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

Quality Assurance and Testing Foundations

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

SDLC and Testing Integration

• 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

• Reviews, walkthroughs, and inspections.
• Performing static analysis with automated tools.
• Checklist-based and role-based review methods.
• Formal versus informal review techniques.
• Incorporating static testing into Agile workflows.

Test Design Techniques

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

Defect Management

• The defect lifecycle: from detection and reporting to triage, resolution, and closure.
• Creating effective defect reports using JIRA.
• Classifying defect severity versus priority.
• Techniques for root cause analysis.
• Tracking defect metrics and analyzing trends.

Test Management and Risk-Based Strategies

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

Test Tools and Automation Basics

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

Introducing AI to Quality Assurance

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

AI-Assisted Test Case Generation

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

AI-Assisted Test Automation

• Implementing self-healing 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.
• Minimizing maintenance overhead through intelligent locators.

AI for Defect Prediction and Analysis

• Selecting predictive tests with Launchable and Sealights.
• Clustering failures and detecting anomalies using ReportPortal.
• Conducting AI-assisted root cause analysis.
• Performing quality risk scoring and test gap analytics.
• Prioritizing testing based on historical defect data.

AI Tool Evaluation and CI/CD Integration

• Establishing criteria for evaluating AI testing tools.
• Analyzing ROI and developing adoption strategies.
• Integrating AI tools into Jenkins, GitHub Actions, and GitLab CI.
• Designing pipelines to determine optimal timing for AI-powered tests.
• Measuring the effectiveness of AI testing through 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.
• Considerations for governance and compliance.
• Implementing responsible AI practices within QA teams.

ISTQB CTFL Exam Preparation

• Understanding the CTFL v4.0 exam structure, duration, and scoring.
• Strategies for various question types.
• Distribution of topic weight across the CTFL syllabus.
• Practice exams with ISTQB-style sample questions.
• Developing a study roadmap and selecting recommended 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 using self-healing tools.
• Reporting defects and performing AI-assisted root cause analysis.
• Conducting a retrospective on integrating AI into daily QA practices.