environment focused on delivering a high-quality product.

Risk evaluation is the process of assessing identified risks in testing based on their likelihood and impact. This step is crucial for prioritizing and addressing the most critical risks to ensure effective risk management throughout the testing process.

The primary purpose of test automation in functional testing is to accelerate repetitive tasks, enabling faster and more efficient execution of test cases. Automation can handle repetitive and time-consuming scenarios, allowing human testers to focus on more complex and exploratory aspects of testing.

Functional testing assesses whether the system meets specified requirements, while non-functional testing evaluates characteristics like performance and reliability.