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Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
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Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with curated synthesis problems and test-cases, are used to measure the performance of various LLMs on code synthesis. However, these test-cases can be limited in both quantity and quality for fully assessing the functional correctness of the generated code. Such limitation in the existing benchmarks begs the following question: In the era of LLMs, is the code generated really correct? To answer this, we propose EvalPlus -- a code synthesis evaluation framework to rigorously benchmark the functional correctness of LLM-synthesized code. EvalPlus augments a given evaluation dataset with large amounts of test-cases newly produced by an automatic test input generator, powered by both LLM- and mutation-based strategies. While EvalPlus is general, we extend the test-cases of the popular HumanEval benchmark by 80x to build HumanEval+. Our extensive evaluation across 26 popular LLMs (e.g., GPT-4 and ChatGPT) demonstrates that HumanEval+ is able to catch significant amounts of previously undetected wrong code synthesized by LLMs, reducing the pass@k by up-to 19.3-28.9%. We also surprisingly found that test insufficiency can lead to mis-ranking. For example, both WizardCoder-CodeLlama and Phind-CodeLlama now outperform ChatGPT on HumanEval+, while none of them could on HumanEval. Our work not only indicates that prior popular code synthesis evaluation results do not accurately reflect the true performance of LLMs for code synthesis, but also opens up a new direction to improve such programming benchmarks through automated testing. We have open-sourced our tools, enhanced datasets as well as all LLM-generated code at https://github.com/evalplus/evalplus to facilitate and accelerate future LLM-for-code research.
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“Similarly, MBPP+ offers 35 times more test cases than the original MBPP.”
From Qwen2.5-Coder Technical Report (2024) · cites this paper as Liu et al., 2023
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“For the second case, a finite test suite necessarily under-approximates an executable's full specification (Liu et al., 2023; Le Goues et al., 2015).”
From ProgramBench: Can Language Models Rebuild Programs From Scratch? (2026) · cites this paper as Liu et al., 2023
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“As a result, LLMs frequently produce code that fails to compile, violates existing patterns, or ignores critical constraints when the relevant context exceeds their window size [22].”
From LongCodeZip: Compress Long Context for Code Language Models (2025) · cites this paper as [22]
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“For example, some subsample instances from existing benchmarks that cannot be solved by current models [93, 82], or augment them to construct more challenging evaluations [21, 44, 49].”
From SciCode: A Research Coding Benchmark Curated by Scientists (2024) · cites this paper as [49]
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“To further ensure accurate evaluation, EvalPlus (Liu et al., 2023) extends HumanEval into HumanEval+ by adding 80 times more unique test cases and correcting inaccurate groundtruth solutions in HumanEval.”
From Qwen2.5-Coder Technical Report (2024) · cites this paper as Liu et al., 2023
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“While passing these tests provides some evidence of functional correctness[25], we deliberately exclude them from our core success metric-since test cases offer only partial coverage and cannot guarantee semantic soundness (see Section 2 for discussion).”
From CLEVER: A Curated Benchmark for Formally Verified Code Generation (2025) · cites this paper as [25]
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“While passing these tests provides some evidence of functional correctness[25], we deliberately exclude them from our core success metric-since test cases offer only partial coverage and cannot guarantee semantic soundness (see Section 2 for discussion).”
From CLEVER: A Curated Benchmark for Formally Verified Code Generation (2025) · cites this paper as [25]
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“When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version.”
From Life After Benchmark Saturation: A Case Study of CORE-Bench (2026) · cites this paper as [35]
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Cited by
Sorted by citations
A Survey on Evaluation of Large Language Models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 4,254
“to ChatGPT using a larger parameter setting, reaching about a 78% match (Liu et al., 2023b).”
MethodSelf-citationLarge language models for software engineering: survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650
“Liu et al. [90] draw attention to the problem, showing how existing test suites can lead to high degrees of false positive conclusions (also a serious problem for online judge platforms [89]).”
BackgroundUsing an LLM to Help With Code Understanding
Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., & Myers, B. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3639187 · Cited by 487
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335
He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246
“Humans bring unparalleled creativity, critical thinking, ethical judgment, and domain-specific knowledge [95].”
Metadata
- Authors
- Liu, J., Xia, C. S., Wang, Y., & Zhang, L.
- ORCID
- Xia, Chunqiu Steven
- Venue
- Advances in Neural Information Processing Systems 36
- Published
- 2023
- Pages
- 21558-21572
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133198585
- Cited by
- 384
- References
- 0
- Citations by year
- 2026: 143 · 2025: 116 · 2024: 102 · 2023: 23
- FWCI
- 98.69
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- neural information processing systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2131943, 2141474)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- large language models, code generation, ChatGPT, program correctness, program verification
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