Test Analysis involves comparing actual test results with expected outcomes to identify any discrepancies. This process is crucial for understanding the effectiveness of test cases and taking corrective actions to ensure the software meets the specified requirements.

Pairwise testing offers simplicity in test case creation by focusing on specific pairs of input parameters. This approach provides adequate coverage of test scenarios without the exhaustive nature of testing every possible combination. The advantage lies in achieving effective coverage while minimizing the number of test cases, thereby reducing execution time and maintaining a balance between thorough testing and efficiency.

Non-functional testing, including performance testing, verifies how well the system performs under different conditions. Functional testing, on the other hand, focuses on what the system is intended to do.

Agile methodologies stress Incremental Testing as a key aspect of test monitoring and control. Incremental Testing involves testing individual components and systems incrementally, allowing teams to adapt to changing requirements and ensure the continuous delivery of a functional product.

Test automation in functional testing offers increased test coverage, allowing the execution of a large number of test cases across different scenarios. It also provides faster test execution, improved repeatability of test cases, and early detection of defects, contributing to the overall effectiveness and efficiency of the testing process.

TestComplete's Keyword-Driven Testing feature enables the simulation of user interactions with the application by defining keywords that represent specific actions or operations. This approach enhances test automation flexibility and readability, making it easier to design and maintain automated tests while promoting collaboration among team members with varying technical skills.

Skipping test closure activities poses the risk of missing valuable insights for process improvement. Lessons learned may be overlooked, leading to recurring issues in future projects. On the other hand, the benefits include a shorter project timeline and immediate focus on deployment tasks. However, the risks of incomplete documentation and missed process improvements should be carefully weighed against the potential benefits.

Performance analysis in functional testing involves assessing how well the system meets performance criteria. Identifying potential performance bottlenecks is crucial to ensure that the system can handle the expected load without degradation in performance, ensuring optimal functionality under normal operating conditions.

AI-driven analytics in functional testing can improve defect prediction and prioritization by identifying patterns in historical test data. By analyzing trends and correlations, AI algorithms can predict potential defects and prioritize them based on historical patterns. This helps testing teams focus their efforts on critical areas, leading to more efficient defect management and improved overall software quality.

Ensuring correct calculation of total booking cost is crucial to guarantee accurate financial transactions. Testing the integration with payment gateways ensures seamless transactions for users.