Mastering AI-Powered Testing to Future-Proof Your QA Career
You’re feeling the pressure. Deadlines are tighter, testing cycles are shorter, and the expectation to deliver flawless software-faster-is growing every day. You see AI tools emerging, teams scrambling to adopt them, and whispers that traditional QA roles might not survive the transformation. But here’s the truth: AI won’t replace QA professionals. Those who master AI-powered testing will lead the next wave of quality assurance. They’ll be the ones designing intelligent test strategies, reducing regression time by 70%, and earning strategic seats at the planning table-no longer seen as gatekeepers, but as innovation enablers. “Mastering AI-Powered Testing to Future-Proof Your QA Career” is not another theory-based course. It’s a field-tested, step-by-step blueprint to transition from manual or script-based testing into an AI-empowered QA leader-within 30 days. You’ll go from uncertainty to confidence, building and deploying AI-augmented test frameworks that cut execution time, increase coverage, and deliver board-ready results. One senior QA analyst, Maria T., applied the course’s methodology at her fintech firm and reduced their end-to-end regression suite from 8 hours to under 90 minutes-all while improving defect detection by 41%. The best part? You don’t need a data science degree. You don’t need prior AI experience. This program assumes only foundational QA knowledge and walks you through everything-from tool selection to ethical AI governance-with precision and clarity. Here’s how this course is structured to help you get there.Course Format & Delivery Details Fully Self-Paced, On-Demand Access
The course is designed for professionals with real-world schedules. You’ll get immediate online access upon enrollment, with no fixed start dates or time commitments. Learn at your own pace, on your own time, and from anywhere in the world. Fast Results, Flexible Timeline
Most learners complete the core program in 4 to 6 weeks with 60–90 minutes of weekly effort. Many apply the first framework to their current projects within the first 10 days-delivering measurable improvements in test efficiency before finishing the course. Lifetime Access & Ongoing Updates
Enroll once, own it forever. You’ll receive lifetime access to all course materials, including every future update at no extra cost. As AI testing tools and best practices evolve, your knowledge stays cutting-edge. Designed for Global, Mobile-First Learning
The entire program is mobile-friendly and accessible 24/7. Whether you’re commuting, working remotely, or studying between sprints, you can progress seamlessly across devices-no downloads or installations required. Direct Instructor Support & Guidance
You’re not learning in isolation. Gain access to structured guidance from seasoned AI testing architects with over 15 years of combined experience in enterprise QA transformation. Clarify concepts, refine strategies, and accelerate your progress with curated feedback channels built into each module. Builds Toward a Globally Recognized Credential
Upon successful completion, you’ll earn a Certificate of Completion issued by The Art of Service-a credential trusted by professionals in 140+ countries and recognised by leading tech employers. This isn’t just a PDF; it’s verifiable, career-advancing proof that you’ve mastered AI-powered testing at a professional level. Transparent, Upfront Pricing - No Hidden Fees
The course fee is straightforward and all-inclusive. There are no recurring charges, hidden costs, or upsells. What you see is exactly what you get-lifetime access, full curriculum, certification, and support. Accepted Payment Methods
We accept Visa, Mastercard, and PayPal. Secure checkout ensures your information is protected with bank-level encryption. Zero-Risk Enrollment with 100% Satisfaction Guarantee
If you complete the first two modules and don’t believe this course is transforming your skills and career trajectory, simply request a full refund. No forms, no hassle. This is our promise: you either move forward, or you get your money back. Confirmation & Access Workflow
After enrollment, you’ll receive a confirmation email. Your course access details will be sent separately once your materials are finalised and ready-ensuring you begin with a polished, up-to-date learning experience. This Works Even If…
You’ve never touched an AI tool. You work in a regulated industry. Your team uses legacy systems. You’re not in a leadership role. You’re not a coder. This program is built for real-world constraints and real engineers. With step-by-step implementation guides, role-specific workflows, and AI testing patterns that integrate with existing QA processes, you’ll be applying what you learn-even in highly regulated, low-flexibility environments. What Our Learners Say
