arXiv Open Access 2025

Ever-Improving Test Suite by Leveraging Large Language Models

Ketai Qiu

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Abstrak

Augmenting test suites with test cases that reflect the actual usage of the software system is extremely important to sustain the quality of long lasting software systems. In this paper, we propose E-Test, an approach that incrementally augments a test suite with test cases that exercise behaviors that emerge in production and that are not been tested yet. E-Test leverages Large Language Models to identify already-tested, not-yet-tested, and error-prone unit execution scenarios, and augment the test suite accordingly. Our experimental evaluation shows that E-Test outperforms the main state-of-the-art approaches to identify inadequately tested behaviors and optimize test suites.

Topik & Kata Kunci

cs.SE

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Ketai Qiu

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APA MLA BibTeX

Qiu, K. (2025). Ever-Improving Test Suite by Leveraging Large Language Models. https://arxiv.org/abs/2506.11000

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