Related papers: Neural Embeddings for Web Testing

Neural Embeddings for Web Testing

URL: http://arxiv.org/abs/2306.07400v1
Date: Mon, 12 Jun 2023 19:59:36 GMT
Title: Neural Embeddings for Web Testing
Authors: Andrea Stocco, Alexandra Willi, Luigi Libero Lucio Starace, Matteo Biagiola, Paolo Tonella
Abstract summary: Existing crawlers rely on app-specific, threshold-based, algorithms to assess state equivalence. We propose WEBEMBED, a novel abstraction function based on neural network embeddings and threshold-free classifiers. Our evaluation on nine web apps shows that WEBEMBED outperforms state-of-the-art techniques by detecting near-duplicates more accurately.
Score: 49.66745368789056
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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%.

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