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

Module 1: Fundamentals of Quality Assurance and Testing

• Establishing definitions for quality, assurance, and testing functions
• Application of the seven core testing principles (ISTQB CTFL v4.0)
• Distinguishing between testing, debugging, and quality control processes
• Psychological factors influencing testing activities
• Defining roles, responsibilities, and accountabilities within QA teams

Module 2: Software Development Life Cycle and Testing Integration

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

Module 3: Static Testing Techniques

• Conducting reviews, walkthroughs, and formal inspections
• Utilizing automated tools for static code analysis
• Applying checklist-based and role-based review methods
• Differentiating between formal and informal review processes
• Integrating static testing into Agile development workflows

Module 4: Test Design Techniques

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

Module 5: Defect Management Processes

• Defect lifecycle stages: detection, reporting, triage, resolution, and closure
• Best practices for writing defect reports using JIRA
• Differentiating defect severity from priority classification
• Conducting root cause analysis
• Analyzing defect metrics and trend data

Module 6: Test Management and Risk-Based Testing

• Methods for test planning and effort estimation
• Identifying, assessing, and mitigating testing risks
• Monitoring, controlling, and reporting on test progress
• Establishing test completion criteria and exit conditions
• Developing ISTQB-aligned test strategies and policies

Module 7: Test Tools and Automation Fundamentals

• Classifying test tools per ISTQB categories
• Evaluating the benefits and risks associated with test automation
• Selecting appropriate tools: open-source versus commercial solutions
• Overview of Selenium, Playwright, and Cypress frameworks
• Constructing a basic automated test suite

Module 8: Introduction to Artificial Intelligence in Quality Assurance

• Fundamental concepts of AI and machine learning for testers
• Differentiating AI for testing versus testing AI systems
• Current landscape: opportunities and limitations of AI in testing
• Quality characteristics specific to AI-based systems
• Overview of the ISTQB CT-AI syllabus and its relevance

Module 9: AI-Assisted Test Case Generation

• Leveraging LLMs (ChatGPT, Claude, Copilot) for drafting test cases
• Prompt engineering techniques for creating test scenarios
• Translating user stories and acceptance criteria into test cases
• Validating and reviewing AI-generated test outputs
• Evaluating platforms such as Testim, Mabl, and AI-native generation tools

Module 10: AI-Assisted Test Automation

• Implementing self-healing test automation via Katalon Studio AI
• AI-driven object recognition and element location strategies
• Visual regression testing using Applitools Eyes
• Enhancing Selenium resilience with AI plugins
• Reducing maintenance overhead through intelligent locator techniques

Module 11: AI for Defect Prediction and Analysis

• Predictive test selection using Launchable and Sealights
• Anomaly detection and failure clustering with ReportPortal
• AI-assisted root cause analysis methods
• Assessing quality risk scores and identifying test gaps
• Leveraging historical defect data to prioritize testing efforts

Module 12: AI Tools Evaluation and CI/CD Integration

• Criteria for selecting and evaluating AI testing tools
• Conducting ROI analysis and developing adoption strategies
• Integrating AI tools into Jenkins, GitHub Actions, and GitLab CI pipelines
• Designing pipelines to determine the optimal placement for AI-powered tests
• Measuring effectiveness through defined AI testing metrics

Module 13: Ethical Considerations in AI-Driven Testing

• Addressing bias and fairness in AI-generated test data
• Mitigating privacy risks when utilizing cloud-based AI tools
• Ensuring transparency and explainability in AI testing decisions
• Adhering to governance and compliance requirements
• Establishing responsible AI practices for QA teams

Module 14: ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring criteria
• Analyzing question types and developing answer strategies
• Reviewing topic weight distribution across the CTFL syllabus chapters
• Completing practice exams with 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 sample requirements documentation
• Utilizing AI to generate and refine test scenarios
• Automating selected tests using self-healing automation tools
• Reporting defects and executing AI-assisted root cause analysis
• Conducting a retrospective on integrating AI into daily QA operations for government