计算机科学 ›› 2022, Vol. 49 ›› Issue (11): 65-75.doi: 10.11896/jsjkx.220200122
李子东, 姚怡飞, 王微微, 赵瑞莲
LI Zi-dong, YAO Yi-fei, WANG Wei-wei, ZHAO Rui-lian
摘要： 为了给用户提供丰富的交互响应,Web应用的可视化元素越发复杂多样,传统基于DOM的测试已不能满足Web应用的测试新需求。新一代基于机器视觉的测试方法为Web应用复杂元素的测试提供了一种有效途径。目前,此类方法主要依赖于模版匹配技术识别Web页面元素,以生成可视化测试脚本对Web应用进行测试。然而,页面元素外观的细微变化可导致模版匹配技术失效,从而无法识别Web页面元素,更无法生成可视化测试脚本。因此,如何提高基于机器视觉的Web页面元素识别的准确性,使其在复杂条件中仍然适用是一项具有挑战性的工作。基于深度学习的目标检测是当前计算机视觉和机器学习领域的研究热点,可通过大样本学习得到深层的数据特征表示,以准确识别目标,其泛化能力相比模板匹配更强。针对Web应用,从页面元素的视觉特征出发,提出了一种基于深度学习的Web页面元素识别方法,即利用基于深度学习的目标检测算法YOLOv3构建Web页面元素识别模型,自动定位元素的位置和边界,识别Web页面元素类型及功能描述;在此基础上,自动为Web应用生成可视化测试脚本,提升Web应用的测试效率。为了验证基于机器视觉的Web页面元素识别的准确性,针对同一Web应用的不同版本及不同Web应用分别进行实验,结果表明,基于机器视觉的Web页面元素识别模型的平均召回率为75.6%,可有效辅助Web应用可视化测试脚本生成。
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