Mastering AI-Powered Test Automation for Future-Proof Quality Engineering
Course Format & Delivery Details Learn on Your Terms, With Zero Risk and Maximum Support
This course is designed for quality engineering professionals who demand clarity, control, and immediate applicability. You gain full self-paced access the moment you enroll, with on-demand learning that adapts to your schedule and time zone. There are no fixed dates, deadlines, or live sessions to attend. You progress at your own speed, revisiting concepts as needed, with lifetime access to all materials. Fast Results, Anytime Access, Career-Ready Outcomes
Most learners report applying core techniques within the first 48 hours. The average completion time is 6 weeks with 5–7 hours of weekly engagement, though many complete key implementation modules in under 10 days. Every lesson is mobile-friendly, ensuring you can learn during commutes, on breaks, or from any location with internet access, 24/7. Trusted Guidance and Real Instructor Support
You’re not navigating this alone. The course includes direct instructor support through structured query channels, where experienced quality engineering mentors provide actionable feedback and technical clarification. Whether you’re refining test strategies or troubleshooting integration logic, expert guidance is embedded into the learning journey to ensure clarity at every step. Certificate of Completion Issued by The Art of Service
Upon successful completion, you receive a formal Certificate of Completion issued by The Art of Service. This credential is globally recognised and designed to validate mastery of AI-powered testing methodologies. Recruiters and hiring managers across leading technology firms consistently recognise The Art of Service credentials as evidence of deep, practical expertise in modern quality assurance frameworks. No Hidden Fees, Transparent Enrollment, Full Payment Flexibility
The price you see is the price you pay. There are no recurring charges, upsells, or surprise fees. The one-time enrollment fee covers everything, including all future updates and lifetime access. We accept all major payment methods, including Visa, Mastercard, and PayPal, ensuring secure and convenient registration for professionals worldwide. Satisfied or Refunded: Our No-Risk Guarantee
Your success is our priority. If you complete the first two modules and find the content doesn’t meet your expectations, simply request a full refund. This is not a trial. This is a commitment to delivering value you can trust. We remove the risk so you can focus entirely on transformation. What Happens After You Enroll
Once registered, you will receive a confirmation email acknowledging your enrollment. Shortly after, a separate message will deliver your access details, providing entry to the course materials as they are prepared for you. This ensures a smooth, structured onboarding experience aligned with our academic standards. Will This Work for Me? Real Results Across Roles
Whether you’re a manual tester transitioning to automation, a lead test engineer modernising your team’s approach, or a QA architect designing enterprise test strategies, this course meets you where you are. Past participants include: - Senior QA Analysts who used the AI scripting templates to reduce test execution time by 78%
- DevOps Engineers who integrated AI-driven test triggers into their CI/CD pipelines
- Test Managers who restructured their reporting with AI-generated defect pattern analytics
This works even if you have limited prior automation experience, if your organisation uses legacy tools, or if you’re uncertain about integrating AI without disrupting existing workflows. The curriculum is built on phased adoption, with real project templates that scale from small test suites to enterprise systems. Your Learning Experience Is Safe, Structured, and Backed by Real Value
We’ve eliminated the friction that plagues online learning. No confusing navigation. No outdated content. No dead ends. What you get is a meticulously structured, continuously updated program engineered for measurable career ROI. This is not theoretical. This is how top-performing quality engineers operate today.
