Learning Codacy

This repository is dedicated to learning about the code review automation tool Codacy, which helps improve code quality by analyzing codebases and providing actionable feedback.

   

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Introduction

Codacy is an automated code review tool that integrates seamlessly with GitHub and other version control platforms. It performs static code analysis, identifies code smells, and enforces best practices to streamline development workflows.

Steps to Set Up Codacy

1. Create an Account

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

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2. Add Your Repository

1. On the Codacy dashboard, click "Add a repository".
2. Select the repository (Learning-Codacy) you want Codacy to analyze.
   • Ensure Codacy has the required access permissions.
3. Codacy will perform an initial scan and generate a baseline report.

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3. Test with Deliberate Code Issues

To test Codacy’s capabilities, include intentional errors in your code, such as naming inconsistencies, duplication, or missing error handling. Observe how Codacy detects and reports these issues.

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4. Integrate Codacy with GitHub

1. Enable GitHub integration in Codacy:
   • Go to Settings > Integrations in your Codacy dashboard.
   • Activate GitHub Status Checks to view analysis results directly in your pull requests.
2. Once integrated, Codacy will provide feedback automatically during your development process.

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Analysis and Evaluation

Benefits of Integration

• Automatic Code Reviews: Save time by automating style and complexity checks.
• Feedback in Pull Requests: View issues directly in GitHub pull requests.
• Customizable Rules: Focus on the metrics that matter most for your project.
• Improved Code Quality: Maintain consistent standards across your team.

Example Workflow

1. Developer submits a pull request.
2. CodeFactor/Codacy analyzes the code changes.
3. Issues and suggestions are displayed directly in the pull request.
4. Developer resolves the issues before merging.

Troubleshooting

• Analysis Not Triggering: Ensure the repository is public or that the service has access to your private repository.

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Directory Structure

📂 Learning-Codacy
├── 📁 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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