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ML for QA automation
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Module 2
Data Collection: Mining Software Repositories
1
Analyzing Git Repository Metrics with GitPython
Extract commit history and code churn metrics from a Git repository using GitPython.
2
Parsing Python with `ast`
Parse Python source code into Abstract Syntax Trees (AST) using the `ast` module.
3
Calculating Code Complexity from an AST
Compute code complexity metrics, such as cyclomatic complexity, from an AST.
Analyzing CI/CD Test Logs for Pass/Fail and Timing
Parse CI/CD test execution logs to extract pass/fail histories and timing data.
Identifying Flaky Test Indicators
Define and extract concrete flakiness indicators from test logs (e.g., passed-on-rerun, timeout errors).
6
Mapping Test Dependencies
Identify and map test dependencies by analyzing build configuration files or code imports.
Previous module
Foundations: ML for Software Quality
Next module
Classical ML: Feature Engineering for Code