Extensive and Detailed Course Curriculum
Module 1: Foundations of AI in Quality Engineering - Understanding the shift from traditional testing to intelligent validation
- Why AI is no longer optional in modern software delivery pipelines
- Core definitions: AI, machine learning, generative models, and their role in testing
- Differentiating between test automation and AI-powered test automation
- The evolution of QA: from manual scripts to self-healing test frameworks
- Key drivers of AI adoption in quality engineering organisations
- Common misconceptions about AI in testing and how to avoid them
- The role of data in intelligent test systems
- How AI reduces false positives and improves test reliability
- Analysing real-world case studies of AI-driven QA transformations
Module 2: AI-Powered Test Strategy Frameworks - Designing an AI-ready test strategy from the ground up
- Mapping test coverage to business-critical paths using AI insights
- Integrating risk-based testing with AI probability models
- Building adaptive test suites that evolve with code changes
- Selecting the right application areas for AI automation
- Defining success metrics for AI test initiatives
- Creating an ROI model for AI test implementation
- Establishing governance for AI test assets and model integrity
- Aligning AI testing with compliance and audit requirements
- Developing a phased rollout plan for enterprise adoption
Module 3: Core AI Technologies for Test Automation - Natural Language Processing for test case generation
- Computer vision techniques for UI validation without locators
- Reinforcement learning for dynamic test path exploration
- Deep learning models for anomaly detection in test results
- Generative AI for synthetic test data creation
- Large Language Models for converting requirements to executable tests
- Transfer learning to adapt pre-trained models for domain-specific testing
- Neural networks for predicting test failure likelihood
- Clustering algorithms to group similar test failures
- Time series analysis for performance test anomaly spotting
Module 4: AI-Enhanced Test Design & Planning - Generating test cases from user stories using AI
- Automated boundary value analysis with intelligent input suggestion
- Predicting high-risk test scenarios based on code complexity
- Optimising test suite size using AI-driven prioritisation
- Dynamic test case generation based on production usage patterns
- Using AI to identify overlooked edge cases
- Creating self-documenting test logic with explainable AI
- Incorporating domain knowledge into AI test design
- Designing for test maintainability in AI-augmented systems
- Integrating AI planning into Agile and DevOps workflows
Module 5: Intelligent Test Execution Frameworks - Building self-healing test scripts that auto-correct locators
- Implementing AI-driven test scheduling based on code impact
- Dynamic test execution routing: selecting the right environment
- Parallel test optimisation using AI resource forecasting
- Real-time test execution adjustment based on pass/fail trends
- Failure prediction models that prevent wasted test runs
- Context-aware test execution using session intelligence
- Integrating AI executors into Jenkins and GitLab CI
- Sleeping test elimination: removing obsolete test cases automatically
- Creating resilient execution pipelines for distributed systems
Module 6: AI for Test Data Management - Generating realistic, non-PII test data at scale
- Using AI to mask and anonymise production data for testing
- Dynamic data provisioning based on test context
- Predicting data needs before test execution begins
- Creating data mutation models for negative testing
- Automating data state resets using AI orchestration
- Validating data integrity across microservices with AI
- Synthetic data alignment with business logic rules
- Temporal data modelling for time-sensitive applications
- Data drift detection between test and production environments
Module 7: AI-Driven Test Analysis & Reporting - Automated test result summarisation using natural language generation
- Classifying failures into categories without manual tagging
- Predicting root cause from failure patterns using clustering
- Highlighting regression risks based on historical test data
- Creating executive dashboards powered by AI insights
- Identifying flaky tests automatically
- Correlating test failures with deployment events
- Auto-generating incident reports for failed builds
- Translating technical test data into business impact language
- Implementing anomaly detection in test metrics over time
Module 8: Visual Validation with AI - Pixel-based comparison vs semantic visual testing
- Training models to understand UI meaning, not just pixels
- Handling dynamic content in visual tests using AI masking
- Automated baseline selection and maintenance
- Responsive design validation across device types
- Accessibility checks integrated into visual AI workflows
- Text extraction from UI for validation accuracy
- Layout change detection using spatial reasoning models
- Real-time visual regression feedback in pull requests
- Combining visual AI with performance monitoring
Module 9: API and Service-Level AI Testing - Auto-generating API test suites from OpenAPI specs
- Predicting edge case requests using AI mutation
- Detecting contractual deviations in microservices
- Validating response semantics, not just structure
- AI-based fuzz testing for security and stability
- Monitoring endpoint behaviour drift over time
- Generating negative test payloads from production traffic
- Inferring service dependencies automatically
- Performance baseline prediction for new endpoints
- AI-assisted contract testing in asynchronous systems
Module 10: Performance and Load Testing with AI - AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
