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Cited by 64Software Engineering ResearchTopic ModelingNatural Language Processing Techniques

Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering

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Wang, Ruiqi, Guo, Jiyu, Gao, Cuiyun, Fan, Guodong, Chong, Chun Yong, Xia, Xin·2025·Proceedings of the ACM on Software Engineering
DOI 10.1145/3728963
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Abstract

Recently, large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation, significantly advancing the automation of SE tasks. However, assessing the quality of these LLM-generated code and text remains challenging. The commonly used Pass@ k metric necessitates extensive unit tests and configured environments, demands a high labor cost, and is not suitable for evaluating LLMgenerated text. Conventional metrics like BLEU, which measure only lexical rather than semantic similarity, have also come under scrutiny. In response, a new trend has emerged to employ LLMs for automated evaluation, known as LLM-as-a-judge. These LLM-as-a-judge methods are claimed to better mimic human assessment than conventional metrics without relying on high-quality reference answers. Nevertheless, their exact human alignment in SE tasks remains unexplored. In this paper, we empirically explore LLM-as-a-judge methods for evaluating SE tasks, focusing on their alignment with human judgments. We select seven LLM-as-a-judge methods that utilize general-purpose LLMs, alongside two LLMs specifically fine-tuned for evaluation. After generating and manually scoring LLM responses on three recent SE datasets of code translation, code generation, and code summarization, we then prompt these methods to evaluate each response. Finally, we compare the scores generated by these methods with human evaluation. The results indicate that output-based methods reach the highest Pearson correlation of 81.32 and 68.51 with human scores in code translation and generation, achieving near-human evaluation, noticeably outperforming ChrF++, one of the best conventional metrics, at 34.23 and 64.92. Such outputbased methods prompt LLMs to output judgments directly, and exhibit more balanced score distributions that resemble human score patterns. Finally, we provide insights and implications, concluding that current state-of-the-art LLM-as-a-judge methods can potentially replace human evaluations in certain SE tasks.

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  • The LLM-as-Judge evaluation strategy is widely adopted and has been demonstrated to align well with human preferences.

    “This LLM-as-Judge evaluation strategy is widely adopted in both NLP and software engineering domains [35], [36], as it has been demonstrated to align well with human preferences and provides more nuanced evaluation compared to traditional metrics [37], [38].”

    From LongCodeZip: Compress Long Context for Code Language Models (2025) · cites this paper as [37]

    The Introduction describes LLM-as-a-judge as an emerging evaluation approach, but says its use in software-engineering tasks is potential rather than establishing that it is widely adopted in both domains. The Introduction and §5.1 report strong alignment with human scores for some methods and tasks, particularly code translation, while emphasizing that performance varies substantially by task. The unqualified claim that the strategy aligns well with human preferences overstates those findings. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Wang, R., Guo, J., Gao, C., Fan, G., Chong, C. Y., & Xia, X.
Affiliations
  • Harbin Institute of Technology (CN)
  • Monash University Malaysia (MY)
  • Zhejiang University (CN)
ORCID
Venue
Proceedings of the ACM on Software Engineering
Publisher
Association for Computing Machinery (ACM)
Published
2025-06-22
Volume
2
Issue
ISSTA
Pages
1955-1977
ISSN
2994-970X (ISSN-L)
DOI
10.1145/3728963
OpenAlex
W4411522868
Cited by
62
References
8
Citations by year
2026: 44 · 2025: 18
FWCI
65.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the ACM on software engineering. (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Natural Science Foundation of China (62472126)
  • Natural Science Foundation of Guangdong Province (2023A1515011959)
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
LLM-as-a-judge, large language models, automated assessment, human evaluation alignment, code generation evaluation, program translation, code summarization, human judgment correlation, BLEU, chrF, evaluation metrics

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