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

Module 1: Quality Assurance and Testing Fundamentals

• Establishing definitions for quality, quality assurance, and testing.
• Exploring the seven testing principles (ISTQB CTFL v4.0).
• Clarifying distinctions between testing, debugging, and quality control.
• Understanding the psychology behind testing.
• Defining roles and responsibilities within a QA team.

Module 2: Software Development Lifecycle and Testing Integration

• Examining the phases of the Software Testing Life Cycle (STLC).
• Comparing testing approaches in Waterfall, Agile, DevOps, and CI/CD environments.
• Differentiating test levels: unit, integration, system, and acceptance.
• Implementing shift-left and shift-right testing strategies.
• Establishing traceability between requirements and test cases.

Module 3: Static Testing Methodologies

• Conducting reviews, walkthroughs, and inspections.
• Utilizing static analysis tools for automated checks.
• Implementing checklist-based and role-based review processes.
• Applying formal and informal review techniques.
• Integrating static testing into Agile workflows.

Module 4: Advanced Test Techniques

• Mastering black-box techniques: equivalence partitioning and boundary value analysis.
• Employing decision table and state transition testing.
• Executing use case and exploratory testing.
• Applying white-box techniques: statement and decision coverage.
• Utilizing experience-based techniques and error guessing.

Module 5: Defect Management Strategies

• Managing the defect lifecycle: detection, reporting, triage, resolution, and closure.
• Writing clear and effective defect reports using JIRA.
• Classifying defect severity versus priority.
• Performing root cause analysis techniques.
• Analyzing defect metrics and trends.

Module 6: Test Management and Risk-Based Testing

• Developing test plans and estimation methods.
• Identifying, assessing, and mitigating risks.
• Monitoring, controlling, and reporting on test activities.
• Defining test completion criteria and exit conditions.
• Creating ISTQB-aligned test strategy and policy documents.

Module 7: Test Tools and Automation Essentials

• Classifying test tools according to ISTQB categories.
• Weighing the benefits and risks of test automation.
• Selecting tools: comparing open-source vs. commercial options.
• Gaining an introduction to Selenium, Playwright, and Cypress.
• Constructing a basic automated test suite.

Module 8: Introduction to AI in Quality Assurance

• Understanding AI and machine learning concepts relevant to testers.
• Exploring the taxonomy: AI for testing versus testing AI systems.
• Assessing the current AI testing landscape: opportunities and limitations.
• Evaluating quality characteristics for AI-based systems.
• Reviewing the ISTQB CT-AI syllabus and its relevance.

Module 9: AI-Assisted Test Case Generation

• Using LLMs (ChatGPT, Claude, Copilot) to draft test cases.
• Applying prompt engineering techniques for scenario generation.
• Transforming user stories and acceptance criteria into test cases.
• Reviewing and validating AI-generated content.
• Exploring platforms like Testim, Mabl, and AI-native generation tools.

Module 10: AI-Assisted Test Automation

• Implementing self-healing test automation with Katalon Studio AI.
• Utilizing AI-driven object recognition and element location.
• Conducting visual regression testing with Applitools Eyes.
• Enhancing Selenium with AI plugins for resilient automation.
• Reducing maintenance overhead through intelligent locators.

Module 11: AI for Defect Prediction and Analysis

• Predicting test selection with Launchable and Sealights.
• Cluster analysis and anomaly detection using ReportPortal.
• Facilitating AI-assisted root cause analysis.
• Evaluating quality risk scoring and test gap analytics.
• Prioritizing testing efforts using historical defect data.

Module 12: Evaluating AI Tools and CI/CD Integration

• Establishing criteria for evaluating AI testing tools.
• Conducting ROI analysis and developing adoption strategies.
• Integrating AI tools into Jenkins, GitHub Actions, and GitLab CI.
• Designing pipelines to determine when and where to execute AI-powered tests.
• Measuring AI testing effectiveness through key metrics.

Module 13: Ethical Considerations in AI-Driven Testing

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

Module 14: ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring.
• Mastering question types and answer strategies.
• Analyzing topic weight distribution across CTFL syllabus chapters.
• Taking practice exams with sample ISTQB-style questions.
• Receiving a study roadmap and resource recommendations.

Module 15: Capstone: End-to-End AI-Enhanced Testing Workflow

• Designing test cases from a sample requirements document.
• Employing AI to generate and refine test scenarios.
• Automating selected tests using self-healing tools.
• Reporting defects and conducting AI-assisted root cause analysis.
• Reflecting on integrating AI into daily QA practices.