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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/2510.01024 |
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| _version_ | 1866916983069474816 |
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| author | Júnior, Elvis Valejo, Alan Valverde-Rebaza, Jorge Neves, Vânia de Oliveira |
| author_facet | Júnior, Elvis Valejo, Alan Valverde-Rebaza, Jorge Neves, Vânia de Oliveira |
| contents | Software testing is essential to ensure system quality, but it remains time-consuming and error-prone when performed manually. Although recent advances in Large Language Models (LLMs) have enabled automated test generation, most existing solutions focus on unit testing and do not address the challenges of end-to-end (E2E) testing, which validates complete application workflows from user input to final system response. This paper introduces GenIA-E2ETest, which leverages generative AI to generate executable E2E test scripts from natural language descriptions automatically. We evaluated the approach on two web applications, assessing completeness, correctness, adaptation effort, and robustness. Results were encouraging: the scripts achieved an average of 77% for both element metrics, 82% for precision of execution, 85% for execution recall, required minimal manual adjustments (average manual modification rate of 10%), and showed consistent performance in typical web scenarios. Although some sensitivity to context-dependent navigation and dynamic content was observed, the findings suggest that GenIA-E2ETest is a practical and effective solution to accelerate E2E test automation from natural language, reducing manual effort and broadening access to automated testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenIA-E2ETest: A Generative AI-Based Approach for End-to-End Test Automation Júnior, Elvis Valejo, Alan Valverde-Rebaza, Jorge Neves, Vânia de Oliveira Software Engineering Software testing is essential to ensure system quality, but it remains time-consuming and error-prone when performed manually. Although recent advances in Large Language Models (LLMs) have enabled automated test generation, most existing solutions focus on unit testing and do not address the challenges of end-to-end (E2E) testing, which validates complete application workflows from user input to final system response. This paper introduces GenIA-E2ETest, which leverages generative AI to generate executable E2E test scripts from natural language descriptions automatically. We evaluated the approach on two web applications, assessing completeness, correctness, adaptation effort, and robustness. Results were encouraging: the scripts achieved an average of 77% for both element metrics, 82% for precision of execution, 85% for execution recall, required minimal manual adjustments (average manual modification rate of 10%), and showed consistent performance in typical web scenarios. Although some sensitivity to context-dependent navigation and dynamic content was observed, the findings suggest that GenIA-E2ETest is a practical and effective solution to accelerate E2E test automation from natural language, reducing manual effort and broadening access to automated testing. |
| title | GenIA-E2ETest: A Generative AI-Based Approach for End-to-End Test Automation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.01024 |