AI for QA
A role-based AI program that enables QA teams to use Generative AI, machine learning, and intelligent automation to test faster and smarter - and to test AI systems themselves - without becoming data scientists.
What it covers and how it works
A structured, enterprise-ready AI training program for software testers and QA engineers. It shows manual testers, automation engineers, SDETs, and QA leads how to apply Generative AI and ML across the testing lifecycle - from AI-assisted test design and self-healing automation to testing AI and ML systems for bias, drift, and accuracy. 100 detailed chapters across five levels, designed for testers, not data scientists.
Five levels build from AI foundations for QA, through AI-assisted manual and automation testing, into advanced AI-driven QA engineering and, finally, testing AI and ML systems themselves - each level ending in an enterprise case study or capstone.
Built for testers, not data scientists
- AI for Manual, Automation, and SDET roles
- GenAI-powered test design and defect analysis
- Self-healing automation and visual testing
- Testing AI and ML systems for bias, drift, and accuracy
- Corporate labs and real enterprise use cases
Immediate, tool-agnostic value
It is designed for testers, delivers immediate productivity gains, and stays tool-agnostic and enterprise-friendly. Uniquely, it also covers how to test AI systems themselves - the assurance skill that regulated organizations increasingly require.
100 chapters · 5 levels
Five levels take you from AI foundations for QA to testing AI systems themselves. Expand any level for its chapters.
L100 AI Foundations for QA Engineers 10 chapters
- What AI Really Is (and Is Not) for Testers
- AI vs Rule-Based Automation in QA
- Generative AI vs Classical ML - Tester's View
- Where AI Fits in SDLC & STLC
- AI-Assisted QA Roles & Career Impact
- Understanding Prompts for QA Use
- AI Risks, Hallucinations & Validation
- Using AI Without Violating Compliance
- Enterprise AI Adoption Patterns
- Case Study: QA Team Before & After AI
L200 AI-Assisted Manual & Functional Testing 20 chapters
- Requirement Understanding with AI
- User Story to Test Case Conversion
- Functional Test Case Generation using GenAI
- Boundary & Edge Case Discovery with AI
- Negative & Abuse Testing via AI
- AI-Based Test Data Generation
- Test Scenario Optimization
- Exploratory Testing with AI Assistants
- AI for Test Coverage Analysis
- Requirement Gap Detection
- AI-Generated Test Documentation
- AI-Assisted UAT Support
- Defect Description Enhancement
- Root Cause Hypothesis Generation
- Duplicate Defect Detection
- Test Case Review Automation
- Regression Scope Reduction using AI
- AI for Acceptance Criteria Validation
- Human Review & AI Guardrails
- Enterprise Functional Testing Case Study
L300 AI in Automation Testing 25 chapters
- Limitations of Traditional Automation
- AI-Augmented Test Automation Concepts
- Self-Healing Automation Architecture
- AI-Based Locator Strategies
- Dynamic UI Change Handling
- Flaky Test Detection Using AI
- Intelligent Wait & Sync Mechanisms
- Visual Testing with AI (DOM vs Pixel)
- AI-Powered Cross-Browser Testing
- AI in API Testing
- AI for Test Script Optimization
- Automation Failure Pattern Recognition
- Smart Retry & Execution Control
- AI-Assisted Test Maintenance
- Test Automation Analytics with AI
- AI-Driven Regression Selection
- CI/CD Pipeline Intelligence
- AI for Build Failure Diagnosis
- Test Execution Forecasting
- Automation Debt Reduction
- Scaling Automation with AI
- Tool Landscape (Testim, Mabl, Applitools)
- Enterprise Automation Governance
- Risk of Over-Automation
- Case Study: Stable Automation at Scale
L400 Advanced AI-Driven QA Engineering 20 chapters
- QA as a Data-Driven Function
- Defect Prediction Models (Conceptual)
- Risk-Based Testing using AI Signals
- AI-Based Test Prioritization
- Intelligent Test Scheduling
- Release Readiness Scoring
- Predictive Quality Metrics
- AI-Driven Environment Validation
- Change Impact Analysis with AI
- AI-Based Root Cause Analytics
- Intelligent Quality Dashboards
- AI for Production Defect Prevention
- Feedback Loops from Production
- Autonomous Testing Concepts
- Human-in-the-Loop QA Models
- AI Decision Accountability
- QA Governance in AI-Driven Teams
- Cost Optimization with AI Testing
- Scaling QA Across Products
- Enterprise QA Transformation Case Study
L500 Testing AI & ML Systems 25 chapters
- Why AI Systems Need Different Testing
- Data Quality for AI Models
- Training vs Test Data Validation
- Bias & Fairness Testing
- Model Explainability (XAI) for QA
- Accuracy vs Business Risk
- AI Model Boundary Testing
- Model Drift Detection
- Monitoring AI in Production
- Testing AI APIs & Services
- Security Risks in AI Systems
- Adversarial Input Testing
- Ethical AI Testing Principles
- Regulatory Expectations (EU AI Act, ISO)
- AI Governance Frameworks
- Auditability & Traceability
- AI Failure Incident Analysis
- Human Override Testing
- Responsible AI Checklists
- Testing Chatbots & LLM Apps
- Testing Recommendation Engines
- Testing Financial / Risk Models
- AI QA Sign-Off Criteria
- Enterprise AI Assurance Model
- Capstone: AI System Test Strategy
Explore the track
How you learn
- Self-paced with lifetime access
- Mentor-led cohorts
- Private corporate and enterprise delivery
- Certificate and digital badge
AI for QA - answered
Who is the AI for QA course for?
QA engineers, test automation engineers, SDETs, and QA leads - manual or automation - who want to apply AI in real testing workflows, and teams that must also test AI and ML systems.
Do I need AI or data-science knowledge?
No. It is designed for testers, not data scientists. It builds the AI foundations you need from a tester's point of view and stays focused on testing work.
What will I be able to do?
Use GenAI to design test cases and analyze defects, build self-healing and visual automation, apply AI across manual and automation testing, and test AI and ML systems for bias, drift, accuracy, and security.
Is it tool-specific?
It is tool-agnostic and enterprise-friendly, with coverage of the tool landscape (for example Testim, Mabl, Applitools) so the skills transfer across your stack.
Does it cover testing AI systems themselves?
Yes. Level 500 is dedicated to testing AI and ML systems - data quality, bias and fairness, explainability, drift, adversarial input, and AI assurance and sign-off.
How is it delivered?
Self-paced with video, decks, and a deep-dive podcast; as an instructor-led corporate cohort with labs; or as enterprise-custom, role-based and tool-specific enablement.
Is there a certificate?
Yes. Participants earn a Durga Analytics certificate and digital badge.
Is there a downloadable brochure?
Yes, from the download button at the top of this page.
AI for QA for you or your team
Enquire about enrolment, or scope a private corporate cohort.