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

Foundations of Quality Assurance and Testing

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

Software Development Lifecycle and Testing

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

Static Testing Techniques

• Reviews, walkthroughs, and inspections
• Automated static analysis using specialized tools
• Checklist-driven and role-based reviewing methods
• Formal and informal review techniques
• Incorporating static testing into Agile workflows

Test Techniques

• Black-box techniques: equivalence partitioning and boundary value analysis
• Decision table testing and state transition testing
• Use case testing and exploratory testing
• White-box techniques: 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
• Root cause analysis methodologies
• Defect metrics and trend analysis

Test Management and Risk-Based Testing

• Test planning and estimation methodologies
• Identifying, assessing, and mitigating risks
• Test monitoring, control, and reporting mechanisms
• Defining test completion criteria and exit conditions
• Developing ISTQB-aligned test strategy and policy documents

Test Tools and Automation Fundamentals

• Categorizing test tools based on ISTQB tool categories
• Advantages and risks associated with test automation
• Tool selection: comparing open-source vs. commercial solutions
• Overview of Selenium, Playwright, and Cypress
• Constructing a basic automated test suite

Introduction to AI in Quality Assurance

• AI and machine learning concepts relevant to testers
• Differentiating between AI for testing and testing of AI systems
• The current AI testing landscape: opportunities and constraints
• Quality attributes for AI-based systems
• Overview of the ISTQB CT-AI syllabus and its relevance

AI-Assisted Test Case Generation

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

AI-Assisted Test Automation

• Self-healing test automation via Katalon Studio AI
• AI-driven object recognition and element location
• Visual regression testing using Applitools Eyes
• Enhancing Selenium with AI plugins for resilient automation
• 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
• AI-assisted root cause analysis
• Quality risk scoring and test gap analytics
• Leveraging historical defect data to prioritize testing efforts

AI Tools 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 through metrics

Ethical Considerations in AI-Driven Testing

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

ISTQB CTFL Exam Preparation

• CTFL v4.0 exam structure, duration, and scoring mechanisms
• Question types and effective answer strategies
• Distribution of topic weights across CTFL syllabus chapters
• Practice exam featuring sample ISTQB-style questions
• Study roadmap and recommended learning resources

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

• Designing test cases from 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 practices