An Intelligent Framework for Auto-filling Web Forms from Different Web Applications

Shaohua Wang, Ying Zou, I. Keivanloo, Bipin Upadhyaya, J. Ng
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引用次数: 13

Abstract

Nowadays, people use on-line services to conduct various tasks such as on-line shopping and holiday trip planning using web applications. Generally users are required to enter information into web forms to interact with the web applications. However they often have to type in the same information to different web applications repetitively. It could be a tedious job for a user to fill in a large amount of web forms with the same information. To save users from typing redundant information, it is critical to propagate and pre-fill the user's previous inputs across different web applications. However, existing software and approaches cannot meet this urgent need. In this position paper, we propose an intelligent framework to propagate user's inputs across different web applications. Our framework collects user's inputs and analyzes the patterns of user's usage. Furthermore it detects the changes of user's contexts by extracting user's contextual information from various sources such as a user's calender. Our framework clusters the user interface (UI) components to form semantic groups of similar UI components based on our proposed clustering approach. Knowing the similarity relation between UI components, the framework can pre-fill the web forms with user's previous inputs. We conduct a preliminary study on effectiveness of our proposed clustering approach. We achieved a precision of 80% and a recall of 87%.
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用于自动填写不同Web应用程序的Web表单的智能框架
如今,人们使用在线服务来执行各种任务,例如使用web应用程序进行在线购物和假日旅行计划。通常,用户需要在web表单中输入信息以与web应用程序交互。然而,他们经常需要在不同的web应用程序中重复输入相同的信息。对于用户来说,填写大量具有相同信息的web表单可能是一项乏味的工作。为了避免用户输入多余的信息,在不同的web应用程序中传播和预填充用户之前的输入是至关重要的。然而,现有的软件和方法无法满足这一迫切需求。在这篇论文中,我们提出了一个智能框架,在不同的web应用程序中传播用户的输入。我们的框架收集用户的输入并分析用户的使用模式。此外,它通过从各种来源(如用户的日历)提取用户的上下文信息来检测用户上下文的变化。基于我们提出的聚类方法,我们的框架将用户界面(UI)组件聚类,形成类似UI组件的语义组。该框架了解UI组件之间的相似关系,可以用用户之前的输入预填充web表单。我们对我们提出的聚类方法的有效性进行了初步研究。我们达到了80%的准确率和87%的召回率。
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