Closed-Loop Feedback
Bridge the gap between production errors and source code with automatic traceability. When an error occurs in production, Qommity instantly maps it to the exact pull request, commit, and line of code that introduced it. No more hunting through git history or guessing which change caused the incident.
The debugging death spiral
- ×Production errors happen, but finding which code change caused them takes hours of manual investigation
- ×Engineers waste time hunting through git history, Jira tickets, and Slack threads to trace the origin
- ×By the time the root cause is found, the context has faded and the fix is more expensive
- ×The same bugs keep resurfacing because lessons from incidents never make it back to the development phase
Connect every error to its source
Qommity automatically traces production errors back to the exact pull request, commit, and line of code that introduced them. Our AI analyzes the change context and generates fix suggestions with test scenarios, turning incident response from detective work into a structured workflow.
Automatic error linking
When an error occurs in production, Qommity's deployment tracking immediately identifies which release introduced it. The system correlates error signatures with code changes, presenting a direct link from the production incident to the PR that caused it. No more guessing which commit is responsible— engineers see the exact change, author, and deployment context within seconds.
AI-generated root cause analysis
Beyond simple linking, Qommity's AI examines the problematic code change to understand why it failed. It analyzes the diff, considers the runtime environment, and generates a detailed root cause explanation. The AI suggests specific fixes with code examples, estimating the effort required and flagging any potential side effects of the proposed changes.
Prevent regression with generated tests
Every incident becomes a learning opportunity. Qommity automatically generates test scenarios that would have caught the bug, suggesting where to add them in your test suite. These AI-generated tests cover edge cases and failure modes that human reviewers often miss, building a defensive test suite that prevents similar issues from reaching production again.
Technical Specifications
Version Control
- GitHub
- GitLab
- Bitbucket
Languages
- Node.js
- Java Spring Boot
Cloud Platforms
- AWS
- GCP
- Azure
Error Tracking
- Sentry
- Datadog
- Custom Webhooks
Related Features
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