# On The Impact of Merge Request Deviations on Code Review Practices

arXiv: https://arxiv.org/abs/2506.08860 · HTML: https://mlsanity.com/paper/2506.08860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021)
- [The impact of feature importance methods on the interpretation of defect classifiers](https://mlsanity.com/paper/2202.02389/) (2022)
- [An Exploratory Study on Code Attention in BERT](https://mlsanity.com/paper/2204.10200/) (2022)
- [Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation](https://mlsanity.com/paper/2502.02757/) (2025)
- [Nudge: Accelerating Overdue Pull Requests Towards Completion](https://mlsanity.com/paper/2011.12468/) (2020)
- [Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering](https://mlsanity.com/paper/2202.06146/) (2022)
- [The Impact of Using Regression Models to Build Defect Classifiers](https://mlsanity.com/paper/2202.06157/) (2022)
- [Understanding the Time to First Response In GitHub Pull Requests](https://mlsanity.com/paper/2304.08426/) (2023)
- [Using Machine Intelligence to Prioritise Code Review Requests](https://mlsanity.com/paper/2102.05916/) (2021)
- [An Empirical Study of the Characteristics of Popular Minecraft Mods](https://mlsanity.com/paper/2103.14439/) (2021)
- [Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning](https://mlsanity.com/paper/2301.08427/) (2023)
- [Early Prediction for Merged vs Abandoned Code Changes in Modern Code Reviews](https://mlsanity.com/paper/1912.03437/) (2019)
- [Do Code Review Measures Explain the Incidence of Post-Release Defects?](https://mlsanity.com/paper/2005.09217/) (2020)
- [Automatic Categorization of GitHub Actions with Transformers and Few-shot Learning](https://mlsanity.com/paper/2407.16946/) (2024)

## Abstract (arXiv)

Code review is a key practice in software engineering, ensuring quality and collaboration. However, industrial Merge Request (MR) workflows often deviate from standardized review processes, with many MRs serving non-review purposes (e.g., drafts, rebases, or dependency updates). We term these cases deviations and hypothesize that ignoring them biases analytics and undermines ML models for review analysis. We identify seven deviation categories, occurring in 37.02% of MRs, and propose a few-shot learning detection method (91% accuracy). By excluding deviations, ML models predicting review completion time improve performance in 53.33% of cases (up to 2.25x) and exhibit significant shifts in feature importance (47% overall, 60% top-*k*). Our contributions include: (1) a taxonomy of MR deviations, (2) an AI-driven detection approach, and (3) empirical evidence of their impact on ML-based review analytics. This work aids practitioners in optimizing review efforts and ensuring reliable insights.