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Version Control

This document outlines the Git version control strategy and development workflow for the JLR AI Benchmarking Dashboard project. It details the branching strategy, commit message guidelines, pull request workflow, and collaboration rules, emphasizing the importance of peer review and structured commits. Additionally, it provides useful Git commands and recommended tools for contributors, along with contact information for the project maintainer.

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8 views3 pages

Version Control

This document outlines the Git version control strategy and development workflow for the JLR AI Benchmarking Dashboard project. It details the branching strategy, commit message guidelines, pull request workflow, and collaboration rules, emphasizing the importance of peer review and structured commits. Additionally, it provides useful Git commands and recommended tools for contributors, along with contact information for the project maintainer.

Uploaded by

Mysterious Mr.Mister
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Version Control & Development Workflow

Guide
JLR AI Benchmarking Dashboard
Version 1.0 – July 2025

1. Purpose
This document defines the Git version control strategy, branch management policy, and team
workflow guidelines for the JLR-AI-Benchmark-Prediction project.

2. Repository
GitHub URL: https://github.com/Parth-Parakhiya/JLR-AI-Benchmark-Prediction

3. Branching Strategy
The project uses a simplified variant of GitFlow.

Main Branches
• main – Always reflects production-ready code.
• dev – Active development and feature integration.

Supporting Branches
• feature/<name> – New features (e.g., feature/nn-model)
• bugfix/<name> – Minor fixes or patches
• hotfix/<name> – Urgent fixes to main
• docs/<section> – Documentation updates (optional)

4. Commit Message Guidelines


Use clear and structured commit messages:
• Format: [type]: short message
• Example: feat: add GPT-2 model predictor
Common Types
• feat – New feature

• fix – Bug fix

• docs – Documentation changes

• style – Formatting, missing semicolons, etc.

• refactor – Code restructuring (no behavior change)

• test – Adding or modifying tests

• chore – Build or tooling changes

5. Pull Request (PR) Workflow


1. Fork or clone the repo and create a new branch from dev.

2. Implement the feature or fix.

3. Ensure all changes pass:

• pytest for unit tests


• black or flake8 for formatting

4. Push your branch:


git push origin feature/my-feature

5. Open a PR to merge into dev, not main.

6. Assign reviewers and write a clear PR title and description.

7. Once approved, the maintainer merges to main when stable.

6. Release Tagging
Use annotated tags for production-ready versions:
git tag -a v1.0.0 -m "Initial release"
git push origin v1.0.0

Versioning follows Semantic Versioning (SemVer): MAJOR.MINOR.PATCH (e.g., 1.0.0)


7. Collaboration Rules
• Always branch off from dev, never from main.

• Never commit directly to main.

• Every commit should represent a functional or isolated change.

• All changes must be peer reviewed before merging.

• Resolve merge conflicts before submitting PRs.

8. Useful Git Commands


# Create a new branch
git checkout -b feature/gpu-parser

# Stage and commit


git add .
git commit -m "feat: add GPU parsing logic"

# Push to origin
git push origin feature/gpu-parser

# Fetch and rebase


git pull origin dev --rebase

# Merge to dev
git checkout dev
git merge feature/gpu-parser

9. Recommended Tools
• GitHub Desktop – For GUI users

• GitKraken, SourceTree – Graphical Git tools

• GitHub Projects – For task and sprint management

10. Contributor Contact


• Maintainer: Parth Parakhiya

• Email: parakhi@uwindsor.ca

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