Module 1: Foundations of AI in Quality Engineering - Understanding the shift from traditional testing to intelligent validation
- Why AI is no longer optional in modern software delivery pipelines
- Core definitions: AI, machine learning, generative models, and their role in testing
- Differentiating between test automation and AI-powered test automation
- The evolution of QA: from manual scripts to self-healing test frameworks
- Key drivers of AI adoption in quality engineering organisations
- Common misconceptions about AI in testing and how to avoid them
- The role of data in intelligent test systems
- How AI reduces false positives and improves test reliability
- Analysing real-world case studies of AI-driven QA transformations
Module 2: AI-Powered Test Strategy Frameworks - Designing an AI-ready test strategy from the ground up
- Mapping test coverage to business-critical paths using AI insights
- Integrating risk-based testing with AI probability models
- Building adaptive test suites that evolve with code changes
- Selecting the right application areas for AI automation
- Defining success metrics for AI test initiatives
- Creating an ROI model for AI test implementation
- Establishing governance for AI test assets and model integrity
- Aligning AI testing with compliance and audit requirements
- Developing a phased rollout plan for enterprise adoption
Module 3: Core AI Technologies for Test Automation - Natural Language Processing for test case generation
- Computer vision techniques for UI validation without locators
- Reinforcement learning for dynamic test path exploration
- Deep learning models for anomaly detection in test results
- Generative AI for synthetic test data creation
- Large Language Models for converting requirements to executable tests
- Transfer learning to adapt pre-trained models for domain-specific testing
- Neural networks for predicting test failure likelihood
- Clustering algorithms to group similar test failures
- Time series analysis for performance test anomaly spotting
Module 4: AI-Enhanced Test Design & Planning - Generating test cases from user stories using AI
- Automated boundary value analysis with intelligent input suggestion
- Predicting high-risk test scenarios based on code complexity
- Optimising test suite size using AI-driven prioritisation
- Dynamic test case generation based on production usage patterns
- Using AI to identify overlooked edge cases
- Creating self-documenting test logic with explainable AI
- Incorporating domain knowledge into AI test design
- Designing for test maintainability in AI-augmented systems
- Integrating AI planning into Agile and DevOps workflows
Module 5: Intelligent Test Execution Frameworks - Building self-healing test scripts that auto-correct locators
- Implementing AI-driven test scheduling based on code impact
- Dynamic test execution routing: selecting the right environment
- Parallel test optimisation using AI resource forecasting
- Real-time test execution adjustment based on pass/fail trends
- Failure prediction models that prevent wasted test runs
- Context-aware test execution using session intelligence
- Integrating AI executors into Jenkins and GitLab CI
- Sleeping test elimination: removing obsolete test cases automatically
- Creating resilient execution pipelines for distributed systems
Module 6: AI for Test Data Management - Generating realistic, non-PII test data at scale
- Using AI to mask and anonymise production data for testing
- Dynamic data provisioning based on test context
- Predicting data needs before test execution begins
- Creating data mutation models for negative testing
- Automating data state resets using AI orchestration
- Validating data integrity across microservices with AI
- Synthetic data alignment with business logic rules
- Temporal data modelling for time-sensitive applications
- Data drift detection between test and production environments
Module 7: AI-Driven Test Analysis & Reporting - Automated test result summarisation using natural language generation
- Classifying failures into categories without manual tagging
- Predicting root cause from failure patterns using clustering
- Highlighting regression risks based on historical test data
- Creating executive dashboards powered by AI insights
- Identifying flaky tests automatically
- Correlating test failures with deployment events
- Auto-generating incident reports for failed builds
- Translating technical test data into business impact language
- Implementing anomaly detection in test metrics over time
Module 8: Visual Validation with AI - Pixel-based comparison vs semantic visual testing
- Training models to understand UI meaning, not just pixels
- Handling dynamic content in visual tests using AI masking
- Automated baseline selection and maintenance
- Responsive design validation across device types
- Accessibility checks integrated into visual AI workflows
- Text extraction from UI for validation accuracy
- Layout change detection using spatial reasoning models
- Real-time visual regression feedback in pull requests
- Combining visual AI with performance monitoring
Module 9: API and Service-Level AI Testing - Auto-generating API test suites from OpenAPI specs
- Predicting edge case requests using AI mutation
- Detecting contractual deviations in microservices
- Validating response semantics, not just structure
- AI-based fuzz testing for security and stability
- Monitoring endpoint behaviour drift over time
- Generating negative test payloads from production traffic
- Inferring service dependencies automatically
- Performance baseline prediction for new endpoints
- AI-assisted contract testing in asynchronous systems
Module 10: Performance and Load Testing with AI - AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Designing an AI-ready test strategy from the ground up
- Mapping test coverage to business-critical paths using AI insights
- Integrating risk-based testing with AI probability models
