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ML for QA automation
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Module 4
Classical ML: Building Predictive Models
1
Balancing Imbalanced Software Quality Datasets with Over/Under-sampling
Handle class imbalance in software quality datasets using over/under-sampling techniques (e.g., SMOTE).
2
Detecting Flaky Tests with Binary Classification
Train a binary classifier (e.g., RandomForest, XGBoost) to detect flaky tests from engineered features.
Prioritizing Technical Debt with Learning-to-Rank
Train a learning-to-rank model to prioritize technical debt items by estimated impact.
4
Evaluating Classifiers on Imbalanced QA Datasets
Evaluate classifier performance using precision, recall, and F1-score on imbalanced QA datasets.
5
Ranking Model Evaluation Metrics
Evaluate ranking model performance using metrics such as nDCG or Mean Average Precision.
Explaining Model Predictions with Feature Importance & SHAP
Interpret model predictions for stakeholder communication using feature importance or SHAP.
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Classical ML: Feature Engineering for Code
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Deep Learning: Code as Data with Embeddings