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

Foundations of Quality Assurance and Testing

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

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.
• Establishing traceability between requirements and test cases.

Static Testing Techniques

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

Test Techniques

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

Defect Management

• The defect lifecycle: detection, reporting, triage, resolution, and closure.
• Crafting effective defect reports using JIRA.
• Classifying defect severity versus priority.
• Applying root cause analysis techniques.
• Analyzing defect metrics and trends.

Test Management and Risk-Based Testing

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

Test Tools and Automation Fundamentals

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

Introduction to AI in Quality Assurance

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

AI-Assisted Test Case Generation

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

AI-Assisted Test Automation

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

AI for Defect Prediction and Analysis

• Executing predictive test selection with Launchable and Sealights.
• Clusterizing failures and detecting anomalies with ReportPortal.
• Performing AI-assisted root cause analysis.
• Scoring quality risks and analyzing test gaps.
• Prioritizing testing efforts using historical defect data.

AI Tools Evaluation and CI/CD Integration

• Criteria for evaluating AI testing tools.
• Conducting ROI analysis and planning adoption strategies.
• Integrating AI testing tools into Jenkins, GitHub Actions, and GitLab CI.
• Designing pipelines: determining when and where to execute AI-powered tests.
• Measuring the effectiveness of AI testing using metrics.

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 in AI testing decisions.
• Considering governance and compliance requirements.
• Adopting responsible AI practices for QA teams.

ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring.
• Strategies for handling question types and answering effectively.
• Reviewing topic weight distribution across CTFL syllabus chapters.
• Taking a practice exam with sample ISTQB-style questions.
• Following a study roadmap and utilizing recommended resources.

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

• Designing test cases from a sample requirements document.
• Generating and refining test scenarios using AI.
• Automating selected tests with self-healing tools.
• Reporting defects and conducting AI-assisted root cause analysis.
• Retrospective: integrating AI into daily QA practice.