- Automating the generation of code summaries and reviews can save developers and reviewers valuable time, allowing them to focus on more complex issues that require human intervention.
- AI can provide a level of consistency in code reviews that is difficult to achieve with reviews done exclusively by humans, especially in large teams.
- Over time, AI can be trained to identify common error patterns or poor coding practices, making it a learning tool for developers.
Feature | Availability |
---|---|
Code Review | โ |
Code Suggestion | โ |
Fine-tunning | โ |
Commit Suggestion | โ |
Code context | โ |
Comando | Descriรงรฃo |
---|---|
npm install |
Install all dependencies |
npm run dev |
Generate AI reviews of your code integrated with GitHub |
Variable | Description | Required |
---|---|---|
OPENAI_API_KEY | OpenAI API Key (ChatGPT) | โ |
GITHUB_AUTH_TOKEN | GitHub Access Token for Applications | โ |
GITHUB_ORGANIZATION | Name of the organization or user who will have access to the repository for review | โ |
GITHUB_REPOSITORY | Name of the repository that will have the PRs reviewed | โ |
GITHUB_PULL_NUMBER | Pull Request number that will have the commits retrieved for review | โ |
graph TD;
A[Open PR on GitHub] --> B[GitHub API: Get Latest PR]
B --> C[GitHub API: Get Commit History]
C -->|Commits Found| D[Send History to IA]
C -->|No Commits| E[Feedback: PR with no commits to review]
D --> F{IA Processes Commits?}
F -->|Yes| G[IA: Create Commit Summary]
F -->|Yes| H[IA: Generate Code Review]
F -->|No| I[Feedback: Failed to process commits]
G --> J[Generate Comment on PR with Summary]
H --> J
I --> K[Add Failed Label on PR]
E --> L[Add Label with No Commits on PR]