Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites
Unit tests represent the most basic level of testing within the software testing lifecycle and are crucial to ensuring software correctness. Designing and creating unit tests is a costly and labor-intensive process that …
Code Generationsoftware testingAligning Offline Metrics and Human Judgments of Value for Code Generation Models
Large language models have demonstrated great potential to assist programmers in generating code. For such human-AI pair programming scenarios, we empirically demonstrate that while generated code is most often evaluated…
Code GenerationUsing Large Language Models to Generate JUnit Tests: An Empirical Study
A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both. Although code generation models (e.g., GitHub Copilot) are increasingly being adopted in practice, i…
Code GenerationHumanEvalEvaluating LLM-Based Test Generation Under Software Evolution
Large Language Models (LLMs) are increasingly used for automated unit test generation. However, it remains unclear whether these tests reflect genuine reasoning about program behavior or simply reproduce superficial patt…
Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMs
Large language model (LLM)-based test generation has gained attention in software engineering, yet most studies evaluate LLMs' ability to generate unit tests in a single attempt for a given language, missing the opportun…
DiversityLarge Language ModelOverall - Test