- Building adaptive test suites that evolve with code changes
- Selecting the right application areas for AI automation
- Defining success metrics for AI test initiatives
- Creating an ROI model for AI test implementation
- Establishing governance for AI test assets and model integrity
- Aligning AI testing with compliance and audit requirements
- Developing a phased rollout plan for enterprise adoption
Module 3: Core AI Technologies for Test Automation - Natural Language Processing for test case generation
- Computer vision techniques for UI validation without locators
- Reinforcement learning for dynamic test path exploration
- Deep learning models for anomaly detection in test results
- Generative AI for synthetic test data creation
- Large Language Models for converting requirements to executable tests
- Transfer learning to adapt pre-trained models for domain-specific testing
- Neural networks for predicting test failure likelihood
- Clustering algorithms to group similar test failures
- Time series analysis for performance test anomaly spotting
Module 4: AI-Enhanced Test Design & Planning - Generating test cases from user stories using AI
- Automated boundary value analysis with intelligent input suggestion
- Predicting high-risk test scenarios based on code complexity
- Optimising test suite size using AI-driven prioritisation
- Dynamic test case generation based on production usage patterns
- Using AI to identify overlooked edge cases
- Creating self-documenting test logic with explainable AI
- Incorporating domain knowledge into AI test design
- Designing for test maintainability in AI-augmented systems
- Integrating AI planning into Agile and DevOps workflows
Module 5: Intelligent Test Execution Frameworks - Building self-healing test scripts that auto-correct locators
- Implementing AI-driven test scheduling based on code impact
- Dynamic test execution routing: selecting the right environment
- Parallel test optimisation using AI resource forecasting
- Real-time test execution adjustment based on pass/fail trends
- Failure prediction models that prevent wasted test runs
- Context-aware test execution using session intelligence
- Integrating AI executors into Jenkins and GitLab CI
- Sleeping test elimination: removing obsolete test cases automatically
- Creating resilient execution pipelines for distributed systems
Module 6: AI for Test Data Management - Generating realistic, non-PII test data at scale
- Using AI to mask and anonymise production data for testing
- Dynamic data provisioning based on test context
- Predicting data needs before test execution begins
- Creating data mutation models for negative testing
- Automating data state resets using AI orchestration
- Validating data integrity across microservices with AI
- Synthetic data alignment with business logic rules
- Temporal data modelling for time-sensitive applications
- Data drift detection between test and production environments
Module 7: AI-Driven Test Analysis & Reporting - Automated test result summarisation using natural language generation
- Classifying failures into categories without manual tagging
- Predicting root cause from failure patterns using clustering
- Highlighting regression risks based on historical test data
- Creating executive dashboards powered by AI insights
- Identifying flaky tests automatically
- Correlating test failures with deployment events
- Auto-generating incident reports for failed builds
- Translating technical test data into business impact language
- Implementing anomaly detection in test metrics over time
Module 8: Visual Validation with AI - Pixel-based comparison vs semantic visual testing
- Training models to understand UI meaning, not just pixels
- Handling dynamic content in visual tests using AI masking
- Automated baseline selection and maintenance
- Responsive design validation across device types
- Accessibility checks integrated into visual AI workflows
- Text extraction from UI for validation accuracy
- Layout change detection using spatial reasoning models
- Real-time visual regression feedback in pull requests
- Combining visual AI with performance monitoring
Module 9: API and Service-Level AI Testing - Auto-generating API test suites from OpenAPI specs
- Predicting edge case requests using AI mutation
- Detecting contractual deviations in microservices
- Validating response semantics, not just structure
- AI-based fuzz testing for security and stability
- Monitoring endpoint behaviour drift over time
- Generating negative test payloads from production traffic
- Inferring service dependencies automatically
- Performance baseline prediction for new endpoints
- AI-assisted contract testing in asynchronous systems
Module 10: Performance and Load Testing with AI - AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Generating test cases from user stories using AI
- Automated boundary value analysis with intelligent input suggestion
- Predicting high-risk test scenarios based on code complexity
- Optimising test suite size using AI-driven prioritisation
- Dynamic test case generation based on production usage patterns
- Using AI to identify overlooked edge cases
- Creating self-documenting test logic with explainable AI
- Incorporating domain knowledge into AI test design
- Designing for test maintainability in AI-augmented systems
- Integrating AI planning into Agile and DevOps workflows
Module 5: Intelligent Test Execution Frameworks - Building self-healing test scripts that auto-correct locators
- Implementing AI-driven test scheduling based on code impact
- Dynamic test execution routing: selecting the right environment
- Parallel test optimisation using AI resource forecasting
- Real-time test execution adjustment based on pass/fail trends
- Failure prediction models that prevent wasted test runs
- Context-aware test execution using session intelligence
