AI/ML for continuous testing
Integrating AI and ML into the software’s continuous testing activities can significantly streamline processes and address potential bottlenecks in each activity, as illustrated in Figure 8.2.
Figure 8.2 – AI/ML for continuous testing activities
Here’s how these technologies can be applied across various testing activities:
- Requirements analysis:
- Explanation: Ensures that test scenarios align with business requirements and user needs.
- Bottleneck: Misinterpretation or incomplete analysis can lead to inadequate test coverage.
- AI/ML solution: NLP can automate the extraction and interpretation of requirements, ensuring comprehensive and accurate test coverage.
- Test strategy:
- Explanation: Outlines the testing approach, objectives, and resources.
- Bottleneck: An unclear strategy may lead to inefficient testing efforts and resource allocation.
- AI/ML solution: AI can analyze historical data to suggest the...
