TY - CHAP A1 - Huang, Wenfei A1 - Melzer, Matthias A1 - Dünnweber, Jan T1 - Optimizing Smart Retail by Experiment Using an Online AI Model Exploration Interface T2 - 2024 Winter Simulation Conference (WSC), 15-18 December 2024, Orlando, FL, USA N2 - Smart retail technologies save grocery store operators a lot of work. At the same time, these technologies produce valuable data for building sustainable and economical inventory management strategies. AI models can be trained for sales forecasting using the data. The forecasts support the provisioning of fresh food over the whole week and help reducing food waste. In this paper, we present a Web portal which we developed to allow grocery store operators experiments with AI models revealing interrelations between observed and anticipated customer behavior. Clickable diagrams facilitate the exploration of data sets combining historical data and synthetically generated data. Pricing and ordering can be adapted accordingly to the simulated forecasts. By means of a case study, we show that our simulations are not only useful for predicting future sales but for other smart retail tasks as well. Y1 - 2024 U6 - https://doi.org/10.1109/WSC63780.2024.10838897 SP - 2631 EP - 2642 PB - IEEE ER - TY - CHAP A1 - Hoess, Nicole A1 - Paradis, Carlos A1 - Kazman, Rick A1 - Mauerer, Wolfgang T1 - Does the Tool Matter? Exploring Some Causes of Threats to Validity in Mining Software Repositories T2 - 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Montreal, QC, Canada, March, 4-7, 2025 N2 - Software repositories are an essential source of information for software engineering research on topics such as project evolution and developer collaboration. Appropriate mining tools and analysis pipelines are therefore an indispensable precondition for many research activities. Ideally, valid results should not depend on technical details of data collection and processing. It is, however, widely acknowledged that mining pipelines are complex, with a multitude of implementation decisions made by tool authors based on their interests and assumptions. This raises the questions if (and to what extent) tools agree on their results and are interchangeable. In this study, we use two tools to extract and analyse ten large software projects, quantitatively and qualitatively comparing results and derived data to better understand this concern. We analyse discrepancies from a technical point of view, and adjust code and parametrisation to minimise replication differences. Our results indicate that despite similar trends, even simple metrics such as the numbers of commits and developers may differ by up to 500%. We find that such substantial differences are often caused by minor technical details. We show how tool-level and data post-processing changes can overcome these issues, but find they may require considerable efforts. We summarise identified causes in our lessons learned to help researchers and practitioners avoid common pitfalls, and reflect on implementation decisions and their influence in ensuring obtained data meets explicit and implicit expectations. Our findings lead us to hypothesise that similar uncertainties exist in other analysis tools, which may limit the validity of conclusions drawn in tool-centric research. KW - Codes KW - Collaboration KW - Data collection KW - Data mining KW - Developer Networks KW - Empirical Software Engineering KW - Market research KW - Measurement KW - Mining Software Repositories KW - Pipelines KW - Research Software KW - Software engineering KW - Uncertainty Y1 - 2025 SN - 979-8-3315-3510-0 U6 - https://doi.org/10.1109/SANER64311.2025.00067 SP - 645 EP - 656 PB - IEEE ER - TY - CHAP A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Engl, Fabian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - Eye Movement Modelling Examples on Usability Heuristics T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - The user interface (UI) and user experience (UX) design is of crucial importance for human-computer interaction (HCI), particularly in the context of web applications. In light of the high expectations of users and the competitive nature of the market, it is imperative to employ usability measurement techniques to avoid losing users. Heuristic evaluation (HE) is a cost- and resource-efficient method for evaluating the usability of websites in which evaluators are guided by heuristics. However, the level of expertise of the evaluators has a significant impact on the results, with experts identifying up to 50% more usability issues than novices. To address this gap, this paper proposes Eye Movement Modeling Examples (EMMEs) to demonstrate Jakob Nielsen’s ten usability heuristics in an easy-to-understand format for all levels of experience while also incorporating expert knowledge. In particular, the eye movements and verbal feedback of a usability expert are recorded as the expert analyses the usability of a simple website application in terms of Jakob Nielsen’s ten usability heuristics. This reveals the strategies and cognitive processes of the expert when assessing the usability of a website and makes them more tangible for non-experts or novices. The findings of a questionnaire-based assessment indicate that EMMEs are perceived as beneficial and supportive during the learning process. Ultimately, this comprehensive analysis not only enables a deeper understanding of heuristics for usability novices, but could also lead to EMMEs being applied more efficiently in diverse domains. