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

Module 1: Fundamentals of Quality Assurance and Testing

• Definitions and distinctions among quality, quality assurance, and testing
• Application of the seven testing principles (ISTQB CTFL v4.0)
• Differentiation between testing, debugging, and quality control
• Cognitive factors influencing testing processes
• Organizational roles and responsibilities within QA functions

Module 2: Software Development Lifecycle and Testing Integration

• Phases of the Software Testing Life Cycle (STLC)
• Testing methodologies across Waterfall, Agile, DevOps, and CI/CD environments
• Test levels: unit, integration, system, and acceptance testing
• Implementation of shift-left and shift-right testing strategies
• Establishing traceability between requirements and test cases for government compliance

Module 3: Static Testing Methodologies

• Procedures for reviews, walkthroughs, and inspections
• Static analysis through automated tools
• Checklist-based and role-based review processes
• Formal versus informal review techniques
• Incorporating static testing into Agile development workflows

Module 4: Test Design Techniques

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

Module 5: Defect Lifecycle and Management

• Defect management workflow: detection, reporting, triage, resolution, and closure
• Effective defect reporting standards using JIRA
• Classification of defect severity and priority
• Root cause analysis methodologies
• Analyzing defect metrics and trends for quality improvement

Module 6: Test Management and Risk-Based Strategies

• Test planning and estimation techniques
• Risk identification, assessment, and mitigation planning
• Monitoring, controlling, and reporting on test activities
• Establishing test completion criteria and exit conditions
• Developing ISTQB-aligned test strategies and policies for government projects

Module 7: Test Tools and Automation Fundamentals

• Classification of test tools according to ISTQB categories
• Evaluation of benefits and risks associated with test automation
• Tool selection criteria: open-source versus commercial solutions
• Overview of Selenium, Playwright, and Cypress platforms
• Construction of foundational automated test suites

Module 8: Introduction to Artificial Intelligence in Quality Assurance

• Core concepts of AI and machine learning for QA professionals
• Taxonomy: AI for testing versus testing of AI systems
• Current landscape of AI in testing: opportunities and limitations
• Quality characteristics specific to AI-based systems
• Overview of the ISTQB CT-AI syllabus and its relevance for government applications

Module 9: AI-Assisted Test Case Generation

• Leveraging Large Language Models (LLMs) such as ChatGPT, Claude, and Copilot for test case drafting
• Prompt engineering techniques for scenario generation
• Transforming user stories and acceptance criteria into test cases
• Validation and review of AI-generated test artifacts
• Overview of platforms including Testim, Mabl, and AI-native test generation tools

Module 10: AI-Assisted Test Automation

• Self-healing test automation capabilities with Katalon Studio AI
• AI-driven object recognition and element location strategies
• Visual regression testing using Applitools Eyes
• Enhancing Selenium automation with AI plugins for resilience
• 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
• Utilizing historical defect data to prioritize testing efforts

Module 12: AI Tool Evaluation and CI/CD Integration

• Criteria for evaluating AI testing tools for suitability
• Return on investment (ROI) analysis and adoption strategies
• Integration of AI testing tools into Jenkins, GitHub Actions, and GitLab CI
• Pipeline design: determining optimal deployment points for AI-powered tests
• Measuring AI testing effectiveness through defined metrics

Module 13: Ethical Considerations in AI-Driven Testing

• Addressing bias and fairness in AI-generated test data
• Privacy implications of using cloud-based AI tools in government contexts
• Ensuring transparency and explainability of AI testing decisions
• Compliance with governance and regulatory standards
• Implementing responsible AI practices within QA teams

Module 14: ISTQB CTFL Exam Preparation

• CTFL v4.0 exam structure, duration, and scoring criteria
• Analysis of question types and answering strategies
• Topic weight distribution across the CTFL syllabus
• Practice examination with sample ISTQB-style questions
• Study roadmap and recommended preparatory resources

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

• Designing test cases based on sample requirements documentation
• Utilizing AI to generate and refine test scenarios
• Automating selected tests using self-healing tools
• Reporting defects and conducting AI-assisted root cause analysis
• Retrospective analysis on integrating AI into daily QA operations for government missions