AI for QA: Detect Duplicate Test Cases Using AI

AI-Powered Duplicate Test Cases Detection for QA

What you'll learn
- QA Engineers who want to bring AI into their toolkit
- Automation testers tired of redundant test cases
- Python developers interested in real-world LLM applications
- Anyone curious about semantic search, embeddings, or LLM-powered utilities

Requirements
- Basic understanding of Python (3.x)
- A Gemini API key (we’ll guide you on how to get it)
- A sample test cases CSV (included in course resources)

Description
Hi there, and welcome to“AI for QA: Detect Duplicate Test Cases Using AI”— a hands-on course where we combine the power ofPython,Large Language Models (LLMs)likeGemini, andvector similarityto solve a real-world problem in QA and software testing.

If you've ever worked withhundreds of test casesand wondered:

"Am I repeating the same test over and over with slight wording differences?"then you're in the right place.What Are We Building?

In this course, you're going tobuild a Python-based utilitythat reads a CSV file of test cases — and intelligently findssemantically similarorduplicateones using:

Text EmbeddingsfromGemini AI

Cosine Similarityfor vector comparison

Smart logic todetect similar titles, steps, and expectations

And in the end, you’ll have a tool that can:

Detect overlapping test cases

Highlight duplicate coverage

Help clean up bloated test repositories

What You'll Learn (Hands-On):

By the end of this course, you'll be able to:

Parseraw test case CSV files into structured Python objects

Use Gemini embeddingsto convert titles and actions into semantic vectors

Apply cosine similarityto detect which test cases are actually “similar in meaning”

Set thresholdsto filter only truly overlapping scenarios

Export resultsinto JSON or other readable formats

Who this course is for:
- QA Engineers who want to bring AI into their toolkit
- Automation testers tired of redundant test cases
- Anyone curious about semantic search, embeddings, or LLM-powered utilities
- You don’t need prior experience in machine learning — if you know basic Python and CSV handling, you're all set!
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