Abstract: For safety-critical software in domains such as aviation and aerospace,inadequate software quality can lead to highly hazardous accidents.Software testing is the primary means of ensuring software quality,and unit testing－being the foundational phase of software testing－is a necessary step for guaranteeing the quality of safety-critical software.The core component of unit testing is test case design.Traditional approaches to unit test case design fall into two categories:manual writing and automatic generation.Recent advances in artificial intelligence,particularly large language model(LLM),have introduced new intelligent strategies for generating unit test cases.However,general-purpose LLMs typically lack sufficient adaptability for complex tasks like unit test case design.A widely adopted and effective approach to improving model performance is training LLMs on specia-lized datasets.Currently,most unit test datasets used for model training are constructed using traditional techniques such as static analysis.These datasets often suffer from incorrect code-test mappings,lack standardized definitions and evaluation criteria,and are insufficient in both comprehensiveness and logical coherence.To address these issues,a structured,comprehensive definition for unit test datasets and a novel unit test dataset construction method by LLM named UTDCM-LLM are proposed.This method consists of four main components:manual construction of unit test data points,design of chain-of-thought prompts for LLMs,intelligent batch generation of unit test dataset using LLMs,and dataset evaluation based on static analysis.In addition,a model fine-tuning experiment based on the constructed dataset are conducted.The experiment successfully validates both the effectiveness of the dataset in model training and the feasibility of the UTDCM-LLM approach.

Key words: Safety-critical software, Unit testing, Large language model, Dataset, Model training