Generative AI for QA: Detecting Duplicate Test Cases (2026)

Large QA teams often maintain hundreds or thousands of test cases, many of which unintentionally overlap due to similar scenarios written with different wording. This leads to redundant coverage, longer execution times, and increased maintenance cost.This course is designed for QA Engineers, Test Automation Engineers, and QA Leads who want to use Generative AI techniques to identify duplicate and semantically similar test cases in a practical, scalable way.In this hands-on course, you will build a Python-based utility that analyzes test cases stored in CSV format and detects redundant coverage using:Text embeddings from LLMsVector similarity techniquesCosine similarity scoringCustom thresholds for semantic matchingRather than relying on exact text matching, the solution focuses on semantic similarity, allowing you to detect test cases that mean the same thing even when written differently.By the end of the course, you will have a working tool that can:Detect overlapping and duplicate test casesHighlight redundant functional coverageHelp teams clean and optimize large test repositoriesSupport smarter test suite maintenance decisionsAll concepts are implemented step by step using real QA data formats, following industry-style problem-solving, not theoretical AI demos.What you will be able to do after this courseParse test cases from CSV files into structured Python objectsConvert test titles and steps into semantic vector embeddingsMeasure similarity between test cases using cosine similarityTune similarity thresholds to avoid fals