CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913801293529088 |
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| author | Gorla, Daniele Kumar, Shivam Lorenzini, Pietro Nicolaus Roselli Alipourfaz, Alireza |
| author_facet | Gorla, Daniele Kumar, Shivam Lorenzini, Pietro Nicolaus Roselli Alipourfaz, Alireza |
| contents | This paper presents an approach to automating JUnit test generation for Java applications using the Spring Boot framework, leveraging the LLaMA (Large Language Model Architecture) model to enhance the efficiency and accuracy of the testing process. The resulting tool, called CUBETESTERAI, includes a user-friendly web interface and the integration of a CI/CD pipeline using GitLab and Docker. These components streamline the automated test generation process, allowing developers to generate JUnit tests directly from their code snippets with minimal manual intervention. The final implementation executes the LLaMA models through RunPod, an online GPU service, which also enhances the privacy of our tool. Using the advanced natural language processing capabilities of the LLaMA model, CUBETESTERAI is able to generate test cases that provide high code coverage and accurate validation of software functionalities in Java-based Spring Boot applications. Furthermore, it efficiently manages resource-intensive operations and refines the generated tests to address common issues like missing imports and handling of private methods. By comparing CUBETESTERAI with some state-of-the-art tools, we show that our proposal consistently demonstrates competitive and, in many cases, better performance in terms of code coverage in different real-life Java programs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15286 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model Gorla, Daniele Kumar, Shivam Lorenzini, Pietro Nicolaus Roselli Alipourfaz, Alireza Software Engineering Artificial Intelligence This paper presents an approach to automating JUnit test generation for Java applications using the Spring Boot framework, leveraging the LLaMA (Large Language Model Architecture) model to enhance the efficiency and accuracy of the testing process. The resulting tool, called CUBETESTERAI, includes a user-friendly web interface and the integration of a CI/CD pipeline using GitLab and Docker. These components streamline the automated test generation process, allowing developers to generate JUnit tests directly from their code snippets with minimal manual intervention. The final implementation executes the LLaMA models through RunPod, an online GPU service, which also enhances the privacy of our tool. Using the advanced natural language processing capabilities of the LLaMA model, CUBETESTERAI is able to generate test cases that provide high code coverage and accurate validation of software functionalities in Java-based Spring Boot applications. Furthermore, it efficiently manages resource-intensive operations and refines the generated tests to address common issues like missing imports and handling of private methods. By comparing CUBETESTERAI with some state-of-the-art tools, we show that our proposal consistently demonstrates competitive and, in many cases, better performance in terms of code coverage in different real-life Java programs. |
| title | CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15286 |