一句话：从 Java 项目提取代码知识并蒸馏为结构化提示，引导 LLM 生成路径敏感的高覆盖率单元测试。
单元测试是软件质量保证的关键环节，但人工编写与维护成本高、易出错。已有的自动化测试生成方法，包括基于启发式规则的方法与近期基于大语言模型（LLM）的方法，本质上都是路径不敏感的：它们依赖固定启发式策略或有限的上下文信息，无法对深层控制流结构进行推理，因而在深层执行路径上覆盖率不足。针对这一问题，作者提出 JUnitGenie 框架，从 Java 项目中抽取代码知识（如控制流与控制依赖等），将其蒸馏为结构化提示，用以引导 LLM 生成高覆盖率的单元测试。评估在来自十个真实 Java 项目的 2258 个复杂焦点方法上进行，结果显示 JUnitGenie 相比启发式与 LLM 基线平均提升分支覆盖率 29.60%、行覆盖率 31.00%，并且在闭源与开源基础模型上均能有效泛化。
Unit testing is essential for software quality assurance, yet writing and maintaining tests remains time-consuming and error-prone. To address this challenge, researchers have proposed techniques for automating unit test generation, including heuristic-based methods and more recent approaches that leverage large language models (LLMs). However, these approaches are inherently path-insensitive because they rely on fixed heuristics or limited contextual information and fail to reason about deep control-flow structures, which leads to inadequate coverage, especially for deep execution paths. JUnitGenie extracts code knowledge from Java projects, distills it into structured prompts, and guides LLMs to generate high-coverage tests. We evaluate JUnitGenie on 2,258 complex focal methods from ten real-world Java projects. Results show that JUnitGenie improves branch coverage by an average of 29.60% and line coverage by 31.00% over both heuristic and LLM-based baselines, and generalizes effectively across both closed- and open-source foundation models. JUnitGenie is available at https://github.com/Dianshu-Liao/JUnitGenie-ICSE-Demo , and the demo video is available at https://www.youtube.com/watch?v=-jWJaf661mw.
@inproceedings{paperbot11534,
title = {JUnitGenie: A Framework for Path-Sensitive Unit Test Generation with Large Language Models},
author = {Dianshu Liao and Xin Yin and Shidong Pan and Chao Ni and Zhenchang Xing and xiaoyu sun},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3774748.3787600}
}