Introduction to AI in Testing

1. Explore AI’s impact on testing workflows.

2. Compare traditional vs AI-enhanced processes.

3. Benefits: speed, accuracy, coverage.

4. AI's role across testing stages.

5. Tools for various user levels.

AI for Test Data Generation

1. Importance of quality test data.

2. Use Mockaroo, Faker for data structuring.

3. Edge-case input with ChatGPT.

4. Selecting the best data tool.

5. Keeping data both anonymous and realistic.

AI-Assisted Test Case Selection

1. Prioritize tests based on change tracks.

2. Streamline by removing unneeded tests.

3. Optimize with minimal effective test sets.

4. Derive tests from project requirements.

5. Visualize test impacts with dashboards.

Enhanced Test Generation with AI

1. Transform user stories into test cases.

2. Convert function headers to scripts.

3. Refine outputs via prompt tuning.

4. Utilize Copilot, Testim, Codeium.

5. Integrate tests directly into IDEs.

Streamlined Test Execution & Maintenance

1. Detect and address flaky tests.

2. Identify bottlenecks through analytics.

3. Implement self-repairing selectors.

4. Conduct visual regression evaluations.

5. Monitor evolving failed tests.

Predicting Defects with AI

1. Assess risk using past test/code data.

2. Link bugs to code churn, complexity.

3. Apply NLP for commit analysis.

4. Visualize risk via heatmaps.

5. Incorporate insights into planning.

Incorporating AI in Testing Workflows

1. Introduce AI into daily test routines.

2. Use ChatGPT for summarizing and insights.

3. Enhance CI/CD with AI (e.g., GitHub).

4. Use LLMs for failure triage.

5. Create standardized prompt guidelines.