“I was worried AI would make my role obsolete. Six weeks after starting this course, I led the rollout of an AI-augmented testing pipeline at my insurance tech company. My performance review called it the most impactful initiative of the quarter. I’ve since been promoted to QA Automation Lead.” - James L., Toronto “I work in a government healthcare agency with strict compliance rules. This course gave me the frameworks to introduce AI testing safely, with documented risk controls and audit trails. Now I’m advising other departments on responsible AI adoption.” - Amina R., London
Extensive and Detailed Course Curriculum
Module 1: Foundations of AI in Software Testing - Understanding the role of AI in modern QA
- Key differences between traditional and AI-powered testing
- Common misconceptions about AI in QA
- How AI enhances test coverage and defect detection
- The ethical implications of autonomous testing
- Myths vs realities of AI replacing QA roles
- Overview of AI testing maturity models
- Identifying early indicators of AI readiness in your team
- Building a personal learning roadmap for AI testing
- Defining success metrics for AI testing adoption
Module 2: Core AI Concepts for QA Professionals - Introduction to machine learning and deep learning
- Supervised vs unsupervised learning in testing
- Understanding neural networks at a QA level
- What is natural language processing and how it applies to test case generation
- Computer vision for UI validation
- Reinforcement learning in performance testing
- Probabilistic reasoning and risk-based test prioritisation
- How AI models improve over time with feedback
- Understanding overfitting and underfitting in test models
- The concept of training data in test automation
Module 3: AI Testing Frameworks and Methodologies - Overview of AI-augmented test automation frameworks
- Designing self-healing test scripts with AI
- Model-based testing with AI decision trees
- Risk-based testing using AI-driven analytics
- Evolutionary testing with genetic algorithms
- Test suite optimisation using AI clustering
- Predictive test execution scheduling
- Adaptive test maintenance with anomaly detection
- Feedback loops for continuous model improvement
- Integrating AI logic into existing test frameworks
Module 4: Selecting and Evaluating AI Testing Tools - Comparison of leading AI testing platforms
- Evaluating vendor claims vs actual capabilities
- Open-source vs commercial AI testing tools
- Tool compatibility with existing test environments
- Security and data privacy considerations
- Scalability and licensing models
- Interoperability with CI CD pipelines
- Integration with Jira, TestRail, and Xray
- API support for custom integrations
- Vendor assessment checklist for AI tools
Module 5: Building Your First AI-Powered Test Strategy - Defining a real-world test scenario for AI enhancement
- Identifying high-value areas for AI adoption
- Creating an AI testing roadmap aligned to sprint cycles
- Setting measurable KPIs for AI testing success
- Stakeholder communication and expectation setting
- Resource allocation for initial implementation
- Risk assessment and mitigation planning
- Data requirements and collection strategies
- Defining training and validation datasets
- Documenting your AI testing strategy for review
Module 6: Implementing Self-Healing Test Automation - Understanding dynamic element locators
- How AI detects and corrects broken selectors
- Implementing resilient XPath and CSS strategies
- Using AI to predict UI changes
- Configuring auto-recovery actions in test scripts
- Validating self-healing accuracy
- Reducing false positives with contextual awareness
- Capturing UI snapshots for AI comparison
- Logging and reporting auto-recovery events
- Benchmarking maintenance effort before and after
Module 7: Intelligent Test Case Generation - Generating test cases from user stories with NLP
- Creating edge cases using AI exploration
- Generating negative test scenarios automatically
- Automating acceptance criteria validation
- Deriving test data from system logs
- Using AI to detect missing test coverage
- Generating equivalence classes and boundary values
- Creating end-to-end scenario flows
- Validating test case quality with AI scoring
- Exporting AI-generated cases to test management tools
Module 8: AI-Driven Test Data Management - Synthetic test data generation with AI
- Masking sensitive data using intelligent obfuscation
- Creating realistic user behaviour patterns
- Generating large datasets for performance testing
- Validating data integrity across environments
- Automating data setup and teardown
- Matching test data to specific test goals
- Versioning AI-generated datasets
- Ensuring GDPR and HIPAA compliance
- Linking data profiles to test suites
Module 9: Visual Validation and UI Testing with AI - How AI detects visual regressions
- Setting tolerance levels for visual comparisons
- Handling dynamic content and animations
- Using pixel-based and DOM-aware validation
- Detecting layout shifts and responsiveness issues
- Identifying accessibility violations visually
- Integrating visual testing into CI pipelines
- Reducing false positives with AI context
- Generating visual test reports with annotations
- Scaling visual tests across multiple viewports
Module 10: Performance Testing with AI Predictors - Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
