Uber uReview Code Review Comments
Suggested workflow: Generate and evaluate code review comments, filtering out low-value feedback. Task: Uber's uReview system employs three generators to produce code review comments, followed by an expert evaluator that assesses each comment. Low-value categories are filtered out before presentation. The workflow integrates automated generation with quality assessment to refine feedback.; tools: Tool described in the source. Human role: Not stated in the source; limitations: Specific implementation details, such as the exact models used for generation and evaluation, are not fully detailed in the source. Integration costs and maintenance requirements for the multi-generator setup are unknown. Human oversight roles beyond the initial filtering are not specified..
Tools
Tool described in the source
Published on Agent Work Atlas:
Overview
Suggested workflow: Generate and evaluate code review comments, filtering out low-value feedback.
Workflow
Uber's uReview system employs three generators to produce code review comments, followed by an expert evaluator that assesses each comment. Low-value categories are filtered out before presentation. The workflow integrates automated generation with quality assessment to refine feedback.
Reported result
The system achieved a 75% rating of usefulness for generated comments, with over 65% of those comments actually addressed by developers. These metrics indicate high relevance and actionable output from the filtered review process.
Human role
Not stated in the source
Limitations
Specific implementation details, such as the exact models used for generation and evaluation, are not fully detailed in the source. Integration costs and maintenance requirements for the multi-generator setup are unknown. Human oversight roles beyond the initial filtering are not specified.
Try it
I want to implement a code review comment generation and filtering pipeline similar to Uber's uReview. 1. **Setup**: Create three distinct prompt templates for generating code review comments (e.g., security, performance, style). 2. **Generation**: For a given pull request diff, run all three generators to produce candidate comments. 3. **Evaluation**: Implement an 'expert' evaluator prompt that scores each comment on usefulness and filters out low-value ones. 4. **Output**: Return only the high-scoring comments. **Inputs**: [Replaceable: Pull Request Diff], [Replaceable: Codebase Context]. **Deliverables**: A JSON list of filtered comments with scores. **Quality Check**: Manually review the first 10 outputs to ensure the evaluator correctly filters noise. **Safeguards**: Do not auto-post comments; require human approval before sending to the PR. **Missing Inputs**: If the diff is too large, summarize it first. If the evaluator fails, return all comments with a warning flag.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.