Testing is a crucial stage within the Software Development Life Cycle (SDLC) that ensures the software/application meets requirements, is defect free, and delivers a high-quality experience. Testing can be enhanced by AI, AI testing is a type of software testing that uses artificial intelligence to enhance and streamline the testing process.
Table Of Contents
- Manual Testing Techniques
- Boundary value Analysis (BVA)
- Decision Table Testing (DTT)
- Future of Manual Testing using AI
- Conclusion
1. Manual Testing Techniques
Manual Testing is a process in software development where testers manually execute test cases without using any automation tools. Human Tester tests the software based on predefined test cases derived from requirements.
There are several types of Manual Testing like Black Box Testing, White Box Testing, Gray Box Testing, Exploratory Testing, Usability Testing, Ad-hoc Testing, Functional Testing, Regression Testing and Acceptance Testing (UAT).
2. Boundary value Analysis (BVA)
Boundary Value Analysis (BVA) is most popular black box testing technique. BVA involves testing the numbers at or just beyond the boundaries of that range of numbers to make sure the application knows how to handle both valid and invalid numbers correctly.
Example:
The minimum age limit for term insurance is 18 years while the maximum age is 60 years in Policy Administration System.
• Minimum Boundary: Test with the age values of 17 and 18.
• Maximum Boundary: Test with the age values of 60 and 61.
• Valid Inputs: Test with age values of 19 and 59.
• Invalid Inputs: Test cases with age values outside the valid range (e.g., 16 and 62)
3. Decision Table Testing (DTT)
A Decision Table Testing is black box testing technique used to define test scenarios. Decision table is a tool used in Software Testing to represent and analyze different combinations of conditions and actions. It ensures comprehensive test coverage.
Example:
The minimum age limit for term insurance is 18 years while the maximum age is 60 years in Policy Administration System.
Rule Valid Age Action
1 Y Term insurance allowed
2 N Term Insurance not allowed
4. Future of Manual Testing using AI
AI Software Testing involves integrating artificial intelligence and machine learning algorithms into the software testing process. It provides more accurate results and saves time. In the market, variety of AI tools are available to enhance Manual Testing. These AI tools are revolutionizing four main functions of software testing like test case preparation, test data generation, predicting defects and analyzing results.
Test Case Preparation
AI can analyze user behavior, application descriptions, historical data to automatically generate test cases, focusing on critical areas and prioritizing high risk scenarios. AI can interpret natural language descriptions using NLP (Natural Language Processing) of testcases and it can automatically translate them into executable test scripts.
Test Data generation
AI can create test data that closely resembles real user inputs enhancing the quality of testing
Test Execution
AI can prioritize critical test cases, saving time and resources by executing only essential test cases. It can facilitate parallel execution across multiple environments, reducing execution time. It eliminates repetitive tests.
Debugging
AI can easily identify unusual patterns in application behavior, helping human tester to find potential issues and bugs. AI can also help identify areas where defects are more likely to occur so that testers focus their efforts on the most vulnerable part of application. AI can also analyze system logs and other data to help identify the root cause of the defects.
5.Conclusion
AI can automate repetitive tasks, improve testing efficiency and providing intelligent insights. It cannot replace human testers out of box thinking abilities, knowledge and creative thinking. By combining AI tools with Manual testing, we can create testing process faster, more reliable and delivers high quality software.