| File Name: | AI for QA: Detect Duplicate Test Cases Using AI |
| Content Source: | https://www.udemy.com/course/ai-for-qa-detect-duplicate-test-cases-using-ai |
| Genre / Category: | Other Tutorials |
| File Size : | 374.5 MB |
| Publisher: | Prateek Sethi |
| Updated and Published: | July 26, 2025 |
Hi there, and welcome to “AI for QA: Detect Duplicate Test Cases Using AI” — a hands-on course where we combine the power of Python, Large Language Models (LLMs) like Gemini, and vector similarity to solve a real-world problem in QA and software testing.
If you’ve ever worked with hundreds of test cases and 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 to build a Python-based utility that reads a CSV file of test cases — and intelligently finds semantically similar or duplicate ones using:
- Text Embeddings from Gemini AI
- Cosine Similarity for vector comparison
- Smart logic to detect 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:
- Parse raw test case CSV files into structured Python objects
- Use Gemini embeddings to convert titles and actions into semantic vectors
- Apply cosine similarity to detect which test cases are actually “similar in meaning”
- Set thresholds to filter only truly overlapping scenarios
- Export results into JSON or other readable formats
DOWNLOAD LINK: AI for QA: Detect Duplicate Test Cases Using AI
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