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

Foundations of Quality Assurance and Testing

• Defining quality, quality assurance, and the testing process
• The seven testing principles (ISTQB CTFL v4.0)
• Distinguishing testing from debugging and quality control
• The psychological aspects of effective testing
• Roles and responsibilities within a QA team

Software Development Lifecycle and Testing

• Stages of the Software Testing Life Cycle (STLC)
• Testing approaches across Waterfall, Agile, DevOps, and CI/CD models
• 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
• Applying checklist-based and role-based review methods
• Formal and informal review techniques
• Incorporating static testing into Agile workflows

Test Techniques

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

Defect Management

• Managing the defect lifecycle: detection, reporting, triage, resolution, and closure
• Crafting effective defect reports using JIRA
• Classifying defect severity versus priority
• Applying root cause analysis techniques
• 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 on testing activities
• Defining test completion criteria and exit conditions
• Creating ISTQB-aligned test strategy and policy documents

Test Tools and Automation Fundamentals

• Categorizing test tools according to ISTQB standards
• Understanding the benefits and risks of test automation
• Selecting tools: comparing open-source and commercial solutions
• Introduction to Selenium, Playwright, and Cypress
• Developing a basic automated test suite

Introduction to AI in Quality Assurance

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

AI-Assisted Test Case Generation

• Utilizing LLMs (ChatGPT, Claude, Copilot) to draft test cases
• Prompt engineering techniques for generating test scenarios
• Transforming user stories and acceptance criteria into test cases
• Reviewing and validating test cases generated by AI
• Exploring platforms: Testim, Mabl, and AI-native test generation tools

AI-Assisted Test Automation

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

AI for Defect Prediction and Analysis

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

AI Tools Evaluation and CI/CD Integration

• Establishing criteria for evaluating AI testing tools
• Conducting ROI analysis and defining adoption strategies
• 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

Ethical Considerations in AI-Driven Testing

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

ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring
• Strategies for handling various question types
• Reviewing topic weight distribution across CTFL syllabus chapters
• Taking a practice exam with sample ISTQB-style questions
• Developing a study roadmap and identifying recommended resources

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

• Designing test cases based on a sample requirements document
• Using 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