Module 1: Foundations of AI in Software Testing - Understanding the role of AI in modern QA
- Key differences between traditional and AI-powered testing
- Common misconceptions about AI in QA
- How AI enhances test coverage and defect detection
- The ethical implications of autonomous testing
- Myths vs realities of AI replacing QA roles
- Overview of AI testing maturity models
- Identifying early indicators of AI readiness in your team
- Building a personal learning roadmap for AI testing
- Defining success metrics for AI testing adoption
Module 2: Core AI Concepts for QA Professionals - Introduction to machine learning and deep learning
- Supervised vs unsupervised learning in testing
- Understanding neural networks at a QA level
- What is natural language processing and how it applies to test case generation
- Computer vision for UI validation
- Reinforcement learning in performance testing
- Probabilistic reasoning and risk-based test prioritisation
- How AI models improve over time with feedback
- Understanding overfitting and underfitting in test models
- The concept of training data in test automation
Module 3: AI Testing Frameworks and Methodologies - Overview of AI-augmented test automation frameworks
- Designing self-healing test scripts with AI
- Model-based testing with AI decision trees
- Risk-based testing using AI-driven analytics
- Evolutionary testing with genetic algorithms
- Test suite optimisation using AI clustering
- Predictive test execution scheduling
- Adaptive test maintenance with anomaly detection
- Feedback loops for continuous model improvement
- Integrating AI logic into existing test frameworks
Module 4: Selecting and Evaluating AI Testing Tools - Comparison of leading AI testing platforms
- Evaluating vendor claims vs actual capabilities
- Open-source vs commercial AI testing tools
- Tool compatibility with existing test environments
- Security and data privacy considerations
- Scalability and licensing models
- Interoperability with CI CD pipelines
- Integration with Jira, TestRail, and Xray
- API support for custom integrations
- Vendor assessment checklist for AI tools
Module 5: Building Your First AI-Powered Test Strategy - Defining a real-world test scenario for AI enhancement
- Identifying high-value areas for AI adoption
- Creating an AI testing roadmap aligned to sprint cycles
- Setting measurable KPIs for AI testing success
- Stakeholder communication and expectation setting
- Resource allocation for initial implementation
- Risk assessment and mitigation planning
- Data requirements and collection strategies
- Defining training and validation datasets
- Documenting your AI testing strategy for review
Module 6: Implementing Self-Healing Test Automation - Understanding dynamic element locators
- How AI detects and corrects broken selectors
- Implementing resilient XPath and CSS strategies
- Using AI to predict UI changes
- Configuring auto-recovery actions in test scripts
- Validating self-healing accuracy
- Reducing false positives with contextual awareness
- Capturing UI snapshots for AI comparison
- Logging and reporting auto-recovery events
- Benchmarking maintenance effort before and after
Module 7: Intelligent Test Case Generation - Generating test cases from user stories with NLP
- Creating edge cases using AI exploration
- Generating negative test scenarios automatically
- Automating acceptance criteria validation
- Deriving test data from system logs
- Using AI to detect missing test coverage
- Generating equivalence classes and boundary values
- Creating end-to-end scenario flows
- Validating test case quality with AI scoring
- Exporting AI-generated cases to test management tools
Module 8: AI-Driven Test Data Management - Synthetic test data generation with AI
- Masking sensitive data using intelligent obfuscation
- Creating realistic user behaviour patterns
- Generating large datasets for performance testing
- Validating data integrity across environments
- Automating data setup and teardown
- Matching test data to specific test goals
- Versioning AI-generated datasets
- Ensuring GDPR and HIPAA compliance
- Linking data profiles to test suites
Module 9: Visual Validation and UI Testing with AI - How AI detects visual regressions
- Setting tolerance levels for visual comparisons
- Handling dynamic content and animations
- Using pixel-based and DOM-aware validation
- Detecting layout shifts and responsiveness issues
- Identifying accessibility violations visually
- Integrating visual testing into CI pipelines
- Reducing false positives with AI context
- Generating visual test reports with annotations
- Scaling visual tests across multiple viewports
Module 10: Performance Testing with AI Predictors - Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Introduction to machine learning and deep learning
- Supervised vs unsupervised learning in testing
- Understanding neural networks at a QA level
- What is natural language processing and how it applies to test case generation
- Computer vision for UI validation
- Reinforcement learning in performance testing
- Probabilistic reasoning and risk-based test prioritisation
