Learning-CodeFactor

This repository is dedicated to exploring CodeFactor, an automated code review tool that provides real-time feedback to improve code quality. By analyzing code for style issues, complexity, and potential bugs, CodeFactor ensures clean and maintainable code.

Introduction

CodeFactor provides automated code quality reviews directly integrated with GitHub and other platforms. It identifies code smells, style issues, and best practice violations, streamlining code review processes for developers.

Steps to Set Up CodeFactor

1. Create an Account

1. Visit CodeFactor and sign up using your GitHub account.
   • Grant the necessary permissions for CodeFactor to access your repositories.
2. After authorization, you will be redirected to your CodeFactor dashboard.

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2. Add Your Repository

1. From the CodeFactor dashboard, click on "Add New Repository".
2. Select the repository (Learning-CodeFactor) you want to monitor.
   • Ensure CodeFactor has the appropriate access permissions.
3. CodeFactor will perform an initial scan of your repository and generate a code quality report.

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3. Introduce Deliberate Code Issues

To test CodeFactor’s analysis:

• Add some intentional issues like inconsistent indentation, unused variables, or overly complex functions.
• Observe how CodeFactor detects and highlights these problems.

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4. Integrate CodeFactor with GitHub

1. Enable GitHub integration to receive feedback directly in your pull requests:
   • Go to Settings > GitHub Integration in your CodeFactor dashboard.
   • Enable Status Checks to display analysis results on GitHub pull requests.
2. From now on, CodeFactor will analyze every commit and provide actionable insights.

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Analysis and Evaluation

Benefits of Integration

• Real-Time Feedback: View analysis results for every commit and pull request.
• Enhanced Maintainability: Ensure code adheres to best practices.
• Seamless GitHub Integration: Feedback is displayed directly within the GitHub workflow.
• Customizable Rules: Tailor analysis to meet your team's specific standards.

Example Workflow

1. Developer submits a pull request.
2. CodeFactor analyzes the changes and provides inline feedback.
3. Developers can review, fix issues, and merge confidently.

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Troubleshooting

• No Analysis for Commits: Ensure the repository is public or that CodeFactor has access to private repositories.
• Missing Feedback in Pull Requests: Verify that GitHub integration and Status Checks are enabled in your settings.

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Directory Structure

📂 Learning-CodeFactor
├── 📁 Codes
│   ├── 📄 sample_code_corrected.py               # Testing script for the corrected workflow, ensuring proper functionality of the Streamlit app.
│   ├── 📄 sample_code_with_errors.py             # Script demonstrating the erroneous workflow for analysis and debugging.
│   ├── 📄 sample_code_with_errors_codefactor.py  # Script showcasing the error-prone workflow updated for CodeFactor review and improvements.
│
├── 📁 Documentation Files
│   ├── 📄 Code Review Automation.md              # Sprint planning document outlining the development process and project timeline.
│   ├── 📄 Code Review Automation.pdf             # A formatted PDF report summarizing project outputs and features for sharing and printing.
│
├── 📁 Output
│   ├── 📄 Experiment 9 Output.docx               # Word document explaining the experiment's results in detail.
│   ├── 📄 Experiment 9 Output.pdf                # PDF version of the experiment's outputs for easy distribution.
│
├── 📄 README.md                                  # Overview of the project, including purpose, setup instructions, and key features.
├── 📄 LICENSE.md                                 # License information governing the usage, distribution, and modification of the project.

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