Fundamentals of QA and Modern Testing with AI (ISTQB Foundation Preparation) Training Course

Module 1: Foundations of Quality Assurance and Testing

• Defining quality, quality assurance, and testing
• The seven testing principles (ISTQB CTFL v4.0)
• Distinguishing between testing, debugging, and quality control
• The psychology behind testing
• Roles and responsibilities within a QA team

Module 2: Software Development Lifecycle and Testing

• Phases of the Software Testing Life Cycle (STLC)
• Testing approaches for Waterfall, Agile, DevOps, and CI/CD environments
• Test levels: unit, integration, system, and acceptance
• Shift-left and shift-right testing strategies
• Establishing traceability between requirements and test cases

Module 3: Static Testing Techniques

• Reviews, walkthroughs, and inspections
• Static analysis using automated tools
• Checklist-based and role-based reviewing methods
• Formal and informal review techniques
• Incorporating static testing into Agile workflows

Module 4: Test Techniques

• Black-box techniques: equivalence partitioning and boundary value analysis
• Decision table testing and state transition testing
• Use case testing and exploratory testing
• White-box techniques: statement and decision coverage
• Experience-based techniques and error guessing

Module 5: Defect Management

• Defect lifecycle: detection, reporting, triage, resolution, and closure
• Writing effective defect reports using JIRA
• Classifying defect severity versus priority
• Root cause analysis techniques
• Analyzing defect metrics and trends

Module 6: Test Management and Risk-Based Testing

• Test planning and estimation methods
• Risk identification, assessment, and mitigation strategies
• Monitoring, controlling, and reporting on tests
• Defining test completion criteria and exit conditions
• Developing ISTQB-aligned test strategy and policy documents

Module 7: Test Tools and Automation Fundamentals

• Classification of test tools (ISTQB tool categories)
• Benefits and risks associated with test automation
• Selecting tools: comparing open-source versus commercial solutions
• Introduction to Selenium, Playwright, and Cypress
• Constructing a basic automated test suite

Module 8: Introduction to AI in Quality Assurance

• AI and machine learning concepts relevant to testers
• Taxonomy: distinguishing AI for testing from testing AI systems
• Current AI testing landscape: opportunities and limitations
• Quality characteristics specific to AI-based systems
• Overview and relevance of the ISTQB CT-AI syllabus

Module 9: AI-Assisted Test Case Generation

• Leveraging LLMs (ChatGPT, Claude, Copilot) for drafting test cases
• Prompt engineering techniques for generating test scenarios
• Converting user stories and acceptance criteria into test cases
• Reviewing and validating AI-generated test cases
• Platforms: Testim, Mabl, and AI-native test generation tools

Module 10: AI-Assisted Test Automation

• Self-healing test automation using Katalon Studio AI
• AI-driven object recognition and element location
• Visual regression testing with Applitools Eyes
• Enhancing Selenium with AI plugins for resilient automation
• Reducing maintenance overhead through intelligent locators

Module 11: AI for Defect Prediction and Analysis

• Predictive test selection using Launchable and Sealights
• Failure clustering and anomaly detection with ReportPortal
• AI-assisted root cause analysis
• Quality risk scoring and test gap analytics
• Prioritizing testing efforts using historical defect data

Module 12: AI Tools Evaluation and CI/CD Integration

• Criteria for evaluating AI testing tools
• Conducting ROI analysis and developing an adoption strategy
• Integrating AI testing tools into Jenkins, GitHub Actions, and GitLab CI
• Pipeline design: determining when and where to execute AI-powered tests
• Measuring the effectiveness of AI testing through metrics

Module 13: Ethical Considerations in AI-Driven Testing

• Addressing bias and fairness in AI-generated test data
• Privacy concerns related to cloud-based AI tools
• Ensuring transparency and explainability of AI testing decisions
• Governance and compliance considerations
• Implementing responsible AI practices for QA teams

Module 14: ISTQB CTFL Exam Preparation

• CTFL v4.0 exam structure, duration, and scoring criteria
• Understanding question types and mastering answer strategies
• Distribution of topic weight across CTFL syllabus chapters
• Practice exam featuring sample ISTQB-style questions
• Developing a study roadmap and identifying recommended resources

Module 15: Capstone: End-to-End AI-Enhanced Testing Workflow

• Designing test cases based on a sample requirements document
• Utilizing AI to generate and refine test scenarios
• Automating selected tests with self-healing tools
• Reporting defects and conducting AI-assisted root cause analysis
• Retrospective: integrating AI into daily QA practice