@inproceedings{wang-etal-2026-multifiletest,
title = "{M}ulti{F}ile{T}est: A Multi-File-Level {LLM} Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms",
author = "Wang, Yibo and
Xia, Congying and
Zhao, Wenting and
Du, Jiangshu and
Miao, Chunyu and
Deng, Zhongfen and
Yu, Philip S. and
Xing, Chen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1403/",
pages = "28151--28172",
ISBN = "979-8-89176-395-1",
abstract = "Unit test generation has become a promising and important use case for Large Language Models (LLMs). However, existing evaluation benchmarks for LLM unit test generation primarily focus on function- or class-level code (single-file) rather than on more practical, challenging multi-file codebases.To address this limitation, we propose \textbf{MultiFileTest}, a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. MultiFileTest features 20 high-quality, moderate-sized projects per language. We evaluate eleven frontier LLMs on MultiFileTest, and the results show that most tested LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark{'}s inherent difficulty.We also conduct a thorough error analysis, which shows that even advanced LLMs, such as Gemini 3.0 Pro, exhibit basic yet critical errors, including executability and cascade errors. Motivated by this observation, we further evaluate these frontier LLMs under manual error-fixing and self-error-fixing scenarios to assess their potential when equipped with error-fixing mechanisms.Our dataset is available at MultiFileTest."
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<abstract>Unit test generation has become a promising and important use case for Large Language Models (LLMs). However, existing evaluation benchmarks for LLM unit test generation primarily focus on function- or class-level code (single-file) rather than on more practical, challenging multi-file codebases.To address this limitation, we propose MultiFileTest, a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. MultiFileTest features 20 high-quality, moderate-sized projects per language. We evaluate eleven frontier LLMs on MultiFileTest, and the results show that most tested LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty.We also conduct a thorough error analysis, which shows that even advanced LLMs, such as Gemini 3.0 Pro, exhibit basic yet critical errors, including executability and cascade errors. Motivated by this observation, we further evaluate these frontier LLMs under manual error-fixing and self-error-fixing scenarios to assess their potential when equipped with error-fixing mechanisms.Our dataset is available at MultiFileTest.</abstract>
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%0 Conference Proceedings
%T MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms
%A Wang, Yibo
%A Xia, Congying
%A Zhao, Wenting
%A Du, Jiangshu
%A Miao, Chunyu
%A Deng, Zhongfen
%A Yu, Philip S.
%A Xing, Chen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-multifiletest
%X Unit test generation has become a promising and important use case for Large Language Models (LLMs). However, existing evaluation benchmarks for LLM unit test generation primarily focus on function- or class-level code (single-file) rather than on more practical, challenging multi-file codebases.To address this limitation, we propose MultiFileTest, a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. MultiFileTest features 20 high-quality, moderate-sized projects per language. We evaluate eleven frontier LLMs on MultiFileTest, and the results show that most tested LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty.We also conduct a thorough error analysis, which shows that even advanced LLMs, such as Gemini 3.0 Pro, exhibit basic yet critical errors, including executability and cascade errors. Motivated by this observation, we further evaluate these frontier LLMs under manual error-fixing and self-error-fixing scenarios to assess their potential when equipped with error-fixing mechanisms.Our dataset is available at MultiFileTest.
%U https://aclanthology.org/2026.findings-acl.1403/
%P 28151-28172
Markdown (Informal)
[MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms](https://aclanthology.org/2026.findings-acl.1403/) (Wang et al., Findings 2026)
ACL
- Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, and Chen Xing. 2026. MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28151–28172, San Diego, California, United States. Association for Computational Linguistics.