Code review is an important gate for code changes, but review effort and expertise are limited. Defect prediction and just-in-time risk modeling aim to identify changes that are most likely to introduce defects so that teams can focus additional attention where it is most useful. We study whether risk-aware interventions, targeted specifically at these high-risk changes, can improve review outcomes while preserving developer velocity. In our setting, risk refers to the likelihood that a code change will introduce a defect after landing. We quantify this defect risk with the Diff Risk Score (DRS) used to identify high-risk diffs and investigate three research questions related to code review: (RQ1) whether a dedicated workflow improves review behavior on high-risk diffs, (RQ2) whether we can design a reviewer recommender tailored to risky diffs, and (RQ3) how well that new recommender performs in an A/B test across thousands of high risk diffs.

For RQ1, we introduce the Risky Diff Reviewer (RDR) workflow, which assigns an additional recommended reviewer for high-risk diffs and shows a bypassable pre-land dialog when authors attempt to land without resolving the risk signal. In a randomized trial on the top 5% of diffs by diff defect risk (over 15k diffs), RDR increases review depth and collaboration and increases the fraction of diffs whose final version falls below the high-risk threshold by 9.56%, with a minor 0.45% increase in diff reviewing time.

For RQ2, we design a new reviewer recommender (RecRDR) for risky diffs. RecRDR uses a continuous, weighted ground truth that emphasizes file experience, author collaboration, interaction quality, and recency. In offline backtests, RecRDR improves Top-1 and Top-3 action rates over the baseline recommender.

For RQ3, we evaluate RecRDR using an A/B test within the RDR workflow on over 5k diffs. RecRDR increases engagement from the recommended reviewer, with 9.94% more comments and 7.77% more acceptances, and we do not observe statistically significant regressions in guardrail metrics such as diff reviewing and processing time.