Using a context-aware approach to recommend code reviewers : findings from an industrial case study

Code review is a commonly used practice in software development. It refers to the process of reviewing new code changes before they are merged with the code base. However, to perform the review, developers are mostly assigned manually to code changes. This may lead to problems such as: a time-consuming selection process, limited pool of known candidates and risk of over-allocation of a few reviewers. To address the above problems, we developed Carrot, a machine learning-based tool to recommend code reviewers. We conducted an improvement case study at Ericsson. We evaluated Carrot using a mixed approach. we evaluated the prediction accuracy using historical data and the metrical Mean Reciprocal Rank (MRR). Furthermore, we deployed the tool in one Ericsson project and evaluated how adequate the recommendations were from the point of view of the tool users and the recommended reviewers.We also asked the opinion of senior developers about the usefulness of the tool. The results show that Carrot can help identify relevant non-obvious reviewers and be of great assistance to new developers. However, there were mixed opinions on Carrot's ability to assist with workload balancing and the decrease code review lead time. © 2020 IEEE Computer Society. All rights reserved.open access