# CoCo-Bench: A Comprehensive Code Benchmark For Multi-task Large Language Model Evaluation

Source: [CoCo-Bench: A Comprehensive Code Benchmark For Multi-task Large Language Model Evaluation](<https://www.sota2.com/research/paper/68fa78581e209131cea77db9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:36:17.760Z

Authors: Wenjing Yin, Tianze Sun, Yijiong Yu, Jiawei Fang, Guangyao Su, Jiancheng Wang, Zekun Wang, Wei Wang, Ran Chen, Ziyun Dai, Shuai Yuan, Menghang Dong, Peng Luo, Dong Cao, Da Lei, Yajun Zhang, Hao Chen, Xiang Ma, Yong Liu, Weifeng Liu, Yuanjian Xu, Ji Pei

Published: 2025-04-29T11:57:23.000Z

## Abstract

Large language models (LLMs) play a crucial role in software engineering, excelling in tasks like code generation and maintenance. However, existing benchmarks are often narrow in scope, focusing on a specific task and lack a comprehensive evaluation framework that reflects real-world applications. To address these gaps, we introduce CoCo-Bench (Comprehensive Code Benchmark), designed to evaluate LLMs across four critical dimensions: code understanding, code generation, code modification, and code review. These dimensions capture essential developer needs, ensuring a more systematic and representative evaluation. CoCo-Bench includes multiple programming languages and varying task difficulties, with rigorous manual review to ensure data quality and accuracy. Empirical results show that CoCo-Bench aligns with existing benchmarks while uncovering significant variations in model performance, effectively highlighting strengths and weaknesses. By offering a holistic and objective evaluation, CoCo-Bench provides valuable insights to guide future research and technological advancements in code-oriented LLMs, establishing a reliable benchmark for the field.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.20673>)

[PDF](<https://arxiv.org/pdf/2504.20673.pdf>)