Automated unit test generation has been extensively explored in research, with recent advances highlighting the considerable promise of Large Language Models (LLMs). Models such as GPT-4, trained on extensive corpora of text and code, have demonstrated strong capabilities across various code-related tasks—including unit test generation. Nevertheless, current LLM-based methods tend to operate with a narrow focus, often limited to the immediate code context (e.g., variable references) while overlooking richer, task-specific knowledge sources. For instance, they frequently fail to leverage existing test cases of related methods, which could offer highly relevant guidance. Furthermore, many of these tools emphasize achieving high code coverage, frequently compromising the practical usability, functional correctness, and long-term maintainability of the generated tests.

To address these issues, we introduce a novel mechanism called ​​Reference-Based Retrieval Augmentation​​, which enhances traditional Retrieval-Augmented Generation (RAG) by incorporating task-aware context retrieval. In the context of unit test generation, we define “test reference relationships” as the potential for test reusability or referential value between a focal method and other methods within the codebase. These relationships allow the system to retrieve pertinent methods and their accompanying unit tests, providing rich contextual clues for generating high-quality tests. Our approach further decomposes test construction into three structured phases—​​Given​​, ​​When​​, and ​​Then​​—aligning with the typical test design pattern. For each phase, RefTest retrieves and utilizes examples from existing tests of reference methods, offering targeted support for test setup, method invocation, and assertion writing.

We implemented this approach in a tool named ​​RefTest​​, which systematically conducts preprocessing, test reference retrieval, and unit test generation. An incremental generation strategy is adopted, wherein each newly created test informs and improves subsequent ones. RefTest was evaluated on 12 open-source projects containing 1,515 methods. Results show that it significantly surpasses existing tools across multiple dimensions: correctness, completeness, and maintainability of the generated tests.