- How AI models improve over time with feedback
- Understanding overfitting and underfitting in test models
- The concept of training data in test automation
Module 3: AI Testing Frameworks and Methodologies - Overview of AI-augmented test automation frameworks
- Designing self-healing test scripts with AI
- Model-based testing with AI decision trees
- Risk-based testing using AI-driven analytics
- Evolutionary testing with genetic algorithms
- Test suite optimisation using AI clustering
- Predictive test execution scheduling
- Adaptive test maintenance with anomaly detection
- Feedback loops for continuous model improvement
- Integrating AI logic into existing test frameworks
Module 4: Selecting and Evaluating AI Testing Tools - Comparison of leading AI testing platforms
- Evaluating vendor claims vs actual capabilities
- Open-source vs commercial AI testing tools
- Tool compatibility with existing test environments
- Security and data privacy considerations
- Scalability and licensing models
- Interoperability with CI CD pipelines
- Integration with Jira, TestRail, and Xray
- API support for custom integrations
- Vendor assessment checklist for AI tools
Module 5: Building Your First AI-Powered Test Strategy - Defining a real-world test scenario for AI enhancement
- Identifying high-value areas for AI adoption
- Creating an AI testing roadmap aligned to sprint cycles
- Setting measurable KPIs for AI testing success
- Stakeholder communication and expectation setting
- Resource allocation for initial implementation
- Risk assessment and mitigation planning
- Data requirements and collection strategies
- Defining training and validation datasets
- Documenting your AI testing strategy for review
Module 6: Implementing Self-Healing Test Automation - Understanding dynamic element locators
- How AI detects and corrects broken selectors
- Implementing resilient XPath and CSS strategies
- Using AI to predict UI changes
- Configuring auto-recovery actions in test scripts
- Validating self-healing accuracy
- Reducing false positives with contextual awareness
- Capturing UI snapshots for AI comparison
- Logging and reporting auto-recovery events
- Benchmarking maintenance effort before and after
Module 7: Intelligent Test Case Generation - Generating test cases from user stories with NLP
- Creating edge cases using AI exploration
- Generating negative test scenarios automatically
- Automating acceptance criteria validation
- Deriving test data from system logs
- Using AI to detect missing test coverage
- Generating equivalence classes and boundary values
- Creating end-to-end scenario flows
- Validating test case quality with AI scoring
- Exporting AI-generated cases to test management tools
Module 8: AI-Driven Test Data Management - Synthetic test data generation with AI
- Masking sensitive data using intelligent obfuscation
- Creating realistic user behaviour patterns
- Generating large datasets for performance testing
- Validating data integrity across environments
- Automating data setup and teardown
- Matching test data to specific test goals
- Versioning AI-generated datasets
- Ensuring GDPR and HIPAA compliance
- Linking data profiles to test suites
Module 9: Visual Validation and UI Testing with AI - How AI detects visual regressions
- Setting tolerance levels for visual comparisons
- Handling dynamic content and animations
- Using pixel-based and DOM-aware validation
- Detecting layout shifts and responsiveness issues
- Identifying accessibility violations visually
- Integrating visual testing into CI pipelines
- Reducing false positives with AI context
- Generating visual test reports with annotations
- Scaling visual tests across multiple viewports
Module 10: Performance Testing with AI Predictors - Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Comparison of leading AI testing platforms
- Evaluating vendor claims vs actual capabilities
- Open-source vs commercial AI testing tools
- Tool compatibility with existing test environments
- Security and data privacy considerations
- Scalability and licensing models
- Interoperability with CI CD pipelines
- Integration with Jira, TestRail, and Xray
- API support for custom integrations
- Vendor assessment checklist for AI tools
Module 5: Building Your First AI-Powered Test Strategy - Defining a real-world test scenario for AI enhancement
- Identifying high-value areas for AI adoption
- Creating an AI testing roadmap aligned to sprint cycles
- Setting measurable KPIs for AI testing success
- Stakeholder communication and expectation setting
- Resource allocation for initial implementation
- Risk assessment and mitigation planning
- Data requirements and collection strategies
- Defining training and validation datasets
- Documenting your AI testing strategy for review
Module 6: Implementing Self-Healing Test Automation - Understanding dynamic element locators
- How AI detects and corrects broken selectors
- Implementing resilient XPath and CSS strategies
- Using AI to predict UI changes
- Configuring auto-recovery actions in test scripts
- Validating self-healing accuracy
- Reducing false positives with contextual awareness
