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Duration 21 hours
Course Outline
Foundations of Quality Assurance and Testing
- Defining quality, quality assurance, and testing concepts
- The seven testing principles (ISTQB CTFL v4.0)
- Distinguishing testing, debugging, and quality control
- The psychological aspects of testing
- Roles and responsibilities within a QA team
Software Development Lifecycle and Testing
- Stages of the Software Testing Life Cycle (STLC)
- Testing approaches in Waterfall, Agile, DevOps, and CI/CD environments
- Test levels: unit, integration, system, and acceptance
- Strategies for shift-left and shift-right testing
- Establishing traceability between requirements and test cases
Static Testing Techniques
- Conducting reviews, walkthroughs, and inspections
- Performing static analysis with automated tools
- Applying checklist-based and role-based reviewing methods
- Formal versus informal review techniques
- Incorporating static testing into Agile workflows
Test Techniques
- Black-box methods: equivalence partitioning and boundary value analysis
- Testing via decision tables and state transitions
- 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
- Techniques for root cause analysis
- 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 tests
- Setting test completion criteria and exit conditions
- Developing ISTQB-aligned test strategy and policy documents
Test Tools and Automation Fundamentals
- Categorizing test tools based on ISTQB classifications
- Weighing the benefits and risks of test automation
- Selecting tools: comparing open-source and commercial solutions
- Overview of Selenium, Playwright, and Cypress
- Creating a basic automated test suite
Introduction to AI in Quality Assurance
- AI and machine learning concepts relevant to testers
- Distinguishing between AI for testing and testing AI systems
- Current landscape of AI testing: 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 such as ChatGPT, Claude, and Copilot
- Applying prompt engineering to generate test scenarios
- Translating user stories and acceptance criteria into test cases
- Reviewing and validating AI-generated test outputs
- Exploring platforms like Testim, Mabl, and other AI-native tools
AI-Assisted Test Automation
- Implementing self-healing automation with Katalon Studio AI
- Utilizing AI for object recognition and element location
- Performing visual regression testing with Applitools Eyes
- Enhancing Selenium with AI plugins for resilient automation
- Reducing maintenance efforts through intelligent locators
AI for Defect Prediction and Analysis
- Selecting predictive tests using Launchable and Sealights
- Clustering failures and detecting anomalies with ReportPortal
- Conducting AI-assisted root cause analysis
- Scoring quality risks and analyzing test gaps
- Prioritizing testing using historical defect data
AI Tools Evaluation and CI/CD Integration
- Establishing criteria for evaluating AI testing tools
- Analyzing ROI and planning adoption strategies
- Integrating AI tools into Jenkins, GitHub Actions, and GitLab CI
- Designing pipelines: determining when and where to run 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
- Navigating privacy concerns with cloud-based AI tools
- Ensuring transparency and explainability in AI testing decisions
- Considering governance and compliance requirements
- Adopting responsible AI practices within QA teams
ISTQB CTFL Exam Preparation
- Understanding the CTFL v4.0 exam structure, duration, and scoring
- Identifying question types and developing answer strategies
- Analyzing topic weight distribution across the CTFL syllabus
- Completing a practice exam with ISTQB-style sample 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
- Using AI to generate and refine test scenarios
- Automating selected tests with self-healing tools
- Reporting defects and performing AI-assisted root cause analysis
- Conducting a retrospective on integrating AI into daily QA practices
Requirements
- A basic grasp of software development concepts and related terminology.
- Introductory familiarity with software testing practices.
- No prior ISTQB certification or formal QA training is necessary.
Target Audience
- QA specialists and software testers preparing for the ISTQB Foundation Level certification.
- Test engineers looking to integrate AI-driven tools into their existing workflows.
- Teams seeking to transition from ad-hoc methods to structured QA frameworks.
