AI for Software QA Professionals Training in Astana, Kazakhstan

This comprehensive training program is designed for software QA professionals seeking to integrate artificial intelligence into their testing workflows. Participants will explore how AI can automate test case generation, improve defect detection, and optimize test coverage. The course covers foundational AI concepts, machine learning models for quality assurance, natural language processing for test documentation, and practical strategies for implementing AI-driven testing tools. Through a blend of conceptual learning and practical exercises, attendees will gain the skills needed to modernize their QA practices and deliver higher quality software faster.

Introduction to AI for Software QA

• Overview of artificial intelligence and its relevance to software testing
• Key AI techniques: machine learning, natural language processing, computer vision
• Benefits and challenges of AI in QA
• Real-world use cases and industry trends

Machine Learning Fundamentals for QA

• Supervised vs unsupervised learning
• Common algorithms: regression, classification, clustering
• Feature engineering and data preprocessing for test data
• Model training, evaluation, and validation basics

Defect Prediction and Root Cause Analysis

• Building defect prediction models using historical data
• Identifying high-risk areas in codebases
• Root cause analysis with association rules and clustering
• Integrating predictions into test planning

Natural Language Processing for Test Automation

• Processing requirement documents and user stories
• Generating test cases from natural language descriptions
• Automated test script generation using NLP
• Sentiment analysis for bug report prioritization

AI-Driven Test Execution and Optimization

• Self-healing test automation scripts
• Intelligent test selection and prioritization
• Visual testing with computer vision
• Performance testing with AI-based anomaly detection

Implementing AI in QA Pipelines

• Integrating AI models with CI/CD pipelines
• Selecting appropriate AI tools and platforms
• Data management and governance for AI in QA
• Monitoring and maintaining AI models in production

Ethics, Bias, and Future Trends

• Ethical considerations in AI-based testing
• Identifying and mitigating bias in models
• Explainability and transparency of AI decisions
• Emerging trends: generative AI, autonomous testing