# Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation

arXiv: https://arxiv.org/abs/2509.04810 · HTML: https://mlsanity.com/paper/2509.04810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023)
- [Multilingual training for Software Engineering](https://mlsanity.com/paper/2112.02043/) (2021)
- [Code Review Automation: Strengths and Weaknesses of the State of the Art](https://mlsanity.com/paper/2401.05136/) (2024)
- [AI-Assisted Assessment of Coding Practices in Modern Code Review](https://mlsanity.com/paper/2405.13565/) (2024)
- [Case2Code: Scalable Synthetic Data for Code Generation](https://mlsanity.com/paper/2407.12504/) (2024)
- [The Struggles of LLMs in Cross-lingual Code Clone Detection](https://mlsanity.com/paper/2408.04430/) (2024)

## Built on by (most cited first, 1 of 1)

- [Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies](https://mlsanity.com/paper/2608.05993/) (2026)

## Abstract (arXiv)

Automating the decision of whether a code change requires manual review is vital for maintaining software quality in modern development workflows. However, the emergence of new programming languages and frameworks creates a critical bottleneck: while large volumes of unlabelled code are readily available, there is an insufficient amount of labelled data to train supervised models for review classification. We address this challenge by leveraging Large Language Models (LLMs) to translate code changes from well-resourced languages into equivalent changes in underrepresented or emerging languages, generating synthetic training data where labelled examples are scarce. We assume that although LLMs have learned the syntax and semantics of new languages from available unlabelled code, they have yet to fully grasp which code changes are considered significant or review-worthy within these emerging ecosystems. To overcome this, we use LLMs to generate synthetic change examples and train supervised classifiers on them. We systematically compare the performance of these classifiers against models trained on real labelled data. Our experiments across multiple GitHub repositories and language pairs demonstrate that LLM-generated synthetic data can effectively bootstrap review recommendation systems, narrowing the performance gap even in low-resource settings. This approach provides a scalable pathway to extend automated code review capabilities to rapidly evolving technology stacks, even in the absence of annotated data.