Papers › Feature-Driven End-To-End Test Generation
Feature-Driven End-To-End Test Generation
Parsa Alian, Noor Nashid, Mobina Shahbandeh, Taha Shabani, Ali Mesbah
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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.
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