Durga Analytics • AI • QA & Testing

AI Learning Program for Software Testers & QA Engineers
Corporate • Enterprise-ready

A structured, role-based AI training program that enables QA teams to use Generative AI, Machine Learning, and intelligent automation to test faster, smarter, and with higher confidence — without becoming data scientists.

Curriculum Brochure

Program Snapshot

  • • AI for Manual, Automation & SDET roles
  • • GenAI-powered test design & defect analysis
  • • Self-healing automation & visual testing
  • • Testing AI / ML systems (bias, drift, accuracy)
  • • Corporate labs & real enterprise use cases

Curriculum — 100 Detailed Chapters (AI for Software Testers & QA)

LEVEL 100 — AI Foundations for QA Engineers (Chapters 1–10)
  1. What AI Really Is (and Is Not) for Testers
  2. AI vs Rule-Based Automation in QA
  3. Generative AI vs Classical ML — Tester’s View
  4. Where AI Fits in SDLC & STLC
  5. AI-Assisted QA Roles & Career Impact
  6. Understanding Prompts for QA Use
  7. AI Risks, Hallucinations & Validation
  8. Using AI Without Violating Compliance
  9. Enterprise AI Adoption Patterns
  10. Case Study: QA Team Before & After AI
LEVEL 200 — AI-Assisted Manual & Functional Testing (Chapters 11–30)
  1. Requirement Understanding with AI
  2. User Story to Test Case Conversion
  3. Functional Test Case Generation using GenAI
  4. Boundary & Edge Case Discovery with AI
  5. Negative & Abuse Testing via AI
  6. AI-Based Test Data Generation
  7. Test Scenario Optimization
  8. Exploratory Testing with AI Assistants
  9. AI for Test Coverage Analysis
  10. Requirement Gap Detection
  11. AI-Generated Test Documentation
  12. AI-Assisted UAT Support
  13. Defect Description Enhancement
  14. Root Cause Hypothesis Generation
  15. Duplicate Defect Detection
  16. Test Case Review Automation
  17. Regression Scope Reduction using AI
  18. AI for Acceptance Criteria Validation
  19. Human Review & AI Guardrails
  20. Enterprise Functional Testing Case Study
LEVEL 300 — AI in Automation Testing (Chapters 31–55)
  1. Limitations of Traditional Automation
  2. AI-Augmented Test Automation Concepts
  3. Self-Healing Automation Architecture
  4. AI-Based Locator Strategies
  5. Dynamic UI Change Handling
  6. Flaky Test Detection Using AI
  7. Intelligent Wait & Sync Mechanisms
  8. Visual Testing with AI (DOM vs Pixel)
  9. AI-Powered Cross-Browser Testing
  10. AI in API Testing
  11. AI for Test Script Optimization
  12. Automation Failure Pattern Recognition
  13. Smart Retry & Execution Control
  14. AI-Assisted Test Maintenance
  15. Test Automation Analytics with AI
  16. AI-Driven Regression Selection
  17. CI/CD Pipeline Intelligence
  18. AI for Build Failure Diagnosis
  19. Test Execution Forecasting
  20. Automation Debt Reduction
  21. Scaling Automation with AI
  22. Tool Landscape (Testim, Mabl, Applitools)
  23. Enterprise Automation Governance
  24. Risk of Over-Automation
  25. Case Study: Stable Automation at Scale
LEVEL 400 — Advanced AI-Driven QA Engineering (Chapters 56–75)
  1. QA as a Data-Driven Function
  2. Defect Prediction Models (Conceptual)
  3. Risk-Based Testing using AI Signals
  4. AI-Based Test Prioritization
  5. Intelligent Test Scheduling
  6. Release Readiness Scoring
  7. Predictive Quality Metrics
  8. AI-Driven Environment Validation
  9. Change Impact Analysis with AI
  10. AI-Based Root Cause Analytics
  11. Intelligent Quality Dashboards
  12. AI for Production Defect Prevention
  13. Feedback Loops from Production
  14. Autonomous Testing Concepts
  15. Human-in-the-Loop QA Models
  16. AI Decision Accountability
  17. QA Governance in AI-Driven Teams
  18. Cost Optimization with AI Testing
  19. Scaling QA Across Products
  20. Enterprise QA Transformation Case Study
LEVEL 500 — Testing AI & ML Systems (Chapters 76–100)
  1. Why AI Systems Need Different Testing
  2. Data Quality for AI Models
  3. Training vs Test Data Validation
  4. Bias & Fairness Testing
  5. Model Explainability (XAI) for QA
  6. Accuracy vs Business Risk
  7. AI Model Boundary Testing
  8. Model Drift Detection
  9. Monitoring AI in Production
  10. Testing AI APIs & Services
  11. Security Risks in AI Systems
  12. Adversarial Input Testing
  13. Ethical AI Testing Principles
  14. Regulatory Expectations (EU AI Act, ISO)
  15. AI Governance Frameworks
  16. Auditability & Traceability
  17. AI Failure Incident Analysis
  18. Human Override Testing
  19. Responsible AI Checklists
  20. Testing Chatbots & LLM Apps
  21. Testing Recommendation Engines
  22. Testing Financial / Risk Models
  23. AI QA Sign-Off Criteria
  24. Enterprise AI Assurance Model
  25. Capstone: AI System Test Strategy

Why Enterprises Choose This Program

Pricing & Delivery Models

Self-Paced

Video, PDF/Deck, Deep Dive Podcast

Contact

Individual learners

Corporate Cohort

Instructor-led

Custom

Live sessions + labs

Enterprise Custom

Role-based & tool-specific

Contact

Org-wide enablement

Instructors & Credibility

Course Authors

QA leaders and AI practitioners with deep enterprise testing and automation experience.

Get Started

Request a brochure, sample module, or corporate proposal.

Email: contact@durgaanalytics.com

Get Started

Enroll or request a cohort. We'll provide access to curriculum, lab datasets and project briefs.