- Capturing UI snapshots for AI comparison
- Logging and reporting auto-recovery events
- Benchmarking maintenance effort before and after
Module 7: Intelligent Test Case Generation - Generating test cases from user stories with NLP
- Creating edge cases using AI exploration
- Generating negative test scenarios automatically
- Automating acceptance criteria validation
- Deriving test data from system logs
- Using AI to detect missing test coverage
- Generating equivalence classes and boundary values
- Creating end-to-end scenario flows
- Validating test case quality with AI scoring
- Exporting AI-generated cases to test management tools
Module 8: AI-Driven Test Data Management - Synthetic test data generation with AI
- Masking sensitive data using intelligent obfuscation
- Creating realistic user behaviour patterns
- Generating large datasets for performance testing
- Validating data integrity across environments
- Automating data setup and teardown
- Matching test data to specific test goals
- Versioning AI-generated datasets
- Ensuring GDPR and HIPAA compliance
- Linking data profiles to test suites
Module 9: Visual Validation and UI Testing with AI - How AI detects visual regressions
- Setting tolerance levels for visual comparisons
- Handling dynamic content and animations
- Using pixel-based and DOM-aware validation
- Detecting layout shifts and responsiveness issues
- Identifying accessibility violations visually
- Integrating visual testing into CI pipelines
- Reducing false positives with AI context
- Generating visual test reports with annotations
- Scaling visual tests across multiple viewports
Module 10: Performance Testing with AI Predictors - Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Understanding dynamic element locators
- How AI detects and corrects broken selectors
- Implementing resilient XPath and CSS strategies
- Using AI to predict UI changes
- Configuring auto-recovery actions in test scripts
- Validating self-healing accuracy
- Reducing false positives with contextual awareness
- Capturing UI snapshots for AI comparison
- Logging and reporting auto-recovery events
- Benchmarking maintenance effort before and after
Module 7: Intelligent Test Case Generation - Generating test cases from user stories with NLP
- Creating edge cases using AI exploration
- Generating negative test scenarios automatically
- Automating acceptance criteria validation
- Deriving test data from system logs
- Using AI to detect missing test coverage
- Generating equivalence classes and boundary values
- Creating end-to-end scenario flows
- Validating test case quality with AI scoring
- Exporting AI-generated cases to test management tools
Module 8: AI-Driven Test Data Management - Synthetic test data generation with AI
- Masking sensitive data using intelligent obfuscation
- Creating realistic user behaviour patterns
- Generating large datasets for performance testing
- Validating data integrity across environments
- Automating data setup and teardown
- Matching test data to specific test goals
- Versioning AI-generated datasets
- Ensuring GDPR and HIPAA compliance
- Linking data profiles to test suites
Module 9: Visual Validation and UI Testing with AI - How AI detects visual regressions
- Setting tolerance levels for visual comparisons
- Handling dynamic content and animations
- Using pixel-based and DOM-aware validation
- Detecting layout shifts and responsiveness issues
- Identifying accessibility violations visually
- Integrating visual testing into CI pipelines
- Reducing false positives with AI context
- Generating visual test reports with annotations
- Scaling visual tests across multiple viewports
Module 10: Performance Testing with AI Predictors - Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Synthetic test data generation with AI
- Masking sensitive data using intelligent obfuscation
- Creating realistic user behaviour patterns
- Generating large datasets for performance testing
- Validating data integrity across environments
- Automating data setup and teardown
- Matching test data to specific test goals
- Versioning AI-generated datasets
- Ensuring GDPR and HIPAA compliance
- Linking data profiles to test suites
Module 9: Visual Validation and UI Testing with AI - How AI detects visual regressions
- Setting tolerance levels for visual comparisons
- Handling dynamic content and animations
- Using pixel-based and DOM-aware validation
- Detecting layout shifts and responsiveness issues
- Identifying accessibility violations visually
- Integrating visual testing into CI pipelines
- Reducing false positives with AI context
- Generating visual test reports with annotations
- Scaling visual tests across multiple viewports
Module 10: Performance Testing with AI Predictors - Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Predicting performance bottlenecks before deployment
- Using AI to simulate realistic user loads
- Identifying performance degradation patterns
- Auto-generating load test scenarios
- Correlating code changes with performance impact
- Recommendations for performance optimisation
- Automating threshold detection and alerts
