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Online services such as social networks, online shops, and search engines deliver different content to users depending on their location, browsing history, or client device. Since these services have a major influence on opinion forming, understanding their behavior from a social science perspective is of greatest importance. In addition, technical aspects of services such as security or privacy are becoming more and more relevant for users, providers, and researchers. Due to the lack of essential data sets, automatic black box testing of online services is currently the only way for researchers to investigate these services in a methodical and reproducible manner. However, automatic black box testing of online services is difficult since many of them try to detect and block automated requests to prevent bots from accessing them.
In this paper, we introduce a testing tool that allows researchers to create and automatically run experiments for exploratory studies of online services. The testing tool performs programmed user interactions in such a manner that it can hardly be distinguished from a human user. To evaluate our tool, we conducted - among other things - a large-scale research study on Risk-based Authentication (RBA), which required human-like behavior from the client. We were able to circumvent the bot detection of the investigated online services with the experiments. As this demonstrates the potential of the presented testing tool, it remains to the responsibility of its users to balance the conflicting interests between researchers and service providers as well as to check whether their research programs remain undetected.
Das permanente Angebot und die Nachfrage an Informationen und Daten jeglicher Art wachsen zunehmend. Das Ergebnis einer meist verschachtelten Suche nach bestimmten Zahlen ist jedoch oftmals eine unübersichtliche, tabellarische Aufstellung derer. Zudem ist dagegen die Aufnahme grafischer Informationen erheblich höher und effektiver. Als Ergebnis der Diplomarbeit soll im ersten, theoretisch-wissenschaftlichen Teil eine technische Konzeption für ein intelligentes Visualisierungs-System erarbeitet werden. Im zweiten, praktischen Teil soll anhand der untersuchten Methoden und des entwickelten Konzepts eine Anwendung kreiert werden, welche ausgewählte Methoden zur interaktiven Visualisierung statistischer Daten nutzt.