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Modern Code Review (MCR) is an informal tool-assisted quality assurancepractice. It relies on the asynchronous communication among the authors of codechanges and reviewers, who are developers that provide feedback. However, fromcandidate developers, some are able to provide better feedback than othersgiven a particular context. The selection of reviewers is thus an importanttask, which can benefit from automated support. Many approaches have beenproposed in this direction, using for example data from code reviewrepositories to recommend reviewers. In this paper, we propose the use ofteam-related features to improve the performance of predictions that arehelpful to build code reviewer recommenders, with our target predictions beingthe identification of reviewers that would participate in a review and theprovided amount of feedback. We evaluate the prediction power of thesefeatures, which are related to code ownership, workload, and team relationship.This evaluation was done by carefully addressing challenges imposed by the MCRdomain, such as temporal aspects of the dataset and unbalanced classes.Moreover, given that it is currently unknown how much past data is needed forbuilding MCR prediction models with acceptable performance, we explore theamount of past data used to build prediction models. Our results show that,individually, features related to code ownership have the best predictionpower. However, based on feature selection, we conclude that all proposedfeatures together with lines of code can make the best predictions for bothreviewer participation and amount of feedback. Regarding the amount of pastdata, the timeframes of 3, 6, 9, and 12 months of data produce similar results.Therefore, models can be trained considering short timeframes, thus reducingthe computational costs with negligible impact in the prediction performance...

Team-related Features in Code Review Prediction Models