- Simulating global user distribution
- Generating performance test reports with insights
- Integrating with monitoring tools like New Relic
Module 11: API Testing Enhanced by AI - Automatically generating API test cases
- Predicting edge cases in request payloads
- Detecting anomalies in response times
- Validating schema compliance dynamically
- Testing microservices interaction patterns
- Generating authentication and authorisation flows
- Monitoring API contract drift
- Automated security scanning for APIs
- Creating negative test scenarios for error handling
- Benchmarking API resilience under stress
Module 12: Security Testing with AI Assistance - Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Automated vulnerability scanning with AI
- Detecting OWASP Top 10 issues in test runs
- Identifying insecure API endpoints
- Generating security test payloads
- Predicting likely attack vectors based on architecture
- Integrating security tests into CI CD
- Learning from past penetration test results
- Automating compliance checks
- Reducing false positives in security alerts
- Reporting security findings with risk scoring
Module 13: AI in Regression and Smoke Testing - Intelligent selection of regression test suites
- Impact analysis based on code changes
- Prioritising high-risk test cases
- Reducing execution time with smart filtering
- Automating smoke test coverage
- Validating critical user journeys
- Detecting flaky tests with AI analysis
- Creating lightweight regression packs
- Reporting confidence scores for builds
- Integrating with merge request pipelines
Module 14: AI for Mobile Application Testing - Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Handling device fragmentation with AI
- Simulating real user interactions on mobile
- Testing across OS versions and screen sizes
- Automating gesture-based interactions
- Detecting UI inconsistencies on mobile
- Validating offline functionality
- Testing battery and performance impact
- Generating realistic mobile user scenarios
- Integrating with mobile CI CD tools
- Reporting mobile-specific defects
Module 15: Test Analytics and AI Insights - Building dashboards with AI-driven insights
- Identifying recurring defect patterns
- Correlating test results with deployment events
- Forecasting defect rates based on velocity
- Generating root cause hypotheses
- Measuring test effectiveness over time
- Visualising test coverage heatmaps
- Tracking flaky test trends
- Reporting QA metrics to leadership
- Creating automated weekly QA summaries
Module 16: Governance and Ethics in AI Testing - Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Establishing AI testing policies
- Ensuring transparency in AI decisions
- Documenting model behaviour and limitations
- Handling bias in training data
- Creating audit trails for AI actions
- Compliance with regulatory standards
- Defining roles and responsibilities
- Setting boundaries for autonomous testing
- Legal implications of AI-generated tests
- Creating an AI ethics checklist for QA teams
Module 17: Change Management and Team Adoption - Communicating the value of AI testing
- Overcoming team resistance to AI tools
- Running pilot projects for credibility
- Training team members on new workflows
- Creating internal documentation and playbooks
- Measuring adoption success
- Gathering feedback for continuous improvement
- Scaling AI testing across teams
- Establishing a centre of excellence
- Building a culture of AI-assisted quality
Module 18: Certification Exam Preparation & Career Advancement - Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Reviewing core AI testing competencies
- Practice questions for the certification exam
- Common pitfalls and how to avoid them
- Time management during the assessment
- Preparing a professional portfolio
- Highlighting AI testing skills on your resume
- Answering technical interview questions
- Presenting your certification in performance reviews
- Negotiating promotions based on new skills
- Joining the global Art of Service alumni network
Module 19: Real-World Implementation Projects - Project 1: Implement self-healing scripts in your current suite
- Project 2: Generate test cases from a live user story
- Project 3: Build an AI-optimised regression pack
- Project 4: Create synthetic data for a complex workflow
- Project 5: Run an AI-powered visual test on a live UI
- Project 6: Analyse test logs to improve coverage
- Project 7: Simulate performance under predicted load
- Project 8: Audit an API with AI assistance
- Documenting lessons learned from each project
- Submitting projects for feedback and review
Module 20: Final Assessment & Certification - Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey
- Comprehensive knowledge assessment
- Scenario-based decision evaluation
- Strategy design for a fictional enterprise case
- Grading rubric and feedback process
- Earning your Certificate of Completion
- How to showcase your credential online
- Verification process for employers
- Continuing education pathways
- Access to advanced AI testing communities
- Next steps in your mastery journey