Week 1: AI-Enhanced Quality Assurance Foundations
The Shift-Left Strategy: How AI facilitates earlier testing in the SDLC; moving from manual regression to AI-driven “Self-Healing” scripts.
AI in Test Management: Using LLMs to transform business requirements into comprehensive Test Plans and Traceability Matrices.
Automated Test Generation: Leveraging tools (like GitHub Copilot or ChatGPT) to generate unit tests, integration tests, and boilerplate code in Selenium/Cypress.
Smart Test Data Generation: Using Generative AI to create synthetic, privacy-compliant datasets that mimic complex real-world production data.
Visual Regression Testing: Implementing AI-powered computer vision (e.g., Applitools) to detect UI “look and feel” discrepancies that traditional code-based assertions miss.
Week 2: Advanced AI Testing & Intelligent Automation
Self-Healing Automation: Implementing frameworks that automatically update locators (IDs, XPaths) when the UI changes, drastically reducing maintenance time.
Predictive Analytics & Risk Assessment: Using Machine Learning to analyze historical bug data and predict which modules are most likely to fail in the next build.
AI-Powered Performance & Load Testing: Utilizing AI to simulate realistic user behavior patterns and identify bottleneck trends before they crash the system.
API & Security Testing with AI: Automating the discovery of hidden API endpoints and using AI to simulate “Fuzz Testing” and common vulnerability exploits.
The Future of QA – Autonomous Testing: Exploring “No-Code” AI testing platforms (like Testim.io or Mabl) and the role of the AI-QA Engineer.
Capstone Project: Building an intelligent testing pipeline that generates its own test cases, executes them on a web app, and provides an AI-summarized “Risk Report.”
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