Category · 12 of 30 articles
How to run an evaluation, a trial or a rollout without wasting a quarter on it.
Most AI reviewers catch generic bugs. Whether one will follow your team's own coding rules is a separate question. Here's how to evaluate custom-standards support before you buy.
Ask the right question: AI review tools cut how long PRs WAIT, not much how long they take to READ. Plus a two-week PR-slice protocol.
Teams drowning in AI-generated code often let an LLM review the LLM's own patches. Amazon's judge-correlation work shows why that misses real defects.
Atlassian says Rovo cut PR cycle time 45%. The number is real but self-attested. Here's how to measure whether AI actually reduces your review time.
The volume of AI-generated code is rising faster than review capacity. The fix starts in evaluation design: don't let the model that wrote the code also judge it.
AI is producing more code than teams can review. First-party data on why the old loop breaks (Salesforce, DORA) and what actually scales.
AI made PRs smaller but much more numerous. Reviewing the volume isn't a per-PR speed problem, it's a routing problem. Here's how teams actually triage AI-generated code.
The failure mode that matters in AI review is not missing a bug, it is fluent output that is structurally wrong and easy to trust. How to benchmark for it.
How Martian's Code Review Bench separates reproducible fixed-dataset evals from streaming real-world evals, and the tradeoffs hidden in each.
A practical playbook for how to evaluate AI code review tools: a 9-standard scoring rubric, red flags, a 2-week trial protocol, and vendor questions.
Open source AI code review tools compared: Kodus (AGPL), PR-Agent (MIT), and more — real licenses, BYOK costs, and how they stack up against closed SaaS.
Self-hosted AI code review explained: full-stack vs BYOK vs on-prem runners, verified vendor options, and what deployment really costs in 2026.