Introduction
What is GitLab?
GitLab is a web-based DevOps lifecycle tool that provides a Git repository manager with features like CI/CD, issue tracking, and version control. It enables teams to collaborate on code, manage projects, and automate the deployment process, enhancing software development efficiency and quality.
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 GitLab?
GitLab Connector retrieves all Merge Requests and Issues from a specified GitLab repository. It then processes the text data to generate insights, reports, and summaries.
What exact sources are pulled:
- Merge Requests (both Open & Closed)
- MR title
- MR summary text
- MR URL
- MR State (Open / Closed)
- MR last update date
- MR creator
- If the MR was merged or not
- Issues and Incidents (both Open & Closed)
- Issue title
- Issue content text
- Issue comments
- Issue update date
- Issue URL
- Issue creator
Configuration
Generate a Personal Access Token
To connect CogniVis AI with GitLab, you need to generate a Personal Access Token in GitLab. This token will be used to authenticate the connection between CogniVis AI and GitLab. See the detailed instructions in the GitLab Personal Access Tokens guide.
Sign In
Sign into your GitLab account.
Navigate to Token Settings
Go to your Profile Settings. Select Access Tokens in the left sidebar.
Open token generation pageGenerate a new token
Click on Add new token button
- Name your token something like "CogniVis Connector".
- Set the expiration date. Remember to generate a new token when this one expires.
- Choose the desired scopes for your project. You can find additional details here.
Add the connector to CogniVis
Navigate to CogniVis Connectors
In your platform, go to the Admin Panel, which you can find in the top right corner of the screen.
Go to the Add Connector section in the menu on the left side of the screen.
Then, search for and select the Gitlab connector.
Add your GitLab URL and token
Add your GitLab URL, token & save it.
Specify the repo
Specify the repositories you want to Index by inputing the owner and repository names.
Free trial
If you are interested in creating your own automations and workflows with artificial intelligence based on your data, you can request a free trial of our solution. Please book a demo with us to get started.
book a demoOn-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 aiUse cases
Check out potential benefits and use cases for this connector.
Enhanced Project Management
Utilizing data from GitLab, AI can provide real-time insights into project status, identify bottlenecks, and prioritize tasks. By analyzing issues, merge requests, and their states, the AI can help project managers distribute workloads more efficiently and ensure timely completion of milestones.
Automated Code Review Summaries
By pulling data from merge requests, AI can generate summaries of code changes, highlight important updates, and flag potential issues. This reduces the time spent on manual code reviews and ensures that crucial changes are not overlooked, streamlining the development process.
Predictive Issue Resolution
AI can analyze past issues, incidents, and their resolutions to predict potential problems in current projects. By identifying patterns and common causes, it can suggest proactive measures, helping teams to address issues before they escalate, ultimately improving software stability.
Developer Performance Analytics
Using data on merge requests and issue updates, AI can provide insights into individual and team performance. Metrics like the number of resolved issues, merged requests, and response times can be tracked, allowing for data-driven performance reviews and identifying areas for skill development.
Automated Documentation Updates
AI can pull and analyze comments and summaries from issues and merge requests to automatically update project documentation. This ensures that documentation is always current and reflects the latest changes, reducing the manual effort required to maintain it and enhancing team productivity.
Enhanced Collaboration Insights
By analyzing comments and interactions on issues and merge requests, AI can provide insights into team collaboration patterns. It can identify communication gaps and suggest ways to improve team interaction, fostering a more collaborative and productive work environment.
Incident Response Optimization
AI can review past incidents and their resolutions to optimize current incident response strategies. By understanding the effectiveness of past actions, it can suggest the best courses of action for new incidents, reducing downtime and improving system reliability.
Customizable Alerts and Notifications
AI can set up and manage customizable alerts based on the data from GitLab. For example, it can notify relevant team members about critical issues, unreviewed merge requests, or impending deadlines, ensuring that important tasks are not neglected and facilitating prompt action.
Release Management Insights
By analyzing the state and history of merge requests and issues, AI can provide insights into the readiness of a project for release. It can identify potential risks, incomplete tasks, and other factors that could impact the release schedule, helping teams to plan and execute releases more effectively.
Continuous Improvement Feedback
AI can aggregate and analyze feedback from issue comments and merge request discussions to identify recurring issues and areas for improvement. This continuous feedback loop helps teams to refine their processes, adopt best practices, and enhance overall project quality.