一句话：提出基于与已有测试差异度的 Web 测试生成算法，优先在浏览器中执行多样化候选用例，提高覆盖与故障检测速度。
现有 Web 测试生成器通常基于 Web 应用的导航模型导出测试路径，但输入值要么依赖昂贵的人工选择，要么随机生成，后者常产生被测应用拒绝的不可行输入序列。随机与搜索式方法往往需要大量浏览器内执行才能达到期望的模型覆盖率。本文提出一种新的 Web 测试生成算法：在执行前，根据候选测试用例与已生成测试之间的多样性进行预筛选，仅让探索了应用不同行为的用例进入浏览器执行。作者在工具 DIG 中实现该方法，并在六个真实 Web 应用上评估。结果表明，与基于爬取和基于搜索的 Web 测试生成器相比，DIG 能显著更早达到更高覆盖率和故障检测率。
Existing web test generators derive test paths from a navigational model of the web application, completed with either manually or randomly generated input values. However, manual test data selection is costly, while random generation often results in infeasible input sequences, which are rejected by the application under test. Random and search-based generation can achieve the desired level of model coverage only after a large number of test execution at- tempts, each slowed down by the need to interact with the browser during test execution. In this work, we present a novel web test generation algorithm that pre-selects the most promising candidate test cases based on their diversity from previously generated tests. As such, only the test cases that explore diverse behaviours of the application are considered for in-browser execution. We have implemented our approach in a tool called DIG. Our empirical evaluation on six real-world web applications shows that DIG achieves higher coverage and fault detection rates significantly earlier than crawling-based and search-based web test generators.
DOI 原文 ·
@inproceedings{paperbot5070,
title = {Diversity-based web test generation},
author = {Matteo Biagiola and Andrea Stocco and Filippo Ricca and Paolo Tonella},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338970}
}