Model-MRFaG: A Test Code Generation Framework Based on Fine-tuned LLMs

Software testing is a critical component in software development that is closely related to software quality. Traditional test generation methods face challenges such as producing test cases that are difficult to read and maintain synchronously. Meanwhile, with the advancement of large language models (LLMs) in code generation, the quality of LLM-generated code is increasingly comparable to human-written code. Therefore, this paper proposes a test code generation framework using Model-driven Multiple Results Filtering and Multi-Round Generation strategy (Model-MRFaG). To better adapt to test generation tasks for mainstream programming languages, we built an Alpaca-format Test-Code DataSet for Finetuning Baseline Models (TCDSF) containing six programming languages: Python, Java, JavaScript, C++, C#, and Go, and used this dataset to fine-tune a Baseline Model to obtain the TestCoder model. Subsequently, we developed the Model-MRFaG framework based on the TestCoder model to further improve the accuracy of test code generation. Through comparative experiments evaluating both general test sets and test code generation capabilities, TestCoder outperforms the Baseline Model in both general test sets and test code generation accuracy. Furthermore, the Model-MRFaG framework can further improve the accuracy of test code generation, providing a new solution approach for the intelligent development of software testing.