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).
• Differentiating testing, debugging, and quality control.
• The psychology behind 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 in Waterfall, Agile, DevOps, and CI/CD environments.
• Test levels: unit, integration, system, and acceptance.
• Shift-left and shift-right testing strategies.
• Traceability between requirements and test cases.

Module 3: Static Testing Techniques

• Conducting reviews, walkthroughs, and inspections.
• Performing static analysis using automated tools.
• Checklist-based and role-based reviewing methods.
• Formal and informal review techniques.
• Incorporating 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

• The 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

• Methods for test planning and estimation.
• Identifying, assessing, and mitigating risks.
• Monitoring, controlling, and reporting on tests.
• Defining test completion criteria and exit conditions.
• Developing ISTQB-aligned test strategy and policy documents.

Module 7: Test Tools and Automation Fundamentals

• Classifying test tools according to ISTQB categories.
• Benefits and risks associated with test automation.
• Selecting tools: comparing open-source and commercial solutions.
• Introduction to Selenium, Playwright, and Cypress.
• Constructing a basic automated test suite.

Module 8: Introduction to AI in Quality Assurance

• Core AI and machine learning concepts for testers.
• Taxonomy: applying AI to testing versus testing AI systems.
• Current AI testing landscape: opportunities and limitations.
• Quality characteristics specific to AI-based systems.
• Overview of the ISTQB CT-AI syllabus and its relevance.

Module 9: AI-Assisted Test Case Generation

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

Module 10: AI-Assisted Test Automation

• Self-healing test automation using Katalon Studio AI.
• AI-driven object recognition and element location.
• 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

• 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 efforts using historical defect data.

Module 12: AI Tools Evaluation and CI/CD Integration

• Criteria for evaluating AI testing tools.
• Conducting ROI analysis and developing 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.

Module 13: Ethical Considerations in AI-Driven Testing

• Addressing bias and fairness in AI-generated test data.
• Privacy concerns associated with cloud-based AI tools.
• Ensuring transparency and explainability in AI testing decisions.
• Governance and compliance considerations.
• Adopting responsible AI practices for QA teams.

Module 14: ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring.
• Strategies for different question types.
• Topic weight distribution across CTFL syllabus chapters.
• Practice exam featuring sample ISTQB-style questions.
• Creating a study roadmap and utilizing 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 practice.