The 'Link Text' locator in Selenium is used to find elements based on the visible text within hyperlinks. It is particularly useful for identifying and interacting with links containing specific text. This allows testers to locate elements without relying on the structure or other attributes, enhancing the flexibility and maintainability of Selenium scripts.

Equivalence Partitioning is used in Boundary Value Analysis to identify test cases at the edges of input domains by dividing the input into meaningful equivalence classes.

State Transition Testing involves defining conditions that trigger state transitions. These conditions, often referred to as trigger and action conditions, specify when the system should move from one state to another. Identifying and testing these conditions help ensure the correct functioning of the system as it transitions between states.

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.

The Oracle Assumption is a key principle in functional testing that helps address inconsistencies by using a reliable source (the "Oracle") to determine the expected outcome, ensuring accurate identification and correction of discrepancies during testing.

Testers create Test Cases during Exploratory Testing to guide their exploration. While the testing is unscripted, having a plan helps ensure that critical areas are covered, and findings are documented. Test Cases act as a framework for organized and effective exploration.

Stress testing assesses the system's ability to handle data inputs within specified ranges and under extreme conditions. It helps identify the system's breaking point.

The Defect Lifecycle contributes to the overall quality of the software product by providing a systematic and controlled approach to managing defects. It ensures that identified issues go through a structured process, from identification to resolution and verification. This systematic approach helps in maintaining software quality by addressing defects in an organized manner, reducing the chances of overlooking critical issues during development.

Relying on AI and Machine Learning models in functional testing introduces risks such as a lack of understanding of model decisions. Models may make complex decisions that are challenging for humans to interpret, leading to potential blind spots in testing. Testers must be cautious and supplement AI-based testing with human expertise to ensure a comprehensive and reliable testing strategy.