Code Reviews with Divergent Review Scores: An Empirical Study of the OpenStack and Qt Communities

TL;DR

This study analyzes how divergent review scores affect the code review process in open source communities, revealing that such patches are common, often integrated, and influenced by external factors and review dynamics.

Contribution

It provides empirical insights into the prevalence and impact of divergent review scores, highlighting the need for improved review tooling and strategies.

Findings

Divergent scores occur in 15-37% of multi-review patches.

Patches with divergent scores are more often integrated than abandoned.

Negative scores follow positive ones in 70% of cases.

Abstract

Code review is a broadly adopted software quality practice where developers critique each others' patches. In addition to providing constructive feedback, reviewers may provide a score to indicate whether the patch should be integrated. Since reviewer opinions may differ, patches can receive both positive and negative scores. If reviews with divergent scores are not carefully resolved, they may contribute to a tense reviewing culture and may slow down integration. In this paper, we study patches with divergent review scores in the OPENSTACK and QT communities. Quantitative analysis indicates that patches with divergent review scores: (1) account for 15%-37% of patches that receive multiple review scores; (2) are integrated more often than they are abandoned; and (3) receive negative scores after positive ones in 70% of cases. Furthermore, a qualitative analysis indicates that patches with strongly divergent scores that: (4) are abandoned more often suffer from external issues (e.g., integration planning, content duplication) than patches with weakly divergent scores and patches without divergent scores; and (5) are integrated often address reviewer concerns indirectly (i.e., without changing patches). Our results suggest that review tooling should integrate with release schedules and detect concurrent development of similar patches to optimize review discussions with divergent scores. Moreover, patch authors should note that even the most divisive patches are often integrated through discussion, integration timing, and careful revision.