A Survey of Code Review Benchmarks and Evaluation Practices in Pre-LLM and LLM Era

• URL: http://arxiv.org/abs/2602.13377v1
• Date: Fri, 13 Feb 2026 18:19:38 GMT
• Title: A Survey of Code Review Benchmarks and Evaluation Practices in Pre-LLM and LLM Era
• Authors: Taufiqul Islam Khan, Shaowei Wang, Haoxiang Zhang, Tse-Hsun Chen,
• Abstract summary: Code review is a critical practice in modern software engineering, helping developers detect defects early, improve code quality, and facilitate knowledge sharing.<n>With the rapid advancement of large language models (LLMs), a growing body of work has explored automated support for code review.<n>Current code review datasets are scattered, vary widely in design, and provide limited insight into what review capabilities are actually being assessed.
• Score: 10.935053388447372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code review is a critical practice in modern software engineering, helping developers detect defects early, improve code quality, and facilitate knowledge sharing. With the rapid advancement of large language models (LLMs), a growing body of work has explored automated support for code review. However, progress in this area is hindered by the lack of a systematic understanding of existing benchmarks and evaluation practices. Current code review datasets are scattered, vary widely in design, and provide limited insight into what review capabilities are actually being assessed. In this paper, we present a comprehensive survey of code review benchmarks spanning both the Pre-LLM and LLM eras (2015--2025). We analyze 99 research papers (58 Pre-LLM era and 41 LLM era) and extract key metadata, including datasets, evaluation metrics, data sources, and target tasks. Based on this analysis, we propose a multi-level taxonomy that organizes code review research into five domains and 18 fine-grained tasks. Our study reveals a clear shift toward end-to-end generative peer review, increasing multilingual coverage, and a decline in standalone change understanding tasks. We further identify limitations of current benchmarks and outline future directions, including broader task coverage, dynamic runtime evaluation, and taxonomy-guided fine-grained assessment. This survey provides a structured foundation for developing more realistic and comprehensive benchmarks for LLM-based code review.

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