Team-related Features in Code Review Prediction Models

• URL: http://arxiv.org/abs/2312.06244v1
• Date: Mon, 11 Dec 2023 09:30:09 GMT
• Title: Team-related Features in Code Review Prediction Models
• Authors: Eduardo Witter and Ingrid Nunes and Dietmar Jannach
• Abstract summary: We evaluate the prediction power of features related to code ownership, workload, and team relationship. Our results show that, individually, features related to code ownership have the best prediction power. We conclude that all proposed features together with lines of code can make the best predictions for both reviewer participation and amount of feedback.
• Score: 10.576931077314887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern Code Review (MCR) is an informal tool-assisted quality assurance practice. It relies on the asynchronous communication among the authors of code changes and reviewers, who are developers that provide feedback. However, from candidate developers, some are able to provide better feedback than others given a particular context. The selection of reviewers is thus an important task, which can benefit from automated support. Many approaches have been proposed in this direction, using for example data from code review repositories to recommend reviewers. In this paper, we propose the use of team-related features to improve the performance of predictions that are helpful to build code reviewer recommenders, with our target predictions being the identification of reviewers that would participate in a review and the provided amount of feedback. We evaluate the prediction power of these features, which are related to code ownership, workload, and team relationship. This evaluation was done by carefully addressing challenges imposed by the MCR domain, such as temporal aspects of the dataset and unbalanced classes. Moreover, given that it is currently unknown how much past data is needed for building MCR prediction models with acceptable performance, we explore the amount of past data used to build prediction models. Our results show that, individually, features related to code ownership have the best prediction power. However, based on feature selection, we conclude that all proposed features together with lines of code can make the best predictions for both reviewer participation and amount of feedback. Regarding the amount of past data, the timeframes of 3, 6, 9, and 12 months of data produce similar results. Therefore, models can be trained considering short timeframes, thus reducing the computational costs with negligible impact in the prediction performance ...

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