一句话：首次研究 Web 应用模型推断中的近似重复检测，分类并评估 10 种技术，发现准确检测与覆盖率难以兼得。
自动 Web 测试常从 Web 应用推断状态模型以生成测试。理想模型应仅含功能不同且覆盖主要功能的最少状态，但推断模型常受近似重复影响，即功能相同、仅有无关微小差异的页面副本。论文首次研究 Web 应用模型推断中的近似重复检测：从 6,000 多个网站采集 493k 网页状态对，抽样分类为 clone、near-duplicate、distinct；为各检测技术计算类别边界阈值；并在 9 个开源 Web 应用上评估来自信息检索、Web 测试与计算机视觉的 10 种技术。即使采用最佳阈值，也没有算法能在不牺牲覆盖率的情况下准确检测所有功能近似重复，凸显自动推断 Web 应用模型的挑战。
Automated web testing techniques infer models from a given web app, which are used for test generation. From a testing viewpoint, such an inferred model should contain the minimal set of states that are distinct, yet, adequately cover the app's main functionalities. In practice, models inferred automatically are affected by near-duplicates, i.e., replicas of the same functional webpage differing only by small insignificant changes. We present the first study of near-duplicate detection algorithms used in within app model inference. We first characterize functional near-duplicates by classifying a random sample of state-pairs, from 493k pairs of webpages obtained from over 6,000 websites, into three categories, namely clone, near-duplicate, and distinct. We systematically compute thresholds that define the boundaries of these categories for each detection technique. We then use these thresholds to evaluate 10 near-duplicate detection techniques from three different domains, namely, information retrieval, web testing, and computer vision on nine open-source web apps. Our study highlights the challenges posed in automatically inferring a model for any given web app. Our findings show that even with the best thresholds, no algorithm is able to accurately detect all functional near-duplicates within apps, without sacrificing coverage.
DOI 原文 ·
@inproceedings{paperbot5636,
title = {Near-duplicate detection in web app model inference},
author = {Rahulkrishna Yandrapally and Andrea Stocco and Ali Mesbah},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380416}
}