mlsanityData to 14 Oct 2026

CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model

arXiv 2504.15286 ↗First posted 13 Mar 2025cs.SE cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 13 Mar 2025.

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18 of 48 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  6. 2021Program Synthesis with Large Language Modelscited by 3,637
  7. 2023StarCoder: may the source be with you!cited by 799
  8. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 194
  9. 2022Multi-lingual Evaluation of Code Generation Modelscited by 159
  10. 2023ChatUniTest: A Framework for LLM-Based Test Generationcited by 96
  11. 2023Using Large Language Models to Generate JUnit Tests: An Empirical Studycited by 53
  12. 2024HITS: High-coverage LLM-based Unit Test Generation via Method Slicingcited by 38
6 more
  1. 2023ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generationcited by 32
  2. 2020Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformerscited by 30
  3. 2023Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Toolscited by 13
  4. 2021An Empirical Study of Automated Unit Test Generation for Pythoncited by 10
  5. 2024A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suitescited by 8
  6. 2017A Large-Scale Study on the Usage of Testing Patterns that Address Maintainability Attributes (Patterns for Ease of Modification, Diagnoses, and Comprehension)cited by 7

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