AUTOMATIC TEST GENERATION USING LARGE LANGUAGE MODELS

dc.contributor.author	Damir Rakhmaev
dc.date.accessioned	2026-01-21T20:30:21Z
dc.date.issued	2025-08-08
dc.description.abstract	This article explores approaches to using LLM to generate various test types, including unit tests, integration scenarios, and specification-based tests. A comparative analysis of the advantages and limitations of this approach in the context of test automation is provided. Particular attention is paid to assessing the quality of generated tests, the interpretability of LLM decisions, and identifying promising areas for further research and the practical implementation of these technologies.
dc.format	application/pdf
dc.identifier.uri	https://usajournals.org/index.php/2/article/view/1853
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/111910
dc.language.iso	eng
dc.publisher	Modern American Journals
dc.relation	https://usajournals.org/index.php/2/article/view/1853/1941
dc.rights	https://creativecommons.org/licenses/by/4.0
dc.source	Modern American Journal of Engineering, Technology, and Innovation; Vol. 1 No. 5 (2025); 143-151
dc.source	3067-7939
dc.subject	Large language models, automated testing, code generation, unit tests, software engineering.
dc.title	AUTOMATIC TEST GENERATION USING LARGE LANGUAGE MODELS
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

item.page.files

pagination.showing.labelpagination.showing.detail

item.page.filesection.name: rakhmaev_2025_automatic_test_generation_using_large_la.pdf
item.page.filesection.size: 320.49 KB
item.page.filesection.format: Adobe Portable Document Format