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

Basics of Quality Assurance and Testing

• Defining quality, QA, and testing
• The seven testing principles (ISTQB CTFL v4.0)
• Distinguishing testing, debugging, and quality control
• The psychological aspects of testing
• Duties and roles within a QA team

Software Development Lifecycle and Testing Integration

• Stages of the Software Testing Life Cycle (STLC)
• Testing methods in Waterfall, Agile, DevOps, and CI/CD environments
• Test levels: unit, integration, system, and acceptance
• Shift-left and shift-right testing strategies
• Traceability linking requirements to test cases

Static Testing Methods

• Conducting reviews, walkthroughs, and inspections
• Performing static analysis with automated tools
• Review processes based on checklists and roles
• Formal versus informal review techniques
• Embedding 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

• The defect lifecycle: identification, reporting, triage, resolution, and closure
• Drafting effective defect reports using JIRA
• Classifying defect severity versus priority
• Techniques for root cause analysis
• Defect metrics and trend evaluation

Test Management and Risk-Based Approaches

• Methods for test planning and estimation
• Identifying, assessing, and mitigating risks
• Monitoring, controlling, and reporting on tests
• Establishing test completion criteria and exit conditions
• ISTQB-compliant test strategy and policy documentation

Test Tools and Automation Basics

• Categorization of test tools (ISTQB classifications)
• Advantages and potential risks of test automation
• Tool selection: comparing open-source and commercial options
• Overview of Selenium, Playwright, and Cypress
• Constructing a fundamental automated test suite

Introducing AI into Quality Assurance

• AI and machine learning concepts tailored for testers
• Distinguishing AI for testing from testing AI systems
• The current state of AI testing: possibilities and constraints
• Quality attributes for AI-based systems
• Overview of the ISTQB CT-AI syllabus and its relevance

AI-Supported Test Case Creation

• Drafting test cases using LLMs (ChatGPT, Claude, Copilot)
• Prompt engineering strategies for generating test scenarios
• Transforming user stories and acceptance criteria into test cases
• Reviewing and verifying AI-generated test cases
• Platforms: Testim, Mabl, and AI-native test generation solutions

AI-Enhanced Test Automation

• Self-healing test automation using Katalon Studio AI
• AI-driven object recognition and element identification
• Visual regression testing via Applitools Eyes
• Integrating AI plugins with Selenium for resilient automation
• Mitigating maintenance burdens through intelligent locators

AI for Defect Prediction and Analysis

• Predictive test selection using Launchable and Sealights
• Clustering failures and detecting anomalies with ReportPortal
• AI-assisted root cause analysis
• Scoring quality risks and analyzing test gaps
• Utilizing historical defect data to prioritize testing efforts

Evaluating AI Tools and CI/CD Integration

• Standards for assessing AI testing tools
• ROI analysis and adoption strategies
• Integrating AI testing tools into Jenkins, GitHub Actions, and GitLab CI
• Designing pipelines: determining when and where to execute AI-powered tests
• Measuring the effectiveness of AI testing using metrics

Ethical Aspects in AI-Driven Testing

• Bias and fairness in AI-generated test data
• Privacy issues related to cloud-based AI tools
• Transparency and explainability in AI testing decisions
• Considerations for governance and compliance
• Responsible AI practices for QA teams

ISTQB CTFL Exam Readiness

• Structure, duration, and scoring of the CTFL v4.0 exam
• Question formats and answer strategies
• Topic weight distribution across CTFL syllabus chapters
• Practice exam featuring sample ISTQB-style questions
• Study roadmap and recommended learning resources

Capstone: Complete AI-Enhanced Testing Workflow

• Designing test cases from a sample requirements document
• Generating and refining test scenarios with AI
• Automating selected tests using self-healing tools
• Reporting defects and conducting AI-assisted root cause analysis
• Retrospective: integrating AI into daily QA practices