Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913838490714112 |
|---|---|
| author | Rodríguez, Martín Rossi, Gustavo Fernandez, Alejandro |
| author_facet | Rodríguez, Martín Rossi, Gustavo Fernandez, Alejandro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values Rodríguez, Martín Rossi, Gustavo Fernandez, Alejandro Software Engineering Artificial Intelligence 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. |
| title | Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2505.09830 |