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

Module 1: Foundations of Quality Assurance and Testing

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

Module 2: Software Development Lifecycle and Testing

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

Module 3: Static Testing Techniques

• Conducting reviews, walkthroughs, and inspections.
• Static analysis using automated tools.
• Checklist-based and role-based reviewing methods.
• Formal and informal review techniques.
• Integrating static testing into Agile workflows.

Module 4: 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.

Module 5: Defect Management

• Defect lifecycle: detection, reporting, triage, resolution, and closure.
• Writing effective defect reports using JIRA.
• Classifying defect severity versus priority.
• Root cause analysis techniques.
• Analyzing defect metrics and trends.

Module 6: Test Management and Risk-Based Testing

• Test planning and estimation methods.
• Risk identification, assessment, and mitigation.
• Test monitoring, control, and reporting.
• Defining test completion criteria and exit conditions.
• ISTQB-aligned test strategy and test policy documentation.

Module 7: Test Tools and Automation Fundamentals

• Classification of test tools (ISTQB tool categories).
• Benefits and risks associated with test automation.
• Selecting tools: comparing open-source versus commercial solutions.
• Introduction to Selenium, Playwright, and Cypress.
• Building a basic automated test suite.

Module 8: Introduction to AI in Quality Assurance

• Core AI and machine learning concepts for testers.
• Taxonomy: AI used for testing versus testing AI systems.
• The current AI testing landscape: opportunities and limitations.
• Quality characteristics for AI-based systems.
• Overview and relevance of the ISTQB CT-AI syllabus.

Module 9: 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.

Module 10: AI-Assisted Test Automation

• Self-healing test automation with Katalon Studio AI.
• AI-driven object recognition and element location.
• Visual regression testing using Applitools Eyes.
• Enhancing Selenium with AI plugins for resilient automation.
• Reducing maintenance overhead with intelligent locators.

Module 11: 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.

Module 12: AI Tools Evaluation and CI/CD Integration

• Criteria for evaluating AI testing tools.
• ROI analysis and adoption strategy.
• Integrating AI testing tools into Jenkins, GitHub Actions, and GitLab CI.
• Pipeline design: determining where and when to run AI-powered tests.
• Measuring the effectiveness of AI testing through key metrics.

Module 13: Ethical Considerations in AI-Driven Testing

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

Module 14: ISTQB CTFL Exam Preparation

• CTFL v4.0 exam structure, duration, and scoring methodology.
• Question types and effective answer strategies.
• Topic weight distribution across CTFL syllabus chapters.
• Practice exam featuring sample ISTQB-style questions.
• Study roadmap and recommended resources.

Module 15: 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.