The aim of this thesis is to research the current possibilities and limitations of automatic generation of synthetic XML documents. The first part of the work discusses the properties of the most used XML data generators and compares them to each other. The next part of the thesis proposes an algorithm for XML data generation which focuses on subset of the main XML data characteristics (number of elements, number of attributes, fan-out, mixed contents etc.). The main target of the algorithm is to generate XML documents using a set of settings which are easy to understand. The last part of the work compares the proposed solution with the existing ones. The comparison focuses on how easy it is to generate XML documents, what structures can be created and finally it compares properties of the similar XML data created using different XML data generators. Powered by TCPDF (www.tcpdf.org)

The aim of this thesis is to research the current possibilities and limitations of automatic generation of synthetic XML documents. The first part of the work discusses the properties of the most used XML data generators and compares them to each other. The next part of the thesis proposes an algorithm for XML data generation which focuses on subset of the main XML data characteristics (number of elements, number of attributes, fan-out, mixed contents etc.). The main target of the algorithm is to generate XML documents using a set of settings which are easy to understand. The last part of the work compares the proposed solution with the existing ones. The comparison focuses on how easy it is to generate XML documents, what structures can be created and finally it compares properties of the similar XML data created using different XML data generators. Powered by TCPDF (www.tcpdf.org)

Usability testing within User Interface (UI) is a central part of assuring high-quality UIdesign that provides good user-experiences across multiple user-groups. The processof usability testing often times requires extensive collection of user feedback, preferablyacross multiple user groups, to ensure an unbiased observation of the potential designflaws within the UI design. Attaining feedback from certain user groups has shown tobe challenging, due to factors such as medical conditions that limits the possibilities ofusers to participate in the usability test. An absence of these hard-to-access groups canlead to designs that fails to consider their unique needs and preferences, which maypotentially result in a worse user experience for these individuals. In this thesis, wetry to address the current gaps within data collection of usability tests by investigatingwhether the Generative Adversarial Network (GAN) framework can be used to generatehigh-quality synthetic user interactions of a particular UI gesture across multiple usergroups. Moreover, a collection UI interaction of 2 user groups, namely the elderlyand young population, was conducted where the UI interaction at focus was thedrag-and-drop operation. The datasets, comprising of both user groups were trainedon separate GANs, both using the doppelGANger architecture, and the generatedsynthetic data were evaluated based on its diversity, how well temporal correlations arepreserved and its performance compared to the real data when used in a classificationtask. The experiment result shows that both GANs produces high-quality syntheticresemblances of the drag-and-drop operation, where the synthetic samples show bothdiversity and uniqueness when compared to the actual dataset. The synthetic datasetacross both user groups also provides similar statistical properties within the originaldataset, such as the per-sample length distribution and the temporal correlationswithin the sequences. Furthermore, the synthetic dataset shows, on average, similarperformance achievements across precision, recall and F1 scores compared to theactual dataset when used to train a classifier to distinguish between the elderly andyounger population drag-and-drop sequences. Further research regarding the use ofmultiple UI gestures, using a single GAN to generate UI interactions across multipleuser groups, and performing a comparative study of different GAN architectures wouldprovide valuable insights of unexplored potentials and possible limitations within thisparticular problem domain.