DBInputs: Exploiting Persistent Data to Improve Automated GUI Testing

Abstract

The generation of syntactically and semantically valid input data, able to exercise functionalities imposing constraints on the validity of the inputs, is a key challenge in automatic GUI (Graphical User Interface) testing. Existing test case generation techniques often rely on manually curated catalogs of values, although they might require significant effort to be created and maintained, and could hardly scale to applications with several input forms. Alternatively, it is possible to extract values from external data sources, such as the Web or publicly available knowledge bases. However, external sources are unlikely to provide the domain-specific and application-specific data that are often required to thoroughly exercise applications. This paper proposes DBInputs , a novel approach that automatically identifies domain-specific and application-specific inputs to effectively fulfill the validity constraints present in the tested GUI screens. The approach exploits syntactic and semantic similarities between the identifiers of the input fields shown on GUI screens and those of the tables of the target GUI application database, and extracts valid inputs from such database, automatically resolving the mismatch between the user interface and the database schema. DBInputs can properly cope with system testing and maintenance testing efforts, since databases are naturally and inexpensively available in those phases. Our experiments with 4 Web applications and 11 Mobile apps provide evidence that DBInputs can outperform techniques like random input selection and Link , a competing approach for searching inputs from knowledge bases, in both Web and Mobile domains.