Introduction
Software testing is one of the most important stages of the software development life cycle. Even with the rise of automation, cloud technologies, and artificial intelligence, manual testing remains a critical skill. It ensures the software works correctly where human perception, judgment, and creativity are needed.
In this blog, I will walk through four important areas:
Common Manual Testing Techniques
Boundary Value Analysis (BVA)
Decision Table Testing
The Future of Manual Testing in the Age of AI**
Each of these plays a key role in helping testers deliver high-quality products.
Common Manual Testing Techniques:
Manual testing means checking software by using it the way a real user would. Testers follow steps, try different inputs, and observe how the application behaves. Here are some of the most commonly used techniques:
1. Functional Testing
This is the basic type of testing. You check if each feature works the way it is supposed to.
Example:
-> Does login work?
-> Does the search bar show results?
-> Can the user place an order?
If the feature matches the requirement, it passes.
2. Exploratory Testing
In this technique, testers don’t follow pre-written test cases. They simply explore the application freely.
They click around, try different things, and see what happens.
This helps find unexpected bugs.
3. Regression Testing
Whenever developers add a new feature or fix a bug, testers must check old features again.
Why? Because new changes can break existing functions.
Regression testing ensures everything still works after updates.
4. Usability Testing
Here the tester checks if the app is easy to use.
Questions we ask:
Is the navigation simple?
Are buttons visible?
Is the design clean and user-friendly?
Good usability makes users happy.
5. Smoke Testing
Smoke testing is a quick check done when a new version of the app is released.
It confirms the major features are working so testers can continue detailed testing.
6. Negative Testing
Testers try to enter wrong or invalid data to see if the system handles errors correctly.
Example: Typing letters in a mobile number field.
This helps make the system stronger.
7. Ad-hoc Testing
This is unplanned testing.
Testers use their experience and intuition to quickly find bugs without any documents or test cases.
Boundary Value Analysis:
Boundary Value Analysis (BVA) is a testing technique used to check values at the edges or limits of a field. The idea is simple: most bugs happen at the boundary of a range, not in the middle.
Example:
If an app allows age between 18 and 60, then the important values to test are:
17 (just below minimum)
18 (minimum allowed)
19 (just above minimum)
59 (just below maximum)
60 (maximum allowed)
61 (just above maximum)
By testing these values, we can easily find if the system is accepting or rejecting values correctly. For example, the system should reject 17 and 61 but accept 18 to 60.
Why BVA is useful:
->It reduces the number of test cases
->It finds important validation bugs
->It checks if the limits are handled properly
->It is simple to apply in any form or input field
Where we use BVA :
->Age fields
->Price ranges
->Date ranges
->Quantity input
->Any place where minimum–maximum values are involved
BVA is one of the most commonly used techniques in both manual and automation testing because it gives strong coverage with very little effort.
Decision Table Testing
Decision Table Testing is useful when the system behaves differently based on different conditions.
Instead of writing long test cases, we create a table to show all possible combinations.
It helps when there are multiple “IF–ELSE” type conditions.
Example
An online store gives discount based on two conditions:
Is the user a member?
Is the cart value above ₹500?
Using these conditions, we create a table:
| Rule | Member? | Cart > 500? | Discount |
|---|---|---|---|
| 1 | No | No | No discount |
| 2 | No | Yes | 5% |
| 3 | Yes | No | 10% |
| 4 | Yes | Yes | 15% |
This table clearly shows all situations that must be tested.
Why decision tables are useful :
->They make complex logic easy to understand
->They help ensure no combination is missed
->They give complete test coverage
->They reduce confusion in requirement interpretation
Where decision tables are used :
-> Loan approval systems
-> Bank transactions
-> Insurance claim rules
-> E-commerce discount rules
Any place where multiple conditions decide the output
Decision Table Testing saves time and helps testers handle complicated logic in a clean and simple way.
The Future of Manual Testing in the Age of AI
Artificial Intelligence (AI) is changing how software is tested. Today, many testing tools can automatically run test cases, fix broken scripts, and even suggest new tests. Because of this, many people worry that AI will replace manual testers.
But the truth is very simple: AI will not replace manual testing. Instead, it will change the role and make it smarter.
- AI Will Do Repetitive Work
AI tools are very good at doing boring, repetitive tasks. For example:
-> Running the same regression tests again and again
-> Fixing broken locators when the UI changes
-> Analysing logs and error messages
-> Suggesting test cases based on previous bugs
This helps testers save time.
Because AI handles routine work, testers can focus on more important tasks.
- Human Testers Will Focus on Exploratory and Usability Testing
AI cannot think like a human. It cannot understand:
-> What confuses the user
-> What looks complicated
-> What feels slow or uncomfortable
-> What makes the user happy
These things need human judgment.
Manual testers will spend more time on:
-> Exploring the application freely
-> Checking if the design is user-friendly
-> Understanding real-life behaviour
-> Finding problems that AI cannot see
- Testers Will Need Some Technical Skills
The future tester will be a mix of manual + basic automation.
Companies now expect testers to know:
-> API testing (Postman)
-> Simple automation (Selenium, Playwright, Cypress)
-> SQL
-> Git basics
-> How CI/CD pipelines work
You don’t need to be a developer, but learning these skills makes you stronger and more flexible.
- Manual Testing Will Become Part of Quality Engineering
The industry is moving from QA (Quality Assurance) to QE (Quality Engineering).
This means testers will help:
-> Prevent bugs, not just find them
-> Review requirements from the start
-> Suggest improvements
-> Work closely with developers
-> Test continuously in DevOps environments
Manual testers will play an important role in the full software process, not only at the end.
- What AI Cannot Do:
Even advanced AI cannot:
-> Understand emotions
-> Read unclear requirements
-> Think creatively
-> Test completely new features with no historical data
-> Make logical decisions in tricky situations
-> Notice small UX issues that a user may face
Only humans can do these things.
This is why manual testing is still important.
- The Future: Testers Working With AI, Not Against It
The future is not “AI vs Tester.”
It is AI + Tester = Better Software Quality.
AI will speed up the process, and human testers will make sure the product is correct, easy to use, and meaningful.
Conclusion:
Manual testing is not going away.
AI will make testing faster and smarter, but human testers are still needed for understanding real users, exploring the application, checking usability, and making decisions based on logic and experience.
The future manual tester will be more skilled, more technical, and more valuable — working together with AI to deliver high-quality software.
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