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

Quality Assurance and Testing Basics

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

SDLC Integration and Testing

• Stages of the Software Testing Life Cycle (STLC)
• Testing methodologies 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
• Checklist-driven and role-based reviewing techniques
• Formal versus informal review procedures
• Incorporating static testing into Agile workflows

Test Design Techniques

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

Defect Management

• 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 analysis

Test Management and Risk-Based Strategies

• 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 Essentials

• Categorization of test tools (ISTQB tool categories)
• Advantages and risks associated with test automation
• Tool selection: comparing open-source and commercial solutions
• Overview of Selenium, Playwright, and Cypress
• Creating a foundational automated test suite

Overview of AI in Quality Assurance

• AI and machine learning concepts for testers
• Taxonomy: AI for testing versus testing AI systems
• The current AI testing landscape: opportunities and constraints
• Quality attributes for AI-based systems
• ISTQB CT-AI syllabus overview and its relevance

AI-Supported Test Case Creation

• Drafting test cases using LLMs (ChatGPT, Claude, Copilot)
• Prompt engineering techniques for generating test scenarios
• Translating user stories and acceptance criteria into test cases
• Evaluating and validating AI-generated test cases
• Platforms: Testim, Mabl, and AI-native test generation tools

AI-Supported Test Automation

• Self-healing test automation with Katalon Studio AI
• AI-driven object recognition and element identification
• Visual regression testing using Applitools Eyes
• Resilient automation with Selenium and AI plugins
• Reducing maintenance burden through intelligent locators

AI for Defect Forecasting and Analysis

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

AI Tool Evaluation and CI/CD Integration

• Criteria for assessing AI testing tools
• ROI analysis and 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 via metrics

Ethical Aspects of AI-Driven Testing

• Bias and fairness in AI-generated test data
• Privacy considerations when utilizing cloud-based AI tools
• Transparency and explainability of AI testing decisions
• Governance and compliance factors
• Responsible AI practices for QA teams

ISTQB CTFL Examination Prep

• CTFL v4.0 exam format, duration, and scoring system
• Question types and strategic answering techniques
• Topic weight distribution across CTFL syllabus chapters
• Practice exam featuring sample ISTQB-style questions
• Study roadmap and suggested resources

Capstone: Comprehensive 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: Embedding AI into daily QA practices