Manual Testing

In the world of software development, testing is an essential activity that ensures the product meets the required standards and performs as expected. While automated testing has gained significant attention in recent years, manual testing still plays a vital role in the software testing lifecycle.

This blog will cover the following common manual testing techniques:

1. Boundary Value Analysis (BVA)
2. Decision Table Testing
3. The Future of Manual Testing in the Age of AI

1. Common Manual Testing Techniques

Manual testing is a process where human testers execute test cases without the use of automation tools. Although automation is becoming increasingly popular due to its speed and efficiency, manual testing remains essential for certain scenarios. Some of the most common manual testing techniques include:

a. Exploratory Testing
Exploratory testing is an unscripted testing technique where the tester actively explores the application without predefined test cases. The primary goal of this testing is to uncover unexpected issues by testing the software in various ways. Testers use their experience, knowledge, and intuition to find defects that might not be identified using structured test cases.

b. Ad-Hoc Testing
Ad-hoc testing is similar to exploratory testing, but without any planning. It is unstructured and often spontaneous. The tester simply focuses on breaking the system by performing random actions. This approach is useful when there’s little time for detailed testing, but testers still want to ensure that the application doesn't have major defects.

c. Regression Testing
Regression testing involves checking that previously developed and tested software still works after a change has been made. When developers update or add new features, regression testing ensures that no existing functionalities were inadvertently broken. While automated testing is often used for regression, manual regression testing can still be essential for some projects, especially when the changes are minimal.

d. Usability Testing
Usability testing involves assessing the software from the user’s perspective. Manual testers simulate real-world use cases, ensuring the software is easy to use and that the user interface (UI) is intuitive. Feedback collected during usability testing is invaluable for improving the overall user experience (UX).

e. Smoke Testing
Smoke testing is a preliminary level of testing conducted to determine if the basic functionality of an application works. This type of testing is often performed after a new build is deployed. The aim is to identify critical issues that would prevent further testing from being performed.

2. Boundary Value Analysis (BVA)

Boundary Value Analysis (BVA) is a black-box testing technique that focuses on testing the boundaries or limits of an input range. The core idea behind this technique is that most defects occur at the boundaries rather than in the middle of input ranges. BVA is an extension of Equivalence Partitioning, where input data is divided into equivalent classes.

Why Use Boundary Value Analysis?
Consider a scenario where a field accepts input from 1 to 100. The boundary values here would be 1 and 100. BVA focuses on these values and values near them, such as 0, 1, 99, and 100, as the most likely places for errors.

BVA Steps:

1. Identify the boundaries of the input domain.
2. Test with values on the boundaries.
3. Test values just above and just below the boundaries.
4. Analyze the behavior of the application when boundary values are entered.

Example of Boundary Value Analysis:
If a system accepts input for age between 18 and 65, the boundary values to test would be:

• 17 (just below the lower boundary)
• 18 (lower boundary)
• 64 (upper boundary)
• 65 (upper boundary)
• 66 (just above the upper boundary)

3. Decision Table Testing

Decision Table Testing is another black-box testing technique used to test systems that have complex business logic and multiple conditions. It involves creating a decision table that maps various conditions and their corresponding actions, allowing testers to cover all possible combinations of inputs and expected outcomes.

Why Use Decision Table Testing?
Decision Table Testing is useful when there are multiple conditions that must be considered together. For example, a system may have several Boolean conditions, and the combinations of true/false for these conditions can result in different outputs. Using a decision table helps to organize and manage these combinations efficiently.

Decision Table Structure:
A typical decision table consists of:

1. Conditions: The inputs or criteria.
2. Actions: The corresponding outcomes based on the conditions.
3. Rules: The various combinations of conditions and the corresponding actions.

Steps for Decision Table Testing:

1. Identify the various conditions that influence the outcome.
2. Create a decision table mapping conditions to actions.
3. Test each rule in the table to ensure that all combinations are covered.

4. The Future of Manual Testing in the Age of AI

With advancements in Artificial Intelligence (AI) and Machine Learning (ML), the landscape of software testing is undergoing a significant transformation. Automated testing powered by AI is increasingly able to perform tasks that were traditionally carried out by manual testers. However, despite these advancements, manual testing is far from obsolete. It still plays a crucial role, especially when it comes to complex and nuanced testing scenarios.

AI’s Impact on Manual Testing:
AI can analyze vast amounts of data, learn from past test cases, and predict where defects are likely to occur, making automated testing more intelligent and efficient. AI tools can also help in generating test cases based on application behavior and historical data, reducing the manual effort required.

However, manual testing is still necessary for several reasons:

1. Human Judgment and Creativity: AI may not be able to replicate the intuition and creativity of human testers, especially when dealing with unexpected or complex scenarios.
2. Usability Testing: Human testers can assess the user experience and interface, which requires subjective judgment, something AI cannot fully replicate.
3. Exploratory Testing: AI cannot explore software in the unstructured and unscripted manner that a human can. Exploratory testing often leads to finding hidden bugs that automated tools might miss.

How Manual Testing Will Evolve:
Manual testing is unlikely to disappear, but it will evolve. Testers will need to adopt a more strategic role, focusing on higher-level testing such as:

• Test design and planning.
• Exploratory and usability testing.
• Validating AI-generated test cases.
• Working with automation teams to ensure that automated tests complement manual testing efforts.

The Synergy Between Manual and AI-Driven Testing:
Rather than seeing AI and manual testing as mutually exclusive, the future of software testing lies in their collaboration. AI can handle repetitive, time-consuming tasks, while manual testers can focus on areas where human expertise is needed. Together, they will provide a comprehensive testing approach that ensures quality while maximizing efficiency.

Conclusion

In conclusion, manual testing techniques such as Boundary Value Analysis and Decision Table Testing remain integral to the software development process. While AI and automation continue to revolutionize the field, manual testing still offers irreplaceable value, especially in areas requiring human judgment and creativity. The future of software testing will be defined by the collaboration between manual and AI-driven approaches, ensuring the highest quality and performance in the applications we rely on daily.