Use este identificador para citar ou linkar para este item: https://rd.uffs.edu.br/handle/prefix/9687
Tipo: Monografia
Título: Evaluating LLM-generated and human-written unit tests for iterative code refinement
Autor(es): Machado, Heitor Fernandes Vicente
Primeiro Orientador: Feitosa, Samuel da Silva
Resumo: Large Language Models have shown promising results in automatic code generation, but challenges still exist related to the quality of the produced implementations and the effective use of testing during the generation process. This work investigates the use of unit tests automatically generated by LLMs in contrast with manually crafted tests as an iterative code refinement mechanism for programming problems. The proposed approach consists of a tool capable of generating unit tests from a task specification, validating imple- mentations produced by the models, and performing successive correction cycles guided by the test results. The methodology was evaluated using the HumanEval benchmark in conjunction with the EvalPlus framework, considering scenarios of direct generation, self-validation with automatically generated tests, and validation using the benchmark’s original tests. The evaluation considered the Pass@1 and Pass@10 metrics to measure problem-solving perfor- mance, as well as the analysis of the generated test suites, classified as valid, invalid, or not produced. The results demonstrate that synthetic tests are capable of improving the performance of all evaluated models, although they still present lower performance compared to that obtained with manually crafted tests. The findings indicate that automatic test generation by LLMs represents a viable strategy to support code generation and refinement processes.
Palavras-chave: Inteligência artificial
Geração de código
Testes
Engenharia de software
Idioma: por
País: Brasil
Instituição: Universidade Federal da Fronteira Sul
Sigla da Instituição: UFFS
Faculdade, Instituto ou Departamento: Campus Chapecó
Tipo de Acesso: Acesso Aberto
URI: https://rd.uffs.edu.br/handle/prefix/9687
Data do documento: 2026
Aparece nas coleções:Ciência da Computação

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