# Feature-Driven End-To-End Test Generation

arXiv: https://arxiv.org/abs/2408.01894 · HTML: https://mlsanity.com/paper/2408.01894/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [NaviQAte: Functionality-Guided Web Application Navigation](https://mlsanity.com/paper/2409.10741/) (2024)
- [AI Agents for Web Testing: A Case Study in the Wild](https://mlsanity.com/paper/2509.05197/) (2025)
- [Are Coding Agents Generating Over-Mocked Tests? An Empirical Study](https://mlsanity.com/paper/2602.00409/) (2026)
- [WebTestPilot: Agentic End-to-End Web Testing against Natural Language Specification by Inferring Oracles with Symbolized GUI Elements](https://mlsanity.com/paper/2602.11724/) (2026)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [A Study on the Improvement of Code Generation Quality Using Large Language Models Leveraging Product Documentation](https://mlsanity.com/paper/2503.17837/) (2025)

## Abstract (arXiv)

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.