Autonomous testing of web forms

Abstract: A web form requires filling it with correct information in order to access pages behind it. As a result web forms tend to hinder automatic navigation through web sites. In order to fill a web form, we are going to extract relevant information contained in the HTML. Difficulty arises when taking into account the fact that that visual web pages are designed to be read by humans and not by robots. A human user can easily extract the information contained in a web form that is necessary to fill it. Extraction of visual information for automatic filling of web forms is an ongoing topic of research, which has already provided interesting results. However the task of indexing web sites continues to require some human intervention. The following thesis exposes a novel method of extracting visual as well as hidden information and automatically label each field composing a web form. The classification step boils down to finding keywords and then associating them with a label by using the mechanism validation and submission of web forms. These labeled data are then used to train machine learning models that aim at classifying text from given fields of a web form. A comparison between two different methods of classification illustrates the poor results obtained by the machine learning models when compared to the new methods based on keywords.