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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17837 |
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| _version_ | 1866912289238548480 |
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| author | Morimoto, Takuro Haraguchi, Harumi |
| author_facet | Morimoto, Takuro Haraguchi, Harumi |
| contents | Research on using Large Language Models (LLMs) in system development is expanding, especially in automated code and test generation. While E2E testing is vital for ensuring application quality, most test generation research has focused on unit tests, with limited work on E2E test code. This study proposes a method for automatically generating E2E test code from product documentation such as manuals, FAQs, and tutorials using LLMs with tailored prompts. The two step process interprets documentation intent and produces executable test code. Experiments on a web app with six key features (e.g., authentication, profile, discussion) showed that tests generated from product documentation had high compilation success and functional coverage, outperforming those based on requirement specs and user stories. These findings highlight the potential of product documentation to improve E2E test quality and, by extension, software quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17837 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Study on the Improvement of Code Generation Quality Using Large Language Models Leveraging Product Documentation Morimoto, Takuro Haraguchi, Harumi Software Engineering Artificial Intelligence Research on using Large Language Models (LLMs) in system development is expanding, especially in automated code and test generation. While E2E testing is vital for ensuring application quality, most test generation research has focused on unit tests, with limited work on E2E test code. This study proposes a method for automatically generating E2E test code from product documentation such as manuals, FAQs, and tutorials using LLMs with tailored prompts. The two step process interprets documentation intent and produces executable test code. Experiments on a web app with six key features (e.g., authentication, profile, discussion) showed that tests generated from product documentation had high compilation success and functional coverage, outperforming those based on requirement specs and user stories. These findings highlight the potential of product documentation to improve E2E test quality and, by extension, software quality. |
| title | A Study on the Improvement of Code Generation Quality Using Large Language Models Leveraging Product Documentation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2503.17837 |