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

Essentials of Quality Assurance and Testing

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

Software Development Lifecycle and Testing

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

Static Testing Techniques

• Conducting reviews, walkthroughs, and inspections
• Performing static analysis with automated tools
• Implementing checklist-based and role-based reviews
• Applying formal and informal review methods
• Incorporating static testing into Agile workflows

Testing Techniques

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

Defect Management

• Defect lifecycle: detection, reporting, triage, resolution, and closure
• Creating effective defect reports using JIRA
• Classifying defect severity versus priority
• Techniques for root cause analysis
• Analyzing defect metrics and trends

Test Management and Risk-Based Testing

• Methods for test planning and estimation
• Identifying, assessing, and mitigating risks
• Monitoring, controlling, and reporting test activities
• Setting test completion criteria and exit conditions
• Developing ISTQB-aligned test strategy and policy documents

Test Tools and Automation Fundamentals

• Classifying test tools (ISTQB tool categories)
• Understanding the benefits and risks of test automation
• Selecting tools: open-source versus commercial solutions
• Overview of Selenium, Playwright, and Cypress
• Building a foundational automated test suite

Introduction to AI in Quality Assurance

• AI and machine learning concepts relevant to testers
• Distinguishing AI for testing versus testing of AI systems
• The current AI testing landscape: opportunities and limitations
• Quality characteristics specific to AI-based systems
• Overview of the ISTQB CT-AI syllabus and its relevance

AI-Assisted Test Case Generation

• Drafting test cases using LLMs (ChatGPT, Claude, Copilot)
• Using prompt engineering techniques to generate test scenarios
• Translating user stories and acceptance criteria into test cases
• Reviewing and validating AI-generated test cases
• Utilizing platforms like Testim, Mabl, and AI-native test generation tools

AI-Assisted Test Automation

• Implementing self-healing test automation with Katalon Studio AI
• Leveraging AI-driven object recognition and element location
• Conducting visual regression testing with Applitools Eyes
• Enhancing resilience with Selenium and AI plugins
• Reducing maintenance overhead through intelligent locators

AI for Defect Prediction and Analysis

• Performing predictive test selection with Launchable and Sealights
• Using ReportPortal for failure clustering and anomaly detection
• Applying AI-assisted root cause analysis
• Evaluating quality risk scores and test gap analytics
• Prioritizing testing using historical defect data

AI Tools Evaluation and CI/CD Integration

• Establishing criteria for evaluating AI testing tools
• Analyzing ROI and formulating an adoption strategy
• Integrating AI testing tools into Jenkins, GitHub Actions, and GitLab CI
• Designing pipelines to determine when and where to run AI-powered tests
• Measuring the effectiveness of AI testing with metrics

Ethical Considerations in AI-Driven Testing

• Addressing bias and fairness in AI-generated test data
• Managing privacy concerns with cloud-based AI tools
• Ensuring transparency and explainability in AI testing decisions
• Navigating governance and compliance requirements
• Adopting responsible AI practices within QA teams

ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring
• Identifying question types and developing answer strategies
• Analyzing topic weight distribution across CTFL syllabus chapters
• Completing practice exams with sample ISTQB-style questions
• Following a study roadmap and utilizing recommended resources

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

• Designing test cases from a sample requirements document
• Generating and refining test scenarios using AI
• Automating selected tests with self-healing tools
• Reporting defects and performing AI-assisted root cause analysis
• Conducting a retrospective on integrating AI into daily QA practice