关键词: 单元测试, 代码生成, 大语言模型, 权重增量学习, 微调学习

Abstract:

Data-driven automated generation technology of unit test cases has problems of low coverage and poor readability， struggling to meet the increasing demand for testing. Recently， Large Language Model （LLM） has shown great potential in code generation tasks. However， due to the differences in functional and coding styles of code data， LLMs face the challenges of catastrophic forgetting and resource constraints. To address these problems， a transfer learning idea was proposed by fine-tuning coding and functional styles simultaneously， and an efficient fine-tuning training method was developed for LLMs in generating unit test cases. Firstly， the widely used instruction datasets were adopted to align LLM with instructions， and the instruction sets were divided by task types. At the same time， the weight increments with task-specific features were extracted and stored. Secondly， an adaptive style extraction module was designed for dealing with various coding styles with noise-resistant learning and coding style backtracking learning in the module. Finally， joint training of the functional and coding style increments was performed respectively on the target domain， thereby realizing efficient adaptation and fine-tuning on the target domains with limited resources. Experimental results of test case generation on SF110 Corpus of Classes dataset indicate that the proposed method outperforms the methods for comparison. Compared to the mainstream code generation LLMs — Codex， Code Llama and DeepSeek-Coder， the proposed method has the compilation rate increased by 0.8%， 43.5% and 33.8%， respectively； the branch coverage increased by 3.1%， 1.0%， and 17.2% respectively； and the line coverage increased by 4.1%， 6.5%， and 15.5% respectively； verifying the superiority of the proposed method in code generation tasks.

Key words: unit test, code generation, Large Language Model (LLM), weight incremental learning, fine-tuning learning