Extracting widget descriptions from GUIs

  • Authors:
  • Giovanni Becce;Leonardo Mariani;Oliviero Riganelli;Mauro Santoro

  • Affiliations:
  • Department of Informatics, Systems and Communications, University of Milano Bicocca, Milano, Italy;Department of Informatics, Systems and Communications, University of Milano Bicocca, Milano, Italy;Department of Informatics, Systems and Communications, University of Milano Bicocca, Milano, Italy;Department of Informatics, Systems and Communications, University of Milano Bicocca, Milano, Italy

  • Venue:
  • FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
  • Year:
  • 2012

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Abstract

Graphical User Interfaces (GUIs) are typically designed to simplify data entering, data processing and visualization of results. However, GUIs can also be exploited for other purposes. For instance, automatic tools can analyze GUIs to retrieve information about the data that can be processed by an application. This information can serve many purposes such as ease application integration, augment test case generation, and support reverse engineering techniques. In the last years, the scientific community provided an increasing attention to the automatic extraction of information from interfaces. For instance, in the domain of Web applications, learning techniques have been used to extract information from Web forms. The knowledge about the data that can be processed by an application is not only relevant for the Web, but it is also extremely useful to support the same techniques when applied to desktop applications. In this paper we present a technique for the automatic extraction of descriptive information about the data that can be handled by widgets in GUI-based desktop applications. The technique is grounded on mature standards and best practices about the design of GUIs, and exploits the presence of textual descriptions in the GUIs to automatically obtain descriptive data for data widgets. The early empirical results with three desktop applications show that the presented algorithm can extract data with high precision and recall, and can be used to improve generation of GUI test cases.