By: Jitendra Kumar

Use Case

Software testing presents challenges when validating software using traditional concepts. It relies heavily on the experience and knowledge in the testing domain for comprehensive coverage. GEN AI represents a revolution in overcoming these limitations inherent in software testing, which are often due to the technical expertise and experience of test engineers.

Approach

In software testing, the initial step involves devising scenarios covering the features, followed by selecting appropriate test data to assess the feature's functionality across all valid user flows and handling of invalid data gracefully. With Gen AI tools such as Copilot, test engineers can generate scenarios and test data. Additionally, AI tools can suggest various methods for data coverage, identifying security threats, and ensuring coverage for them.

Architecture Design

The approach is to use the AI tool to assist the Test Engineer in generating test scenarios, test data coverage, and testing approaches for the feature under test. It acts as an expert guide, indicating the desired areas and data for testing. Below is an example of how the AI tool Copilot has generated scenarios, test data, and approaches for testing a file upload feature.

Conclusion

With the evolution of AI tools, software testing has been enhanced, improving test coverage, and moving towards completeness to instill confidence in the quality of the product. This development is certainly helping to overcome the limitations inherent in relying solely on the technical knowledge and experience of test engineers.

While I have shared just one example here, I am confident that this approach will inspire you to consider using AI during your software testing endeavors.

Thank you.

References

https://copilot.microsoft.com/