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
...

现代代码审查是一种非正式的工具辅助质量保证实践，可以通过使用团队相关特征来提高代码评审人员推荐器的性能，并评估过去数据的使用量对建立具有可接受性能的预测模型的影响。

代码审查预测模型中的团队相关特征

Team-related Features in Code Review Prediction Models

Peer review is the method employed by the scientific community for evaluating
research advancements. In the field of cybersecurity, the practice of
double-blind peer review is the de-facto standard. This paper touches on the
holy grail of peer reviewing and aims to shed light on the performance of AI in
reviewing for academic security conferences. Specifically, we investigate the
predictability of reviewing outcomes by comparing the results obtained from
human reviewers and machine-learning models. To facilitate our study, we
construct a comprehensive dataset by collecting thousands of papers from
renowned computer science conferences and the arXiv preprint website. Based on
the collected data, we evaluate the prediction capabilities of ChatGPT and a
two-stage classification approach based on the Doc2Vec model with various
classifiers. Our experimental evaluation of review outcome prediction using the
Doc2Vec-based approach performs significantly better than the ChatGPT and
achieves an accuracy of over 90%. While analyzing the experimental results, we
identify the potential advantages and limitations of the tested ML models. We
explore areas within the paper-reviewing process that can benefit from
automated support approaches, while also recognizing the irreplaceable role of
human intellect in certain aspects that cannot be matched by state-of-the-art
AI techniques.

本研究通过比较人工评审者和机器学习模型得出的结果，研究了 AI 在学术安全会议评审中的表现，并以 ChatGPT 和基于 Doc2Vec 模型的两阶段分类方法为基础，评估了文献评审结果的预测能力，发现 Doc2Vec 方法的实验评估表现明显优于 ChatGPT，达到了超过 90% 的准确率。同时分析了测试的 ML 模型的潜在优势和限制，并探索了文献评审过程中能够从自动化支持方法中获益的领域，同时也认识到在某些方面，尖端 AI 技术无法替代人类智慧的不可替代角色。