uReview: Scalable, Trustworthy GenAI for Code Review at Uber
Uber's code reviewers were overloaded by the rising volume of changes driven by AI-assisted development, leaving insufficient time to catch subtle bugs, security vulnerabilities, and best-practice violations—leading to missed errors, slower feedback loops, production incidents, and wasted resources.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Developer submits code change
When a developer submits a change on Uber's code review platform, uReview's automated review process begins.
uReview is deployed across all six of Uber's monorepos, analyzes over 90% of the approximately 65,000 weekly diffs, maintains a 75% usefulness rate, sees 65% of its comments addressed in the same changeset, and saves approximately 1,500 developer hours per week—equivalent to nearly 39 developer years annually—with a median review turnaround of 4 minutes.
What failed first
Third-party AI code-review tools were evaluated and found unsuitable: most required GitHub (Uber uses Phabricator), suffered from many false positives and low-value true positives, and could not interact with Uber's internal systems. Their per-diff costs at Uber's scale were also an order of magnitude higher than running uReview in-house.