AI-Powered Test Automation Tool
An intelligent test automation assistant that converts natural language test cases into executable Selenium/Playwright scripts using LLM-driven DOM analysis and element resolution.
PythonSeleniumAWS BedrockStreamlitLLM
Problem
QA engineers spend excessive time manually converting test cases into automation scripts. Element selectors break frequently on dynamic web pages, and maintaining large test suites is costly.
Solution
Built an intelligent assistant that:
- Accepts natural language test cases
- Analyzes DOM structure using LLM intelligence
- Resolves target elements with multi-strategy matching
- Generates production-quality Java/Python test code with POM structure
Architecture
The system uses a three-tier element resolution strategy:
- Heuristic Matching — Fast pattern-based resolution using HTML attributes, text content, and accessibility roles
- LLM-Based DOM Parsing — Claude/GPT-4 analyzes sanitized DOM to identify the correct element with natural language understanding
- Vision-Based Fallback — Screenshot analysis for edge cases where DOM parsing falls short
Stack
- Python — Core automation logic
- Selenium/Playwright — Browser automation engines
- AWS Bedrock — LLM inference (Claude)
- Streamlit — Interactive UI for test management
- Page Object Model — Code generation pattern
Results
- 60% reduction in test script creation time
- Robust multi-strategy element resolution
- Real-time execution monitoring via Streamlit UI
- Automated POM-structured Java test generation
Learnings
The gap between POC and production reliability requires thoughtful fallback strategies. No single AI technique handles all edge cases — combining fast heuristics with intelligent LLM reasoning creates the most resilient system.