In the Test Planning phase of the Software Testing Life Cycle (STLC), all the planning for the testing activities is done. This includes selecting and setting up the necessary testing tools.

Beta Testing is one of the final stages in the software testing process where the software is released to a limited number of end-users under real-world conditions. The primary purpose is to get feedback from users, ensuring that there are no major failures when it's released to the public and to understand any enhancements users might like to see.

Parallel execution refers to running multiple test cases or test suites concurrently, usually distributed across various machines or cores. This drastically reduces the total testing time, especially in large-scale projects, as it eliminates the need to wait for each test to finish.

The Agile model is well-suited for projects where rapid changes are expected, as it emphasizes adaptability and customer feedback. Iterative development allows the team to accommodate changes in requirements and design more flexibly than traditional models like Waterfall or V-Model.

Configuration Management (CM) in software development primarily serves to systematically manage, organize, and control the changes and versions in software documents, code, and other entities during the development process. It ensures consistency and traceability across the software lifecycle.

Having domain knowledge allows testers to better anticipate potential problem areas within the software. By understanding the nuances and intricacies of the domain, testers can "guess" or predict where errors might occur, making their testing efforts more focused and effective in uncovering defects.

Performance Testing encompasses a range of tests (including Load, Stress, Scalability, and Volume Testing) to ensure that the software behaves well under expected loads, extreme conditions, and varying workloads. The objective is to deliver a seamless user experience, irrespective of the conditions or demands placed on the software.

"Defect Removal Efficiency" (DRE) metric measures the effectiveness of the testing process. It's calculated as the ratio of defects found before release (by the testing team) to the total defects (found both before and after release). A higher DRE indicates a more effective testing process.

The "Post-release Defect Density" metric gives insights into the number of defects found in the software after its release. A high density suggests that the testing process might have missed issues, while a low density implies effective testing during the development phase.

Automated testing primarily ensures repeatability and consistency in testing scenarios. While speeding up the process is an advantage, the main objective is to execute the same test cases multiple times without human error, ensuring the software behaves consistently.