Code and Data for Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models

This repository contains the source code, dataset, and experiments conducted for the paper titled "Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models". The work explores using data augmentation techniques like SMOTE for flaky test detection and bias evaluation in machine learning models.

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