Manual Testing Techniques:

1.common manual testing techniques;

Manual testing;Manual testing refers to the process of testing software applications by human testers,who carefully execute test cases without using testing tools.In manual testing,testers simulate end user behavior to ensure that the software functions correctly and meets the specified requirements.
They are many variations of manual testing with different purpose and software types,Here are some of the most common type:-Block box testing,white box testing,unit testing,integration testing,system testing,usability testing,user acceptance testing.
2.Boundary value analysis:
Boundary value analysis is a popular technique for black box testing.It is used to identify defects and errors in software by testing input values on the boundaries of the allowable ranges.
The goal of BVA is to find any issues which may arise due to incorrect assumptions about the system behavior.
Testing these boundary values ensures that the software functions correctly.
Every partition for boundary analysis has its minimum and maximum values.Lets look at the following points to understand BVA in software testing in brief.For every variable,testers check the following.
Nominal value
Minimum value
Above minimum value
Below the minimum value
Maximum value
Boundary Value for an invalid partition is called invalid
boundary value
Boundary value for valid partition is called boundary

value.
3.Decision table testing:
Decision table testing is a black box testing design

technique(behavioral or behavior based technique),used
where different combinations of test input conditions
result in different outcomes.When a system has complex

business rules,then the decision table testing techniques
helps in identifying the correct test cases.
4.The future of the manual testing in the age of AI:
As Artificial Intelligence(AI)continues to revolutionize

industries,software testing is undergoing a significant

transformation.Traditional manual and automation testing
methods,designed for deterministic systems with fixed
outputs,are evolving to meet the dynamic and data driven
nature of AI applications.Here's how testing practices
are adapting to ensure AI systems are
accurate,reliable,and fair.