Introduction:-
In today’s fast-paced digital world, software is at the heart of every business. From mobile banking apps to e-commerce platforms and healthcare systems, the reliability of software directly impacts user trust. To ensure quality, software testing plays a crucial role.
Although automation testing has gained massive popularity, manual testing remains relevant. Manual testing allows testers to apply human intuition, creativity, and judgment — things machines cannot fully replicate.
In this blog, we will explore:
- Common manual testing techniques
- Boundary Value Analysis (BVA)
- Decision Table Testing
- The future of manual testing in the age of AI
Along the way, we’ll also look at real-time examples to understand how these techniques are applied in practice.
- Common Manual Testing Techniques Manual testing relies on human effort, where testers execute test cases without automation tools. Here are some widely used techniques: a) Exploratory Testing In exploratory testing, testers learn about the system while testing it. They do not follow pre-written test cases but instead use their creativity and domain knowledge to uncover bugs.
Example: Imagine a food delivery app. A tester might explore what happens if a user places an order, cancels it halfway, then quickly reorders. Such scenarios are not always documented, but exploratory testing helps uncover unexpected behavior.
b) Ad-hoc Testing
Ad-hoc testing is informal and unstructured. It’s often done when time is short, and testers rely on intuition.
Example: In an e-commerce website, a tester might randomly try entering emojis in the delivery address field. If the app crashes, it exposes a potential vulnerability.
c) Smoke Testing
Also called “build verification testing,” smoke testing ensures that the critical functions of an application work after a new build or release.
Example: After deploying a new version of a banking app, testers quickly check if login, account balance viewing, and fund transfer still work. If these fail, deeper testing is halted.
d) Regression Testing
This technique ensures that new changes don’t break existing features.
Example: Suppose a retail website introduces a new “Buy Now, Pay Later” feature. Regression testing would recheck older functionalities like product search, add-to-cart, and payment gateway to ensure they still work.
e) User Acceptance Testing (UAT)
UAT is the final phase where the product is validated against business needs by end users or clients.
Example: A hospital implements a new patient management system. Doctors and nurses test whether they can schedule appointments, update records, and access patient history as expected.
f) Integration Testing
This checks whether different modules or systems work together seamlessly.
Example: In a travel booking platform, the flight module, hotel module, and payment gateway must integrate properly. If booking a flight and hotel together fails, integration testing highlights the issue.
- Boundary Value Analysis (BVA) Boundary Value Analysis is a black-box testing technique that focuses on input boundaries rather than the entire input range. The logic is simple: defects often occur at the “edges” of input values.
Example: Online Form Age Input
Suppose a university application accepts student age between 18 and 25.
• Valid inputs: 18, 19, 25
• Invalid inputs: 17, 26
Test Cases:
• Lower boundary: 17, 18, 19
• Upper boundary: 24, 25, 26
This way, we don’t need to test all ages (18–25). Instead, by testing boundary values, we detect maximum defects with fewer test cases.
Real-time Use Case: Banking
When applying for a credit card online, if the minimum salary is ₹25,000 and the maximum limit is ₹2,00,000, testing values like ₹24,999, ₹25,000, ₹25,001, ₹1,99,999, ₹2,00,000, and ₹2,00,001 ensures system reliability.
Advantages: Efficient, reduces test cases, high defect detection at critical points.
Limitations: Not suitable for non-numeric validations (like text fields).
- Decision Table Testing Decision Table Testing is a systematic technique used when there are multiple conditions leading to different actions. It ensures coverage of all possible combinations. Example: Login System Conditions:
- Correct Username?
- Correct Password?
- Account Active? Possible Outcomes: • If all conditions are true → Login successful • If username or password is wrong → Error message • If account is inactive → Show “Account Disabled”
Condition / Rule R1 R2 R3 R4
Username correct Y Y N Y
Password correct Y N Y N
Account active Y Y Y N
Action Login Success Error Error Account Disabled
This table ensures testers don’t miss any combination.
Real-time Use Case: Insurance Premium Calculation
An insurance app may calculate premium based on:
• Age group (below 30, 30–50, above 50)
• Medical history (yes/no)
• Smoking habit (yes/no)
Decision table testing ensures all rule combinations are verified for accurate premium calculation.
- The Future of Manual Testing in the Age of AI With the rise of Artificial Intelligence (AI) and Machine Learning (ML), automation testing has become more advanced.
Tools can now:
• Generate test cases automatically
• Perform predictive defect analysis
• Execute regression suites faster
Will AI Replace Manual Testing?
Not entirely. While AI can speed up repetitive tasks, it lacks human intuition.
For example:
• Usability Testing: Only a human can judge if an app is user-friendly.
• Exploratory Testing: AI cannot replicate human creativity in trying unexpected scenarios.
• Contextual Understanding: A tester understands business needs and user emotions, which AI cannot
Real-time Example: Chatbots in Customer Service
An AI chatbot may handle most queries, but when a frustrated customer types an angry message, a human agent is better equipped to handle the situation empathetically. Similarly, AI in testing is powerful, but humans are needed for critical judgment.
The Hybrid Future:-
The future is “human + AI collaboration”:
• AI will handle repetitive, data-heavy tasks.
• Manual testers will focus on strategy, usability, ethical testing, and areas requiring creativity.
Conclusion
Manual testing continues to play an essential role in software quality assurance. Techniques like exploratory testing, regression testing, and UAT ensure real-world reliability. Structured methods such as Boundary Value Analysis and Decision Table Testing help cover edge cases and complex rules efficiently.
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