Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models

• URL: http://arxiv.org/abs/2509.23812v2
• Date: Sat, 11 Oct 2025 07:22:01 GMT
• Title: Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
• Authors: Dianshu Liao, Xin Yin, Shidong Pan, Chao Ni, Zhenchang Xing, Xiaoyu Sun,
• Abstract summary: Unit testing is essential for software quality assurance, yet writing and maintaining tests remains time-consuming and errorprone.<n>We present a path-sensitive framework, JUnitGenie, to fill this gap by combining code knowledge with the semantic capabilities of LLMs.<n>We evaluate JUnitGenie on 2,258 complex focal methods from ten real-world Java projects.
• Score: 13.89267206718739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 various techniques for automating unit test generation, including traditional heuristic-based methods and more recent approaches that leverage large language models (LLMs). However, these existing approaches are inherently path-insensitive because they rely on fixed heuristics or limited contextual information and fail to reason about deep control-flow structures. As a result, they often struggle to achieve adequate coverage, particularly for deep or complex execution paths. In this work, we present a path-sensitive framework, JUnitGenie, to fill this gap by combining code knowledge with the semantic capabilities of LLMs in guiding context-aware unit test generation. After extracting code knowledge from Java projects, JUnitGenie distills this knowledge into structured prompts to guide the generation of high-coverage unit tests. We evaluate JUnitGenie on 2,258 complex focal methods from ten real-world Java projects. The results show that JUnitGenie generates valid tests and improves branch and line coverage by 29.60% and 31.00% on average over both heuristic and LLM-based baselines. We further demonstrate that the generated test cases can uncover real-world bugs, which were later confirmed and fixed by developers.

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