# CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks

arXiv: https://arxiv.org/abs/2404.00566 · HTML: https://mlsanity.com/paper/2404.00566/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023)
- [OpenChat: Advancing Open-source Language Models with Mixed-Quality Data](https://mlsanity.com/paper/2309.11235/) (2023)
- [ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation](https://mlsanity.com/paper/2308.01861/) (2023)
- [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://mlsanity.com/paper/1805.08949/) (2018)
- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [Execution-Based Evaluation for Open-Domain Code Generation](https://mlsanity.com/paper/2212.10481/) (2022)
- [Natural Language to Code Generation in Interactive Data Science Notebooks](https://mlsanity.com/paper/2212.09248/) (2022)
- [Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing](https://mlsanity.com/paper/2212.04732/) (2022)
- [A3Test: Assertion-Augmented Automated Test Case Generation](https://mlsanity.com/paper/2302.10352/) (2023)
- [CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models](https://mlsanity.com/paper/2302.00288/) (2023)
- [Aligning Offline Metrics and Human Judgments of Value for Code Generation Models](https://mlsanity.com/paper/2210.16494/) (2022)

## Built on by (most cited first, 23 of 23)

- [BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions](https://mlsanity.com/paper/2406.15877/) (2024)
- [Commit0: Library Generation from Scratch](https://mlsanity.com/paper/2412.01769/) (2024)
- [LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks](https://mlsanity.com/paper/2502.06215/) (2025)
- [ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation](https://mlsanity.com/paper/2503.07010/) (2025)
- [GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents](https://mlsanity.com/paper/2505.23671/) (2025)
- [On the Impacts of Contexts on Repository-Level Code Generation](https://mlsanity.com/paper/2406.11927/) (2024)
- [DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation](https://mlsanity.com/paper/2408.13204/) (2024)
- [Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review](https://mlsanity.com/paper/2406.12655/) (2024)
- [CodeUpdateArena: Benchmarking Knowledge Editing on API Updates](https://mlsanity.com/paper/2407.06249/) (2024)
- [SWE-Universe: Scale Real-World Verifiable Environments to Millions](https://mlsanity.com/paper/2602.02361/) (2026)
- [CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning](https://mlsanity.com/paper/2506.00750/) (2025)
- [Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents](https://mlsanity.com/paper/2505.05283/) (2025)
- [AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists](https://mlsanity.com/paper/2506.08140/) (2025)
- [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025)
- [EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration](https://mlsanity.com/paper/2601.16489/) (2026)
- [Hybrid-Gym: Training Coding Agents to Generalize Across Tasks](https://mlsanity.com/paper/2602.16819/) (2026)
- [Aletheia: What Makes RLVR For Code Verifiers Tick?](https://mlsanity.com/paper/2601.12186/) (2026)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Large Language Models for IT Automation Tasks: Are We There Yet?](https://mlsanity.com/paper/2505.20505/) (2025)
- [Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap](https://mlsanity.com/paper/2508.18646/) (2025)
- [AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation](https://mlsanity.com/paper/2601.04540/) (2026)
- [CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation](https://mlsanity.com/paper/2507.19904/) (2025)
- [Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go](https://mlsanity.com/paper/2511.10868/) (2025)

## Abstract (arXiv)

To adequately test modern code generation systems, evaluation benchmarks must execute and test the code generated by the system. However, these execution and testing requirements have largely limited benchmarks to settings where code is easily executable or has human-written tests. To facilitate evaluation of code generation systems across diverse scenarios, we present CodeBenchGen, a framework to create scalable execution-based benchmarks from naturally occurring code sources. Specifically, we leverage a large language model (LLM) to sandbox arbitrary pieces of code into evaluation examples, including test cases for execution-based evaluation. We illustrate the usefulness of our framework by creating a dataset, Exec-CSN, which includes 1,931 examples involving 293 libraries converted from code in 367 GitHub repositories taken from the Code- SearchNet dataset. To demonstrate the solvability of examples in Exec-CSN, we present a human study demonstrating that 81.3% of the examples can be solved by humans and 61% are rated as "requires effort to solve". We conduct code generation experiments on open-source and proprietary models and analyze the performance of both humans and models. We provide code and data at: https://github.com/yiqingxyq/CodeBenchGen.