What is Manual Testing ?
Manual testing is a software quality assurance process where tester manually execute the test cases to find the bugs and defects in the software without using any automated tools.
Manual Testing Types -
1. White Box Testing
White box testing is a software testing technique that verifies an application's internal structure, design, and code. White box testing which focuses on a program's external functions, white box testing requires the tester to have programming knowledge and access to the source code. This approach is also known as clear box, glass box, or structural testing.
2. Black Box Testing
Black box testing is a software testing method that evaluates an application's functionality without needing knowledge of its internal coding or structure. Testers focus on inputs and outputs, simulating how an end-user would interact with the software to verify that it meets requirements and functions as expected.
3. Grey Box Testing
Grey-box testing is a software testing method where testers have partial knowledge of the internal structure of the software, combining elements of both black-box testing (external view) and white-box testing (internal view). Testers use their limited understanding of the internal components to design more effective tests that verify the functionality and expose potential defects, focusing on areas like data flow, integration issues, and security vulnerabilities that might be missed with purely black-box or white-box approaches.
Testing Techniques -
Software testing techniques are methods used to design and execute tests to evaluate software applications. The following are common testing techniques:
Functional testing - Tests the functional requirements of the software to ensure they are met.
Non-functional testing - Tests non-functional requirements such as performance, security, and usability.
Unit testing - Tests individual units or components of the software to ensure they are functioning as intended.
Integration testing - Tests the integration of different components of the software to ensure they work together as a system.
System testing - Tests the complete software system to ensure it meets the specified requirements.
Acceptance testing - Tests the software to ensure it meets the customer's or end-user's expectations.
Regression testing - Tests the software after changes or modifications have been made to ensure the changes have not introduced new defects.
Performance testing - Tests the software to determine its performance characteristics such as speed, scalability, and stability.
Security testing - Tests the software to identify vulnerabilities and ensure it meets security requirements.
Exploratory testing - A type of testing where the tester actively explores the software to find defects, without following a specific test plan.
Boundary value testing - Tests the software at the boundaries of input values to identify any defects.
Usability testing - Tests the software to evaluate its user-friendliness and ease of use.
User acceptance testing (UAT) - Tests the software to determine if it meets the end-user's needs and expectations.
Boundary Value Analysis -
Boundary value analysis involves software testing data near the specified limits or boundaries in the input specification. The aim is to test the input data’s extreme values and boundary conditions. This includes:
• Minimum and maximum values – Test inputs at or near the defined minimum and maximum limits.
• Default values – Test default values when no input is provided.
• Edge values – Test inputs right on the boundaries or edges.
• Invalid values – Test invalid inputs to check for proper error handling.
Example: assume that the valid age values are between 20 and 50.
- The minimum boundary value is 20
- The maximum boundary value is 50
- Take: 19, 20, 21, 49, 50, 51
- Valid inputs: 20, 21, 49, 50
- Invalid inputs: 19, 51
So, the test cases will look like:
•Case 1: Enter number 19 → Invalid
•Case 2: Enter number 20 → Valid
•Case 3: Enter number 50 → Valid
•Case 4: Enter number 51 → Invalid
Equivalence Partitioning -
It is a type of black-box testing that can be applied to all levels of software testing . In this technique, input data are divided into the equivalent partitions that can be used to derive test cases-
In this input data are divided into different equivalence data classes.
It is applied when there is a range of input values.
Example: the valid usernames must be from 5 to 20 text-only characters.
So, test cases will look like:
•Case 1: Enter within 5 – 20 text characters → Pass
•Case 2: Input <3 characters → Display error message “Username must be from 5 to 20 characters”
•Case 3: Enter >20 characters → Display error message “Username must be from 5 to 20 characters”
•Case 4: Leave input blank or enter non-text characters → Display error message “Invalid username”.
Why Combine Equivalence Partitioning and Boundary Analysis Testing? Following are some of the reasons why to combine the two approaches:
•In this test cases are reduced into manageable chunks.
•The effectiveness of the testing is not compromised on test cases.
•Works well with a large number of variables.
Decision Table Testing -
•A Decision Table is a table that shows the relationship between inputs and rules, cases, and test conditions.
•It's a very useful tool for both complicated software testing and requirements management.
•The decision table allows testers to examine all conceivable combinations of requirements for testing and to immediately discover any circumstances that were overlooked.
•True(T) and False(F) values are used to signify the criteria.
The Future of Manual Testing in the age of AI
Rather than being replaced, manual testing is evolving to complement automation and emerging technologies. As testing needs become more complex, manual testing is adapting to meet new demands, ensuring that human insight remains a critical part of the quality assurance process.
•Shift Towards Hybrid Testing: Testing teams are gradually moving towards a hybrid testing approach with a blend of automation testing and manual testing.
•Exploratory and Ad-hoc Testing: Automation scripts take a defined path, but exploratory testing done by a human tester tests the far corners of application functionality, relying on intuition and adaptability to discover defects in unexpected areas.
•AI-Augmented Testing: Machine Learning techniques are helping to enhance manual testing by predicting test cases, prioritizing test scenarios, and defect detection using AI-driven tools.
•Accessibility Testing: Automated tools are unable to comprehensively evaluate digital accessibility since they cannot simulate how differently abled users with diverse needs experience products.
•Soft Skills and Domain Expertise: As software applications become more complex, testers with strong analytical skills, deep domain knowledge, and user empathy will continue to be valuable.
Conclusion -
As automation continues to grow, manual testers also need to upskill, pivot, and welcome hybrid testing methodologies. Software testing’s future lies in a balanced approach, utilizing automation where possible while maintaining the unique value that human testers deliver to quality assurance . Those who blend these technical skills with human insight will own the future.
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