- Testing is labor-intensive, and Agile allows for changing expectations.
- Implementation changes can break tests, and time is limited, necessitating a reduction in manual efforts.
ML can streamline testing in various stages, including test definition, automatic code generation, execution (exploratory testing), maintenance, code review, bug fixing, test case prioritization, and Bug Triage.
Results & Conclusion:
We’ll highlight how ML benefits each stage and discuss the advantages and potential risks of AI in software testing. In summary, this talk addresses the vital issue of AI-based applications in software testing, considering the current prominence of AI in the software industry. We cover a range of AI applications in different testing phases to cater to your specific interests.
Takeaways from the talk:
After the talk, attendees will have seen how Machine Learning can be used to:
- Generate test cases automatically.
- Review test code.
- Heal broken test code.
- Prioritize test cases.
- Exploratory testing.
- Manage bugs.