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

Module 1: Core Principles of Quality Assurance and Testing

• Defining the concepts of quality, quality assurance, and systematic testing
• Examining the seven fundamental testing principles (ISTQB CTFL v4.0)
• Differentiating between testing, debugging, and quality control processes
• Understanding the psychological aspects involved in effective testing
• Clarifying roles and responsibilities within a quality assurance team

Module 2: Software Development Lifecycle Integration

• Identifying the distinct phases of the Software Testing Life Cycle (STLC)
• Comparing testing methodologies within Waterfall, Agile, DevOps, and CI/CD frameworks
• Reviewing test levels: unit, integration, system, and acceptance testing
• Evaluating shift-left and shift-right testing strategies for efficiency
• Establishing traceability between system requirements and test cases

Module 3: Static Testing Methodologies

• Implementing reviews, walkthroughs, and formal inspections
• Utilizing automated tools for static code and documentation analysis
• Applying checklist-based and role-based reviewing protocols
• Conducting both formal and informal review sessions
• Embedding static testing practices into Agile development workflows

Module 4: Core Test Techniques

• Applying black-box techniques: equivalence partitioning and boundary value analysis
• Utilizing decision table testing and state transition testing methods
• Implementing use case testing and exploratory testing practices
• Using white-box techniques: statement and decision coverage metrics
• Employing experience-based techniques and structured error guessing

Module 5: Defect Management Processes

• Managing the defect lifecycle: detection, reporting, triage, resolution, and closure
• Creating effective defect reports using JIRA
• Classifying defects by severity and priority levels
• Conducting root cause analysis to prevent recurrence
• Tracking defect metrics and performing trend analysis

Module 6: Test Management and Risk-Based Testing

• Developing test plans and applying estimation methods
• Identifying, assessing, and mitigating project risks
• Monitoring test progress and generating control reports
• Defining clear test completion criteria and exit conditions
• Creating ISTQB-aligned test strategy and policy documents

Module 7: Test Tools and Automation Basics

• Classifying test tools according to ISTQB categories
• Weighing the benefits and risks of adopting test automation
• Selecting appropriate tools: open-source versus commercial solutions
• Introducing key automation frameworks: Selenium, Playwright, and Cypress
• Constructing a basic automated test suite for validation

Module 8: Introduction to Artificial Intelligence in Quality Assurance

• Exploring AI and machine learning concepts relevant to testers
• Distinguishing between AI for testing and testing of AI systems
• Analyzing the current AI testing landscape: opportunities and limitations
• Evaluating quality characteristics specific to AI-based systems
• Reviewing the ISTQB CT-AI syllabus and its professional relevance

Module 9: AI-Assisted Test Case Generation

• Leveraging LLMs (ChatGPT, Claude, Copilot) for drafting test cases
• Applying prompt engineering techniques to generate test scenarios
• Translating user stories and acceptance criteria into executable test cases
• Reviewing and validating AI-generated test content for accuracy
• Utilizing platforms such as Testim, Mabl, and AI-native generation tools

Module 10: AI-Enhanced Test Automation

• Implementing self-healing test automation with Katalon Studio AI
• Utilizing AI-driven object recognition and element location strategies
• Conducting visual regression testing using Applitools Eyes
• Integrating Selenium with AI plugins for resilient automation
• Reducing maintenance overhead through intelligent locator strategies

Module 11: AI for Defect Prediction and Analysis

• Performing predictive test selection using Launchable and Sealights
• Clustering failures and detecting anomalies with ReportPortal
• Conducting AI-assisted root cause analysis for deeper insights
• Applying quality risk scoring and test gap analytics
• Leveraging historical defect data to prioritize testing efforts

Module 12: AI Tools Evaluation and CI/CD Integration

• Establishing criteria for evaluating AI testing tools
• Analyzing ROI and developing an adoption strategy
• Integrating AI testing tools into Jenkins, GitHub Actions, and GitLab CI
• Designing pipelines to determine optimal timing for AI-powered tests
• Measuring AI testing effectiveness through defined metrics

Module 13: Ethical Considerations in AI-Driven Testing

• Addressing bias and fairness in AI-generated test data
• Mitigating privacy concerns associated with cloud-based AI tools
• Ensuring transparency and explainability of AI testing decisions
• Adhering to governance and compliance standards
• Implementing responsible AI practices for quality assurance teams

Module 14: ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring
• Applying strategies for different question types and answers
• Reviewing topic weight distribution across CTFL syllabus chapters
• Completing practice exams with sample ISTQB-style questions
• Following a structured study roadmap and recommended resources

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

• Designing test cases derived from a sample requirements document
• Using AI to generate and refine specific test scenarios
• Automating selected tests utilizing self-healing tools
• Reporting defects and executing AI-assisted root cause analysis
• Conducting a retrospective on integrating AI into daily QA practice