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APPLICATION OF LARGE LANGUAGE MODELS FOR SOFTWARE TESTING AND DEBUGGING AUTOMATION

Mozharovskii E. A.  (M. V. Lomonosov Moscow State University)

Aluev A. S.  (Ural Federal University (Ekaterinburg))

Dudak A. A.  (Tomsk State University of Control Systems and Radioelectronics)

Ishankhonov A. Y.  (National University of Science and Technology «MISIS» (Moscow))

The article explores the potential of using large language models (LLMs) for automating software testing and debugging processes. Models such as GPT and BERT, which demonstrate high potential in source code analysis, test scenario generation, and error detection, are discussed. The use of LLMs significantly improves testing accuracy, reduces development time, and automates labor-intensive processes related to identifying software vulnerabilities. Special attention is given to examples of LLM implementation at companies like Google, Microsoft, and Sberbank, where models are used for static code analysis, test generation, and improving software quality. The article also highlights the need for developing new testing methodologies for LLMs to enhance their resilience to errors, bias, and incorrect data. Challenges related to the computational resources required for model operation are also addressed. The authors conclude that the adoption of LLMs can significantly increase the efficiency of software development and testing, improve product quality, and reduce time to market, making these technologies promising for widespread use in software engineering.

Keywords:large language models (LLM), test automation, software debugging, GPT, BERT, testing methodologies
 

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Citation link:
Mozharovskii E. A., Aluev A. S., Dudak A. A., Ishankhonov A. Y. APPLICATION OF LARGE LANGUAGE MODELS FOR SOFTWARE TESTING AND DEBUGGING AUTOMATION // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2024. -№11/2. -С. 103-108 DOI 10.37882/2223-2966.2024.11-2.22
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