iSEngLab/LLM4UT_Empirical
[ISSTA 2025] A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing
This repository provides a comprehensive study on how large language models (LLMs) can be optimized for generating effective unit tests, assertions, and for evolving existing tests. It offers the datasets and scripts needed to reproduce experiments, helping you understand how different LLMs perform and which methods (fine-tuning versus prompt engineering) are most effective. Software engineers and QA specialists can use this to guide their adoption of LLMs for improving software quality.
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Use this if you are a software engineer or QA professional looking to leverage large language models to automate or enhance unit testing processes, and you want to understand the best practices and performance trade-offs.
Not ideal if you are looking for a ready-to-use tool to immediately generate unit tests without needing to understand or reproduce the underlying research.
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Feb 09, 2025
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