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課程簡介
Introduction to AI in QA Automation
- Role of AI in modern software testing
- Comparison of traditional vs. AI-enhanced QA strategies
- Overview of AI-based testing tools (Testim, mabl, Functionize)
Generating Tests with AI
- Model-based and UI-based test generation
- Using Testim or similar platforms to auto-generate flows
- Evaluating test intent, stability, and reusability
Regression Analysis and Test Prioritization
- Impact-based test selection and pruning
- Change-aware test runs for large repositories
- AI-driven prioritization based on risk and frequency
Integration with CI/CD Pipelines
- Connecting automated tests to Jenkins, GitHub Actions, or GitLab CI
- Automated quality gating and test feedback loops
- Triggering tests on pull requests and deployment events
Defect Prediction and Anomaly Detection
- Analyzing test data to predict likely failure areas
- Clustering and triaging anomalies using ML techniques
- Feedback to developers using AI-generated insights
Maintaining and Scaling AI-Based Tests
- Dealing with test drift and UI changes
- Version control and test configuration management
- Scaling to enterprise-level QA environments
Case Studies and Real-World Applications
- Enterprise implementations of AI QA pipelines
- Best practices for team adoption and rollout
- Lessons learned: successes, failures, and tuning
Summary and Next Steps
最低要求
- Experience with software testing or QA workflows
- Familiarity with CI/CD pipelines and DevOps practices
- Basic understanding of automated testing tools or frameworks
Audience
- QA leads and test automation engineers
- DevOps professionals and SREs
- Agile testers and quality managers
14 時間：
