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.
@article{arxiv.2408.01894,
title = {Feature-Driven End-To-End Test Generation},
author = {Parsa Alian and Noor Nashid and Mobina Shahbandeh and Taha Shabani and Ali Mesbah},
journal= {arXiv preprint arXiv:2408.01894},
year = {2025}
}