AI based on GitHub Issues and Pull Requests

Introduction

What is GitHub?

GitHub is a platform for version control and collaboration, enabling developers to store, manage, and track changes to their code. It supports both public and private repositories and integrates with various tools to streamline the software development lifecycle. Ideal for teams looking to manage code repositories efficiently.

What is CogniVis AI?

CogniVis AI is a platform that enables you to pull data from different sources and combine them with each other to create practically useful AI tools. Sources may include your internal company knowledgebase and a variety of most popular business apps like GitHub, Jira, Slack, and more.

This enables you to easily create AI chatbot assistants (internal), custom generators and even embeddable AI chat widgets trained on your data.

How CogniVis AI works with GitHub?

CogniVis AI retrieves all Pull Requests and Issues from a GitHub repository. It then processes the text data to generate insights, reports, and summaries.

What exact sources are pulled:

Pull Requests (both Open & Closed)
PR title
PR summary text
PR URL
PR State (Open / Closed)
PR last update date
If the PR was merged or not
Issues (both Open & Closed)
Issue title
Issue content text
Issue update date
Issue URL

Configuration

Generate a Personal Access Token

To connect CogniVis AI with GitHub, you need to generate a Personal Access Token in GitHub. This token will be used to authenticate the connection between CogniVis AI and GitHub. See the detailed instructions in the GitHub Personal Access Tokens guide.

Sign into your GitHub account.

Navigate to Token Settings

Go to your Profile Settings. You will find the Developer settings in the left sidebar, click on that. After that choose Personal Acceess Tokens.

Generate a new token

Click on the Generate new token button, and choose Generate new token (Classic).

Open token generation page

Name your token something like "CogniVis Connector".
Set the expiration date. Remember to generate a new token when this one expires.
Check the checkbox next to all repo type permissions.

Add the connector to CogniVis

Navigate to CogniVis Connectors

In CogniVis, go to Admin Panel, then in the sidebar choose Add connector. You will find the GitHub connector setup page there.

Open GitHub connector setup page

Add your GitHub token

Add your GitHub token & save it.

Specify the repo

Specify the repositories you want to Index by inputing the owner and repository names.

Free trial for GitHub AI

If you are interested in creating your own automations and workflows with artificial intelligence based on GitHub data, you can request a free trial of our GitHub AI solution. Please book a demo with us to get started.

book a demo

GitHub AI on-premise

Enterprise organizations can choose to deploy this connector on-premise. On-premise deployment provides additional security and privacy, it means that the connector will be hosted on your own servers which you can control and manage.

This option is suitable for organizations that have strict data privacy and security requirements, want to integrate with their existing infrastructure, or need to comply with specific local regulations.

Contact our team & learn about options of deploying this connector on premise.

book a demo learn more about on-premise ai

Use cases

Check out potential benefits and use cases for this connector.

Automated Code Review Summaries

CogniVis AI can analyze PR summary titles, texts, and comments to generate concise, high-level summaries of code reviews. This enables team members to quickly understand the scope and feedback of each pull request without digging through extensive discussions.

Enhanced Bug Tracking

By processing issue texts and comments, the AI can categorize and prioritize bugs based on severity and frequency. This helps development teams focus on critical issues first, improving project timelines and software quality.

Contributor Performance Insights

Using PR and issue authorship data, the AI can generate performance metrics for individual contributors. This allows project managers to identify top performers and areas where team members may need additional support or training.

Trend Analysis for Development

CogniVis AI can track trends in issues and pull requests over time, identifying recurring problems or popular feature requests. This enables better planning and allocation of resources to address long-term project needs.

Automated Documentation Updates

The AI can extract relevant information from PRs and issues to automatically update project documentation. This ensures that documentation stays current with the latest changes and reduces the manual effort required to maintain it.

Predictive Issue Resolution

By analyzing historical issue texts and resolutions, the AI can suggest potential solutions for new issues based on similar past cases. This accelerates the problem-solving process and improves response times.

Enhanced Communication

Using issue comments and PR discussions, the AI can generate summaries and notifications for other communication channels like Slack. This keeps the entire team updated on important changes and discussions without needing to constantly check GitHub.

Quality Assurance Automation

CogniVis AI can identify patterns in issue comments and PR reviews that indicate potential quality concerns. This allows QA teams to focus on specific areas that may need more rigorous testing or code review.

Prioritized Task Lists

By analyzing the text and comments of issues and PRs, the AI can create prioritized task lists for developers. This helps in managing workloads efficiently and ensures that high-priority tasks are addressed promptly.

Stakeholder Reports

The AI can compile data from PRs and issues to create detailed reports for stakeholders, summarizing project progress, challenges, and upcoming tasks. This provides a clear and concise overview, facilitating better decision-making and communication.

Enhanced Bug Tracking