A Cascaded Pipeline for Self-Directed, Model-Agnostic Unit Test Generation via LLMs

While existing ML-based unit test generation methods show promising results, they face three key limitations: (1) incomplete test case generation with excessive focus on test oracles, (2) semantic inconsistencies between test components, and (3) dependency on closed-source models compromising data s...

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Vydané v:	Proceedings - International Symposium on Software Reliability Engineering s. 276 - 287
Hlavní autori:	Ni, Chao, Wang, Xiaoya, Yin, Xin, Chen, Liushan, Ma, Guojun
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 21.10.2025
Predmet:	Cascaded Pipeline Data models Data security Error correction Faces Large Language Model Large language models Measurement Model-agnostic Pipelines Semantics Software reliability Test pattern generators Unit Test Generation
ISSN:	2332-6549
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