- Integrating AI executors into Jenkins and GitLab CI
- Sleeping test elimination: removing obsolete test cases automatically
- Creating resilient execution pipelines for distributed systems
Module 6: AI for Test Data Management - Generating realistic, non-PII test data at scale
- Using AI to mask and anonymise production data for testing
- Dynamic data provisioning based on test context
- Predicting data needs before test execution begins
- Creating data mutation models for negative testing
- Automating data state resets using AI orchestration
- Validating data integrity across microservices with AI
- Synthetic data alignment with business logic rules
- Temporal data modelling for time-sensitive applications
- Data drift detection between test and production environments
Module 7: AI-Driven Test Analysis & Reporting - Automated test result summarisation using natural language generation
- Classifying failures into categories without manual tagging
- Predicting root cause from failure patterns using clustering
- Highlighting regression risks based on historical test data
- Creating executive dashboards powered by AI insights
- Identifying flaky tests automatically
- Correlating test failures with deployment events
- Auto-generating incident reports for failed builds
- Translating technical test data into business impact language
- Implementing anomaly detection in test metrics over time
Module 8: Visual Validation with AI - Pixel-based comparison vs semantic visual testing
- Training models to understand UI meaning, not just pixels
- Handling dynamic content in visual tests using AI masking
- Automated baseline selection and maintenance
- Responsive design validation across device types
- Accessibility checks integrated into visual AI workflows
- Text extraction from UI for validation accuracy
- Layout change detection using spatial reasoning models
- Real-time visual regression feedback in pull requests
- Combining visual AI with performance monitoring
Module 9: API and Service-Level AI Testing - Auto-generating API test suites from OpenAPI specs
- Predicting edge case requests using AI mutation
- Detecting contractual deviations in microservices
- Validating response semantics, not just structure
- AI-based fuzz testing for security and stability
- Monitoring endpoint behaviour drift over time
- Generating negative test payloads from production traffic
- Inferring service dependencies automatically
- Performance baseline prediction for new endpoints
- AI-assisted contract testing in asynchronous systems
Module 10: Performance and Load Testing with AI - AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Generating realistic, non-PII test data at scale
- Using AI to mask and anonymise production data for testing
- Dynamic data provisioning based on test context
- Predicting data needs before test execution begins
- Creating data mutation models for negative testing
- Automating data state resets using AI orchestration
- Validating data integrity across microservices with AI
- Synthetic data alignment with business logic rules
- Temporal data modelling for time-sensitive applications
- Data drift detection between test and production environments
Module 7: AI-Driven Test Analysis & Reporting - Automated test result summarisation using natural language generation
- Classifying failures into categories without manual tagging
- Predicting root cause from failure patterns using clustering
- Highlighting regression risks based on historical test data
- Creating executive dashboards powered by AI insights
- Identifying flaky tests automatically
- Correlating test failures with deployment events
- Auto-generating incident reports for failed builds
- Translating technical test data into business impact language
- Implementing anomaly detection in test metrics over time
Module 8: Visual Validation with AI - Pixel-based comparison vs semantic visual testing
- Training models to understand UI meaning, not just pixels
- Handling dynamic content in visual tests using AI masking
- Automated baseline selection and maintenance
- Responsive design validation across device types
- Accessibility checks integrated into visual AI workflows
- Text extraction from UI for validation accuracy
- Layout change detection using spatial reasoning models
- Real-time visual regression feedback in pull requests
- Combining visual AI with performance monitoring
Module 9: API and Service-Level AI Testing - Auto-generating API test suites from OpenAPI specs
- Predicting edge case requests using AI mutation
- Detecting contractual deviations in microservices
- Validating response semantics, not just structure
- AI-based fuzz testing for security and stability
- Monitoring endpoint behaviour drift over time
- Generating negative test payloads from production traffic
- Inferring service dependencies automatically
- Performance baseline prediction for new endpoints
- AI-assisted contract testing in asynchronous systems
Module 10: Performance and Load Testing with AI - AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Pixel-based comparison vs semantic visual testing
- Training models to understand UI meaning, not just pixels
- Handling dynamic content in visual tests using AI masking
- Automated baseline selection and maintenance
- Responsive design validation across device types
- Accessibility checks integrated into visual AI workflows
- Text extraction from UI for validation accuracy
- Layout change detection using spatial reasoning models
- Real-time visual regression feedback in pull requests
- Combining visual AI with performance monitoring
Module 9: API and Service-Level AI Testing - Auto-generating API test suites from OpenAPI specs
- Predicting edge case requests using AI mutation
- Detecting contractual deviations in microservices
- Validating response semantics, not just structure
