Future of Manual testing in the age of AI & Manual testing techniques

The Future of Manual Testing in the Age of AI

As AI transforms industries, its impact on software testing is undeniable. While AI-driven automation tools are becoming more sophisticated, they are not replacing manual testing. Instead, the future lies in a hybrid approach where AI and manual testers work together to enhance software quality and efficiency.

1. AI-Powered Automation

AI has streamlined tasks like regression testing, data handling, and test execution, allowing for faster and more efficient testing. AI tools can autonomously generate test scripts, adapt to changes, and identify defects. However, AI still struggles with human-centric tasks, such as evaluating user experience (UX) and usability, areas where manual testers play a crucial role.

2. The Evolving Role of Testers

Manual testers will shift from repetitive tasks to more complex activities like exploratory testing. This requires creativity and intuition to uncover hidden issues that AI might miss. Testers will also design intricate test scenarios that are hard for AI to anticipate. As AI becomes a larger part of testing, manual testers will need to learn new skills, including using AI tools and automated frameworks.

3. Optimizing Testing with AI

AI not only automates but also optimizes testing by identifying areas likely to contain defects. By analyzing historical data, AI can streamline the test process, allowing manual testers to focus on the most critical aspects of the application. This combination of AI and human expertise increases testing efficiency and effectiveness.

4. Human Expertise in UX and Exploratory Testing

Human testers will remain essential for tasks requiring emotional intelligence and real-world context, like exploratory testing and UX evaluation. These areas rely on creativity, intuition, and user insight—qualities AI cannot replicate.

Conclusion

The future of software testing is a collaboration between AI and manual testers. AI will handle repetitive tasks, while testers will focus on complex areas requiring human judgment. This hybrid approach will ensure better quality and more efficient testing in the age of AI.

Boundary Value Analysis
This is a process of testing to be performed in and around the boundary values. It involves 4 valid inputs and 2 invalid inputs.
Example:
Consider an age field between 11 to 20,
In this field the valid inputs are 11,12,19 and 20. And invalid inputs are 10 and 21 which are neared to the boundary conditions.

Common Manual Testing Techniques

1. Black Box Testing
2. White Box Testing
3. Exploratory Testing
4. Regression Testing
5. Smoke Testing
6. Sanity Testing
7. Ad-Hoc Testing
8. Usability Testing
9. Compatibility Testing
10. Performance Testing
11. Boundary Value Analysis (BVA)
12. Equivalence Partitioning
13. Error Guessing
14. Acceptance Testing
15. Alpha and Beta Testing