Here is a workflow to leverage the PyTorch git commit logs as input for analysis:
Clone the PyTorch repo and extract the commit history
For each commit:
Extract key metadata like author, date, files changed, commit message
Use natural language processing to analyze commit messages and generate a summary of code changes
Categorize commits by type (new feature, bug fix, refactor, docs etc) using ML classifiers on commit messages
Detect entities like modules, classes, methods changed from files modified
Generate analytics on commit activity:
Commission time series by date, author, commit type
Network graph of code dependencies and coupling based on files changed
Identify major contributors, most active components, change frequency
Surface insights and metrics in a dashboard:
Interactive graphs and charts to slice data
Top contributor leaderboards
Trends in commit types, focus areas
Feed commit data into collaborative review workflows:
Review impact and complexity of new features
Assess architectural changes and refactors
Improve commit message quality and consistency
This provides a continuous stream of commit activity data to power workflows for understanding PyTorch development and improving the project. Let me know if you need any clarification or have additional requirements!
Here is a workflow to leverage the PyTorch git commit logs as input for analysis:
Clone the PyTorch repo and extract the commit history
For each commit:
Extract key metadata like author, date, files changed, commit message
Use natural language processing to analyze commit messages and generate a summary of code changes
Categorize commits by type (new feature, bug fix, refactor, docs etc) using ML classifiers on commit messages
Detect entities like modules, classes, methods changed from files modified
Commission time series by date, author, commit type
Network graph of code dependencies and coupling based on files changed
Identify major contributors, most active components, change frequency
Interactive graphs and charts to slice data
Top contributor leaderboards
Trends in commit types, focus areas
Review impact and complexity of new features
Assess architectural changes and refactors
Improve commit message quality and consistency
This provides a continuous stream of commit activity data to power workflows for understanding PyTorch development and improving the project. Let me know if you need any clarification or have additional requirements!