Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
ML for QA automation
·
Module 3
Classical ML: Feature Engineering for Code
1
Feature Engineering from Git History
Engineer features from git history, such as change frequency, author count, and recency of changes.
Analyzing Test Flakiness Metrics
Compute aggregate flakiness features from historical test execution patterns (e.g., failure rate, rerun success ratio).
3
Calculating Technical Debt Proxy Scores
Calculate technical debt proxy scores from static analysis output.
Building Your Feature Matrix
Assemble engineered features into a structured feature matrix ready for model training.
Previous module
Data Collection: Mining Software Repositories
Next module
Classical ML: Building Predictive Models