Feature-Driven End-To-End Test Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912178470125568 |
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| author | Alian, Parsa Nashid, Noor Shahbandeh, Mobina Shabani, Taha Mesbah, Ali |
| author_facet | Alian, Parsa Nashid, Noor Shahbandeh, Mobina Shabani, Taha Mesbah, Ali |
| contents | End-to-end (E2E) testing is essential for ensuring web application quality. However, manual test creation is time-consuming, and current test generation techniques produce incoherent tests. In this paper, we present AutoE2E, a novel approach that leverages Large Language Models (LLMs) to automate the generation of semantically meaningful feature-driven E2E test cases for web applications. AutoE2E intelligently infers potential features within a web application and translates them into executable test scenarios. Furthermore, we address a critical gap in the research community by introducing E2EBench, a new benchmark for automatically assessing the feature coverage of E2E test suites. Our evaluation on E2EBench demonstrates that AutoE2E achieves an average feature coverage of 79%, outperforming the best baseline by 558%, highlighting its effectiveness in generating high-quality, comprehensive test cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Feature-Driven End-To-End Test Generation Alian, Parsa Nashid, Noor Shahbandeh, Mobina Shabani, Taha Mesbah, Ali Software Engineering End-to-end (E2E) testing is essential for ensuring web application quality. However, manual test creation is time-consuming, and current test generation techniques produce incoherent tests. In this paper, we present AutoE2E, a novel approach that leverages Large Language Models (LLMs) to automate the generation of semantically meaningful feature-driven E2E test cases for web applications. AutoE2E intelligently infers potential features within a web application and translates them into executable test scenarios. Furthermore, we address a critical gap in the research community by introducing E2EBench, a new benchmark for automatically assessing the feature coverage of E2E test suites. Our evaluation on E2EBench demonstrates that AutoE2E achieves an average feature coverage of 79%, outperforming the best baseline by 558%, highlighting its effectiveness in generating high-quality, comprehensive test cases. |
| title | Feature-Driven End-To-End Test Generation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2408.01894 |