In this presentation, we’ll delve into the use of Machine Learning (ML) in Software Testing, accompanied by practical examples and a case study on Bug Triage. Let’s embrace the future together!

Challenges:

• 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.

Solutions:

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.