Aim . The code review team at Meta is continuously improving the code review process. In this work, we report on three randomized controlled experimental trials to improve code reviewer recommendation. Method . To evaluate the recommenders, we conduct three A/B tests which are a type of randomized controlled experimental trial. The unit is either the code diff (Meta’s term for a pull-request) or all the diffs that an author creates during the experimental period. We set goal metrics, i.e., those we expect to improve, and guardrail metrics, those that we do not want to negatively impact, i.e., analogous to safety metrics in medical trials. We test the outcomes using a t -test, Wilcoxon test, or Fisher test depending on the type of data. Expt. 1 . We developed a new recommender, RevRecV2 , based on features that had been successfully used in the literature and that could be calculated with low latency. In an A/B test on 82k diffs in Spring 2022, we found that the new recommender was more accurate and had lower latency. The new recommender did not impact the amount of time a diff was under review. The results allowed us to roll-out the recommender in the Summer 2022 to all of Meta. Expt. 2 . Reviewer workload is not evenly distributed, our goal was to reduce the workload of top reviewers. Based on the literature and using historical data, we conducted backtests to determine the best measure of reviewer workload. We then ran an A/B test on 28k diff authors in Winter 2023 on a workload-balanced recommender, RevRecWL . Our A/B test led to mixed results. When a low workload reviewer had reasonable expertise, authors selected them, however, the top recommended low workload reviewer was often not selected. There was no impact on our guardrail metrics of the amount of time to perform a review. This workload-balancing replaced the recommender from the first experiment as the recommender in production at Meta. Expt. 3 . Engineers at Meta often select a team rather than an individual reviewer to review a diff. We suspected the bystander effect might be slowing down reviews of these diffs because no single individual was assigned the review. On diffs that only had a team assigned, we randomly selected one of the top three recommended reviewers to review the diff with BystanderRecRnd . We conducted an A/B test on 12.5k authors in Spring 2023 and found a large decrease in the amount of time it took for diffs to be reviewed. We did not find that reviewers rushed reviews. The results were strong enough to roll this recommender out to all diffs that only have a team assigned for review. Implications . Aside from the direct findings from our work, our findings suggest there can be a discrepancy between historical backtesting and A/B test experimental findings, and that more A/B tests are necessary to test recommenders in production. Outcome measures beyond accuracy are important. This is especially true in understanding how recommenders change a reviewer’s workload. We also see that the latency in displaying a recommendation can have a large impact on how often authors select recommendations making the reporting of latency an important metric for future work.

We describe a large-scale study to reduce the amount of time code review takes. Each quarter at Meta we survey developers. Combining sentiment data from a developer experience survey and telemetry data from our diff review tool, we address, “When does a diff review feel too slow?” From the sentiment data alone, we learn that 84.7% of developers are satisfied with the time their diffs spend in review. By enriching the survey results with telemetry for each respondent, we determined that sentiment is closely associated with the 75th percentile time in review for that respondent’s diffs, ie those that take more than 24 hours.

Many advanced safety and navigation applications in vehicles require accurate, detailed digital maps, but manual lane measurements are expensive and time-consuming, making automated techniques desirable.This paper describes a data-mining approach to map refinement, using position traces that come from Global Positioning System receivers with differential corrections.The computed lane models enable safety applications, such as lanekeeping, and convenience applications, such as lane-changing advice.Experiments show that, starting from a baseline map that is commercially available, our lane models predict a vehicle's lane with high accuracy from a small number of passes over a particular road segment.Multiple position traces are a powerful new source of data that enables cheap, automated methods of inducing lane models, as well as other geographic knowledge, like traffic signals and elevations, and potentially impacts any geographic information system with a need to relate to actual behavior.Keywords: Background knowledge, noisy data, incremental algorithms, implementation and use of KDD systems, case studies, evaluating knowledge and potential discoveries.'The GPS receivers used in this study are generally accurate to between 1 and 2 meters, whereas road lanes are about 3 to 4 meters wide.Pemissjon to make digital or hard copies of all or part of this work fol personal or classroom use is granted without fee provided that cwics are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.To CWY otherwise, to republish, to post on seners or to redistrihutc 10 Ms. requires prior specific permission and/or a fee.