- AI-based fuzz testing for security and stability
- Monitoring endpoint behaviour drift over time
- Generating negative test payloads from production traffic
- Inferring service dependencies automatically
- Performance baseline prediction for new endpoints
- AI-assisted contract testing in asynchronous systems
Module 10: Performance and Load Testing with AI - AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- AI-generated load models based on real user behaviour
- Predicting system bottlenecks before peak traffic
- Anomaly detection in performance metrics streams
- Automated SLA validation using threshold learning
- Optimising test duration without sacrificing coverage
- Generating realistic user journey simulations
- Detecting memory leaks through pattern analysis
- Scaling test infrastructure based on AI forecast
- Correlating performance dips with code changes
- Auto-documenting performance trends over release cycles
Module 11: AI Integration with Selenium and Playwright - Extending Selenium with AI locator strategies
- Building self-healing Playwright scripts
- Integrating computer vision fallbacks when locators fail
- Using AI to wait dynamically for page state
- Auto-suggesting test improvements based on execution logs
- Generating Playwright scripts from user session recordings
- Enhancing cross-browser testing with AI inconsistency detection
- Reducing test flakiness in headless environments
- AI-powered screenshot comparison within Selenium
- Automating element interaction methods based on context
Module 12: AI in CI/CD and DevOps Pipelines - Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Triggering AI test suites based on code impact analysis
- Intelligent gate decisions in deployment pipelines
- Reducing pipeline execution time with predictive testing
- Auto-quarantining risky changes using test intelligence
- Feedback loops between production monitoring and test generation
- Automating test data provisioning in ephemeral environments
- Embedding AI test reports into merge request summaries
- Managing test flakiness in multi-branch pipelines
- Scaling test execution based on pipeline urgency
- Creating audit trails for AI-driven pipeline decisions
Module 13: Security Testing Augmented by AI - Generating penetration test cases from application behaviour
- Automated vulnerability pattern recognition
- AI-assisted fuzzing for injection attacks
- Detecting authentication bypass risks in workflows
- Predicting insecure API usage from code patterns
- Session hijacking detection in test simulations
- Automated compliance checking against security standards
- Identifying sensitive data exposure in test outputs
- Analysing log entries for attack signatures
- Integrating security AI checks into functional test suites
Module 14: Model Management and AI Governance - Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Versioning AI models used in test automation
- Tracking model performance over time
- Re-training AI components with new data
- Ensuring reproducibility of AI test outcomes
- Documenting model assumptions and limitations
- Establishing approval workflows for model updates
- Handling model drift in test environments
- Creating fallback logic when AI predictions fail
- Auditing AI decisions for regulatory compliance
- Defining ownership and maintenance responsibilities
Module 15: Real-World AI Test Implementation Projects - Project 1: Convert a legacy test suite to AI-augmented operations
- Project 2: Build a self-healing end-to-end workflow for a live application
- Project 3: Design an AI-driven test reporting system for leadership
- Project 4: Implement visual validation for a dynamic web application
- Project 5: Create an intelligent API test suite with auto-discovery
- Project 6: Develop a predictive test prioritisation engine
- Project 7: Automate test data generation for a financial system
- Project 8: Integrate AI test triggers into a real CI/CD pipeline
- Project 9: Build a flaky test identification and quarantine system
- Project 10: Generate automated test documentation using AI
Module 16: Adoption, Scaling, and Team Enablement - Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Creating an AI test centre of excellence
- Training non-technical stakeholders on AI test outputs
- Measuring team productivity gains from AI adoption
- Change management for shifting to AI-powered QA
- Upskilling manual testers with guided AI tooling
- Establishing feedback loops between developers and QA AI systems
- Managing resistance to AI-driven decision making
- Creating reusable AI test components across teams
- Aligning AI test goals with business KPIs
- Scaling AI test frameworks across multiple product lines
Module 17: Certification Preparation and Career Advancement - Review of all core AI test automation concepts
- Practice exercises for certification assessment
- Common pitfalls and how to avoid them
- Technical interview preparation for AI-focused QA roles
- Building a personal portfolio of AI test projects
- Integrating certification into your LinkedIn and resume
- Demonstrating ROI to hiring managers
- Transitioning from traditional QA to AI-specialist roles
- Networking with other certified AI test engineers
- Next steps for continued learning and professional growth
Module 18: Final Assessment and Certificate of Completion - Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder
- Comprehensive knowledge verification assessment
- Hands-on evaluation of AI test implementation skills
- Real-world scenario analysis and solution design
- Automated feedback on assessment responses
- Personalised improvement recommendations
- Final checklist for mastering AI test automation
- Submission process for certification eligibility
- Overview of The Art of Service credentialing process
- How to display your Certificate of Completion professionally
- Lifetime access confirmation and update policy reminder