Are All Code Reviews the Same? Identifying and Assessing the Impact of Merge Request Deviations

Code review is a fundamental practice in software engineering, ensuring code quality, fostering collaboration, and reducing defects. While research has extensively examined various aspects of this process, most studies assume that all code reviews follow a standardized evaluation workflow. However, our industrial partner, which uses Merge Requests (MRs) mechanism for code review, reports that this assumption does not always hold in practice. Many MRs serve alternative purposes beyond rigorous code evaluation. These MRs often bypass the standard review process, requiring minimal oversight. We refer to these cases as deviations, as they disrupt expected workflow patterns. For example, work-in-progress (WIP) MRs may be used as draft implementations without the intention of being reviewed, MRs with huge changes are often created for code rebase, and library updates typically involve dependency version changes that require minimal or no review effort. We hypothesize that overlooking MR deviations can lead to biased analytics and reduced reliability of machine learning (ML) models used to explain the code review process. This study addresses these challenges by first identifying MR deviations. Our findings show that deviations occur in up to 37.02 % of MRs across seven distinct categories. In addition, we develop a detection approach leveraging few-shot learning, achieving up to 91 % accuracy in identifying these deviations. Furthermore, we examine the impact of removing MR deviations on ML models predicting code review completion time. Removing deviations significantly enhances model performance in 53.33 % of cases, with improvements of up to 2.25 times. Additionally, their exclusion significantly impacts model interpretation, strongly altering overall feature importance rankings in 47 % of cases and top-k rankings in 60 %. Our contributions include: (1) a clear definition and categorization of MR deviations, (2) a novel AI-based detection method leveraging few-shot learning, and (3) an empirical analysis of their exclusion impact on ML models explaining code review completion time. Our approach helps practitioners streamline review workflows, allocate reviewer effort more effectively, and ensure more reliable insights from MR analytics.