@article{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {On the perception of graph layouts}, series = {Journal of Software: Evolution and Process}, journal = {Journal of Software: Evolution and Process}, publisher = {Wiley}, doi = {10.1002/smr.2599}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-62973}, abstract = {In the field of software engineering, graph-based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.}, language = {en} } @article{MaierGrabingerUrlhartetal., author = {Maier, Robert and Grabinger, Lisa and Urlhart, David and Mottok, J{\"u}rgen}, title = {Causal Models to Support Scenario-Based Testing of ADAS}, series = {IEEE Transactions on Intelligent Transportation Systems}, journal = {IEEE Transactions on Intelligent Transportation Systems}, publisher = {IEEE}, issn = {1524-9050}, doi = {10.1109/TITS.2023.3317475}, pages = {1 -- 17}, abstract = {In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.}, language = {en} } @article{RoehrlStauferBugertetal., author = {R{\"o}hrl, Simon and Staufer, Susanne and Bugert, Flemming and Nadimpalli, Vamsi Krishna and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Ezer, Timur and Altweck, Ricardo and Graf, Sabine and Kriza, Thomas and Mottok, J{\"u}rgen}, title = {Ethical Considerations of AI in Education: A Case Study based on Pythia Learning Enhancement System}, series = {IEEE Access}, volume = {11}, journal = {IEEE Access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3583975}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-83403}, pages = {1 -- 25}, abstract = {As the use of AI in education increases, addressing ethical concerns is essential for its responsible use and implementation. This study explores the ethical considerations of AI in education by analyzing a concrete application, the Pythia Learning Enhancement System, which enables personalized and adaptive educational experiences. In this case study, the concrete implementation of Pythia, contrary to high-level abstract analyses, provides practical insights into these ethical issues. To achieve this goal, we first define Pythia and outline its implementation. Using the various topics covered by Pythia, the ethical issues associated with each of these topics are outlined, resulting in a list of ten distinct ethical issues. Because there is overlap in issues covered by similar applications, they can benefit from this identification. An ethical (human rights) and didactic (constructivism) theoretical background is then provided as a basis for further discussion. This is used to build the arguments used in the main study. This consists of analyzing each of the issues and providing recommendations to mitigate potential harm arising from these issues. This helps to provide guidelines for future applications. In the analysis, the ethical discussion is mainly based on the human rights of the stakeholders involved, emphasizing human dignity. After the analysis, each of the issues is contextualized with the recent AI Act of the European Union, providing a novel mapping of the issues of AI in education to the AI Act. Finally, the limitations of the study are stated and a plan for future research is presented.}, language = {en} } @article{GrabingerHauserWolffetal., author = {Grabinger, Lisa and Hauser, Florian and Wolff, Christian and Mottok, J{\"u}rgen}, title = {On Eye Tracking in Software Engineering}, series = {SN Computer Science}, volume = {5}, journal = {SN Computer Science}, number = {6}, publisher = {Springer}, address = {Singapore}, issn = {2661-8907}, doi = {10.1007/s42979-024-03045-3}, abstract = {Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.}, language = {en} }