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Course Outline
Module 1: Principles of Quality Assurance and Testing
- Definitions of quality, assurance, and testing activities
- Core testing principles as defined in ISTQB CTFL v4.0
- Distinguishing testing from debugging and quality control
- Behavioral aspects of the testing process
- Organizational roles and responsibilities within QA functions
Module 2: Software Development Lifecycle and Test Integration
- Key phases of the Software Testing Life Cycle (STLC)
- Testing methodologies across Waterfall, Agile, DevOps, and CI/CD frameworks
- Hierarchy of test levels: unit, integration, system, and acceptance
- Strategies for early (shift-left) and late (shift-right) testing
- Maintaining requirements-to-test traceability
Module 3: Static Testing Methodologies
- Processes for reviews, walkthroughs, and inspections
- Application of automated static analysis tools
- Structured reviewing methods using checklists and defined roles
- Differentiating formal versus informal review procedures
- Incorporating static testing into Agile development cycles
Module 4: Dynamic Test Design Techniques
- Black-box methods: equivalence partitioning and boundary value analysis
- Logic-driven approaches: decision table and state transition testing
- Use case and exploratory testing methodologies
- White-box techniques focusing on statement and decision coverage
- Experience-based techniques, including error guessing
Module 5: Defect Tracking and Management
- Defect lifecycle stages: detection, reporting, triage, resolution, and closure
- Effective defect documentation standards using JIRA
- Distinguishing between defect severity and priority classifications
- Methodologies for root cause analysis
- Analyzing defect metrics and identifying trends
Module 6: Test Management and Risk-Based Approaches
- Strategies for test planning and effort estimation
- Risk identification, assessment, and mitigation protocols
- Monitoring test execution and generating status reports
- Establishing test completion criteria and exit conditions
- Developing ISTQB-aligned test strategies and policies
Module 7: Test Tools and Automation Fundamentals
- ISTQB classification of software testing tools
- Evaluating the benefits and risks associated with test automation
- Selecting appropriate solutions: open-source versus commercial options
- Overview of Selenium, Playwright, and Cypress frameworks
- Constructing foundational automated test suites
Module 8: Fundamentals of Artificial Intelligence in Quality Assurance
- AI and machine learning concepts relevant to testing professionals
- Taxonomy: AI applications for testing versus testing of AI systems
- Current landscape of AI in testing: capabilities and constraints
- Quality attributes specific to AI-based systems
- Overview of the ISTQB CT-AI syllabus and its applicability
Module 9: AI-Enabled Test Case Development
- Leveraging Large Language Models (ChatGPT, Claude, Copilot) for draft generation
- Prompt engineering strategies for scenario creation
- Translating user stories and acceptance criteria into test cases
- Validation protocols for AI-generated test artifacts
- Evaluating platforms such as Testim, Mabl, and AI-native tools
Module 10: AI-Enhanced Test Automation
- Self-healing automation capabilities within Katalon Studio AI
- AI-driven object recognition and element location strategies
- Visual regression testing using Applitools Eyes
- Implementing resilient automation with Selenium AI plugins
- Minimizing maintenance efforts through intelligent locators
Module 11: AI for Defect Prediction and Analysis
- Predictive test selection using Launchable and Sealights
- Failure clustering and anomaly detection via ReportPortal
- AI-assisted methodologies for root cause analysis
- Quality risk scoring and identification of test gaps
- Leveraging historical defect data to prioritize testing efforts
Module 12: Evaluation and Integration of AI Testing Tools
- Criteria for assessing AI testing solutions
- Return on investment (ROI) analysis and adoption planning
- Integration with CI/CD pipelines including Jenkins, GitHub Actions, and GitLab CI
- Pipeline architecture: determining optimal execution points for AI tests
- Measuring effectiveness through key performance metrics
Module 13: Ethical Standards in AI-Driven Testing
- Addressing bias and ensuring fairness in AI-generated test data
- Data privacy considerations for cloud-based AI services
- Ensuring transparency and explainability of AI decisions
- Governance and regulatory compliance requirements
- Implementing responsible AI practices within QA teams
Module 14: ISTQB CTFL Certification Preparation
- CTFL v4.0 exam format, duration, and grading criteria
- Strategies for various question types
- Weight distribution across ISTQB syllabus chapters
- Comprehensive practice exams with sample questions
- Structured study roadmap and recommended learning resources
Module 15: Capstone Project: Integrated AI-Enhanced Testing Workflow
- Developing test cases from provided requirements documentation
- Utilizing AI for scenario generation and refinement
- Executing automation with self-healing tools
- Reporting defects and performing AI-assisted root cause analysis
- Retrospective analysis on integrating AI into daily QA operations
Requirements
- Fundamental comprehension of software engineering principles and nomenclature
- Core knowledge of software validation methodologies
- No previous ISTQB credential or formal quality assurance instruction is necessary
Intended Audience
- Quality assurance personnel and software testers pursuing ISTQB Foundation Level credentials for government projects
- Test engineers aiming to incorporate artificial intelligence solutions into their operational processes
- Groups moving from informal testing practices to established quality assurance standards
21 Hours
