一句话：提出FormNexus，用语义推断与LLM反馈式约束精化自动生成Web表单测试用例，覆盖率达89%。
自动化Web表单测试生成长期面临挑战，根源在于表单以人为中心的设计及其设备无关的复杂结构。本文提出FormNexus，从单个表单元素及元素间关系出发推导语义信息，综合利用文本内容、DOM树结构与视觉邻近性，并将这些信息转化为新的概念图——表单实体关系图（Form Entity Relation Graph, FERG），以实现机器友好的语义信息抽取。在此基础上，FormNexus借助大语言模型（LLM）采用反馈驱动机制，依据实时表单提交响应生成并精化输入约束，最终通过系统性地使约束失效，方法化地产出覆盖全面的测试用例集。该工作将LLM能力与先进语义推断方法相结合，弥补了自动化Web表单测试的空白。评估表明，FormNexus结合GPT-4在表单提交状态上达到89%的覆盖率，比最佳基线模型高出25个百分点。
Automated test generation for web forms has been a longstanding challenge, exacerbated by the intrinsic human-centric design of forms and their complex, device-agnostic structures. We introduce an innovative approach, called FormNexus, for automated web form test generation, which emphasizes deriving semantic insights from individual form elements and relations among them, utilizing textual content, DOM tree structures, and visual proximity. The insights gathered are transformed into a new conceptual graph, the Form Entity Relation Graph (FERG), which offers machine-friendly semantic information extraction. Leveraging LLMs, FormNexus adopts a feedback-driven mechanism for generating and refining input constraints based on real-time form submission responses. The culmination of this approach is a robust set of test cases, each produced by methodically invalidating constraints, ensuring comprehensive testing scenarios for web forms. This work bridges the existing gap in automated web form testing by intertwining the capabilities of LLMs with advanced semantic inference methods. Our evaluation demonstrates that FormNexus combined with GPT-4 achieves 89% coverage in form submission states. This outcome significantly outstrips the performance of the best baseline model by a margin of 25%.
DOI 原文 ·
@inproceedings{paperbot8521,
title = {Semantic Constraint Inference for Web Form Test Generation},
author = {Parsa Alian and Noor Nashid and Mobina Shahbandeh and Ali Mesbah},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3680332}
}