Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values
This work addresses the challenge of unit test generation for programmers, but it is incremental as it builds on existing LLM capabilities with optimized prompts.
This research tackled the problem of automating unit test generation by evaluating Large Language Models (LLMs) against manual tests, finding that LLM effectiveness depends on well-designed prompts, robust implementation, and precise requirements, with results showing they are flexible but require human supervision.
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.