Factoring Expertise, Workload, and Turnover into Code Review Recommendation

TL;DR

This paper proposes a novel code review recommendation approach that balances expertise, workload, and knowledge sharing to mitigate developer turnover risks and improve review efficiency.

Contribution

It introduces the SofiaWL recommender that dynamically balances review workload and knowledge distribution, improving expertise, reducing workload concentration, and lowering files at risk.

Findings

Expertise during reviews increased by 3%.

Workload concentration decreased by 12%.

Files at risk reduced by 28%.

Abstract

Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover while more evenly distributing review workload. We conduct historical analyses to understand the natural concentration of review workload and the degree of knowledge spreading that is inherent in code review. Even though review workload is highly concentrated, we show that code review natural spreads knowledge thereby reducing the files at risk to turnover. Using simulation, we evaluate existing code review recommenders and develop novel recommenders to understand their impact on the level of expertise during review, the workload of reviewers, and the files at risk to turnover. Our simulations use seeded random replacement of reviewers to allow us to compare the reviewer recommenders without the confounding variation of different reviewers being replaced for each recommender. Combining recommenders, we develop the SofiaWL recommender that suggests experts with low active review workload when none of the files under review are known by only one developer. In contrast, when knowledge is concentrated on one developer, it sends the review to other reviewers to spread knowledge. For the projects we study, we are able to globally increase expertise during reviews, +3%, reduce workload concentration, -12%, and reduce the files at risk, -28%. We make our scripts and data available in our replication package. Developers can optimize for a particular outcome measure based on the needs of their project, or use our GitHub bot to automatically balance the outcomes.