Feature-Driven End-to-End Test Generation

Parsa Alian, Noor Nashid, Mobina Shahbandeh, Taha Shabani, Ali Mesbah

DOI 10.1109/icse55347.2025.00141 dblp

Abstract

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. Autoe2eintelligently 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 Autoe2eachieves an average feature coverage of 79%, outperforming the best baseline by 558 %, highlighting its effectiveness in generating high-quality, comprehensive test cases.

BibTeX

@inproceedings{Alian-al:ICSE25,
  author    = {Parsa Alian and
               Noor Nashid and
               Mobina Shahbandeh and
               Taha Shabani and
               Ali Mesbah},
  title     = {{Feature-Driven} {End-to-End} Test Generation},
  booktitle = {ICSE},
  pages     = {450--462},
  publisher = {{IEEE}},
  year      = {2025},
}

Feature-Driven End-to-End Test Generation

Abstract

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