# Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values

arXiv: https://arxiv.org/abs/2505.09830 · HTML: https://mlsanity.com/paper/2505.09830/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 4 of 4)

- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021)
- [Using Large Language Models to Generate JUnit Tests: An Empirical Study](https://mlsanity.com/paper/2305.00418/) (2023)
- [A Large-Scale Study on the Usage of Testing Patterns that Address Maintainability Attributes (Patterns for Ease of Modification, Diagnoses, and Comprehension)](https://mlsanity.com/paper/1704.08412/) (2017)

## Abstract (arXiv)

The design and implementation of unit tests is a complex task many programmers neglect. This research evaluates the potential of Large Language Models (LLMs) in automatically generating test cases, comparing them with manual tests. An optimized prompt was developed, that integrates code and requirements, covering critical cases such as equivalence partitions and boundary values. The strengths and weaknesses of LLMs versus trained programmers were compared through quantitative metrics and manual qualitative analysis. The results show that the effectiveness of LLMs depends on well-designed prompts, robust implementation, and precise requirements. Although flexible and promising, LLMs still require human supervision. This work highlights the importance of manual qualitative analysis as an essential complement to automation in unit test evaluation.