  Web test automation techniques employ web crawlers to automatically produce a
web app model that is used for test generation. Existing crawlers rely on
app-specific, threshold-based, algorithms to assess state equivalence. Such
algorithms are hard to tune in the general case and cannot accurately identify
and remove near-duplicate web pages from crawl models. Failing to retrieve an
accurate web app model results in automated test generation solutions that
produce redundant test cases and inadequate test suites that do not cover the
web app functionalities adequately. In this paper, we propose WEBEMBED, a novel
abstraction function based on neural network embeddings and threshold-free
classifiers that can be used to produce accurate web app models during
model-based test generation. Our evaluation on nine web apps shows that
WEBEMBED outperforms state-of-the-art techniques by detecting near-duplicates
more accurately, inferring better web app models that exhibit 22% more
precision, and 24% more recall on average. Consequently, the test suites
generated from these models achieve higher code coverage, with improvements
ranging from 2% to 59% on an app-wise basis and averaging at 23%.