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723035 SP - 106 EP - 114 PB - ACM ER - TY - RPRT A1 - Marschall, Felix T1 - HEKATE - Implementierung eines Dashboards zur Visualisierung von Auswertungen des HASKI Konzepts für einen umfassenden Einblick in den Lernprozess BT - Projektarbeit N2 - In der gegenwärtigen Bildungslandschaft erfährt die adaptive Hochschullehre eine zunehmende Relevanz, da traditionelle Lehrmethoden vielfach nicht mehr hinreichend sind, um den individuellen Bedürfnissen der Studierenden zu entsprechen. Das Projekt HEKATE zielt darauf ab, die Erkenntnisse aus dem HASKI-System, einem innovativen Ansatz für personalisierte Lernunterstützung basierend auf KI und maschinellem Lernen, effektiv zu visualisieren. HEKATE ergänzt das HASKI-System durch die Entwicklung eines Dashboards, welches eine transparente und nachvollziehbare Darstellung der Lernstildaten ermöglicht. Das Ziel des Projekts besteht in der Umsetzung einer Visualisierungslösung, die sich durch eine hohe Benutzerfreundlichkeit sowie Leistungsfähigkeit auszeichnet und alle relevanten Lern- und Nutzungsdaten Studierender adäquat abbildet. Die funktionalen Anforderungen umfassten die präzise Darstellung von Lernstil-Ergebnissen, Quiz- und Übungsergebnissen sowie die Integration des ARIADNE-Konzepts. Des Weiteren werden nichtfunktionale Ziele definiert, welche eine hohe Benutzerfreundlichkeit, Systemsicherheit, kurze Ladezeiten sowie eine verlässliche Datenverarbeitung umfassten. Hekate bietet sowohl für Studierende als auch für Lehrende einen erheblichen Mehrwert, indem es Lern- und Nutzungsmuster auf einfache und intuitive Weise visualisiert. Die Integration in die HASKI-Umgebung und Moodle gewährleistet eine nahtlose Einbindung der Anwendung in bestehende Lernumgebungen, wodurch sich der Nutzen weiter steigert. In künftigen Entwicklungsstufen könnten zusätzliche Visualisierungsoptionen und Funktionen integriert werden, um das System weiter zu optimieren und an die Bedürfnisse der Nutzer anzupassen. N2 - In the current educational landscape, adaptive university teaching is becoming increasingly relevant, as traditional teaching methods are often no longer sufficient to meet the individual needs of students. The HEKATE project aims to effectively visualise the findings from the HASKI system, an innovative approach to personalised learning support based on AI and machine learning. HEKATE complements the HASKI system by developing a dashboard that enables a transparent and comprehensible visualisation of learning style data. The aim of the project is to implement a visualisation solution that is characterised by a high level of user-friendliness and performance and adequately depicts all relevant learning and usage data of students. The functional requirements included the precise visualisation of learning style results, quiz and exercise results as well as the integration of the ARIADNE concept. Non-functional goals were also defined, which included a high level of user-friendliness, system security, short loading times and reliable data processing. Hekate offers significant added value for both students and teachers by visualising learning and usage patterns in a simple and intuitive way. The integration into the HASKI environment and Moodle ensures a seamless integration of the application into existing learning environments, which further increases the benefits. In future development stages, additional visualisation options and functions could be integrated in order to further optimise the system and adapt it to the needs of users. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-79221 PB - Regensburg ER - TY - CHAP A1 - Hauser, Florian A1 - Staufer, Susanne A1 - Grabinger, Lisa A1 - Röhrl, Simon A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - On the analysis of student learning strategies: using the LIST-K questionnaire ro generate ai-based individualized learning paths T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak. Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention. The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes. The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR. Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94). The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning. Y1 - 2023 U6 - https://doi.org/10.21125/iceri.2023.1147 SP - 4611 EP - 4620 PB - IATED ER - TY - CHAP A1 - Dünnweber, Jan A1 - Sarkar, Amitrajit A1 - Puthiyadath, Vimal Kumar A1 - Barde, Omkar ED - Saeed, Khalid ED - Dvorský, Jiří T1 - A Tale of Four Cities - Improving Bus and Waste Collection Schedules in Practical Smart City Applications T2 - Computer Information Systems and Industrial Management N2 - Computer-based Improvements of waste collection and public transport procedures are often a part of smart city initiatives. When we envision an ideal bus network, it will primarily connect the most crowded bus stops. Similarly, an ideal waste collection vehicle will arrive at every container exactly at the time when it is fully loaded. Beyond doubt, this will reduce traffic and support environmentally friendly intentions like waste separation, as it will make more containers manageable. A difficulty of putting that vision into practice is that vehicles cannot always be where they are needed. Knowing the best time for arriving at a position is not insufficient for finding the optimal route. Therefore, we compare four different approaches to optimized routing: Regensburg, Christchurch, Malaysia, and Bangalore. Our analysis shows that the best schedules result from adapting field-tested routes frequently based on sensor measurements and route optimizing computations. KW - Smart city KW - Bus schedules KW - Waste management Y1 - 2021 SN - 978-3-030-84340-3 U6 - https://doi.org/10.1007/978-3-030-84340-3_30 SP - 369 EP - 380 PB - Springer CY - Cham ER - TY - CHAP A1 - Stadler, Timo A1 - Sarkar, Amitrajit A1 - Dünnweber, Jan ED - Saeed, Khalid ED - Dvorský, Jiří T1 - Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm T2 - CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland N2 - In recent years, mobility solutions have experienced a significant upswing. Consequently, it has increased the importance of forecasting the number of passengers and determining the associated demand for vehicles. We analyze all bus routes in a rural area in contrast to other work that predicts just a single bus route. Some differences in bus routes in rural areas compared to cities are highlighted and substantiated by a case study data using Roding, a town in the rural district of Cham in northern Bavaria, as an example. Data collected and we selected a random forest model that lets us determine the passenger demand, bus line effectiveness, or general user behavior. The prediction accuracy of the selected model is currently 87%. The collected data helps to build new mobility-as-a-service solutions, such as on-call buses or dynamic route optimizations, as we show with our simulation. KW - Transportation KW - Rural mobility KW - Prediction KW - Öffentlicher Personennahverkehr KW - Ländlicher Raum KW - Verkehrsnachfrage KW - Prognosemodell KW - Random Forest Y1 - 2021 SN - 978-3-030-84340-3 U6 - https://doi.org/10.1007/978-3-030-84340-3_36 SP - 442 EP - 453 PB - Springer CY - Cham ER - TY - CHAP A1 - Carbonelli, Cecilia A1 - Felderer, Michael A1 - Jung, Matthias A1 - Lobe, Elisabeth A1 - Lochau, Malte A1 - Luber, Sebastian A1 - Mauerer, Wolfgang A1 - Ramler, Rudolf A1 - Schäfer, Ina A1 - Schroth, Christoph ED - Exman, Iaakov ED - Perez-Castillo, Ricardo ED - Piattini, Mario ED - Felderer, Michael T1 - Challenges for Quantum Software Engineering: An Industrial Application Scenario Perspective T2 - Quantum Software: Aspects of Theory and System Design N2 - Quantum software is becoming a key enabler for applying quantum computing to industrial use cases. This poses challenges to quantum software engineering in providing efficient and effective means to develop such software. Eventually, this must be reliably achieved in time, on budget, and in quality, using sound and well-principled engineering approaches. Given that quantum computers are based on fundamentally different principles than classical machines, this raises the question if, how, and to what extent established techniques for systematically engineering software need to be adapted. In this chapter, we analyze three paradigmatic application scenarios for quantum software engineering from an industrial perspective. The respective use cases center around (1) optimization and quantum cloud services, (2) quantum simulation, and (3) embedded quantum computing. Our aim is to provide a concise overview of the current and future applications of quantum computing in diverse industrial settings. We derive presumed challenges for quantum software engineering and thus provide research directions for this emerging field. KW - industrial use cases KW - quantum computing KW - quantum software engineering KW - software development KW - software engineering Y1 - 2024 SN - 978-3-031-64135-0 SN - 978-3-031-64136-7 U6 - https://doi.org/10.1007/978-3-031-64136-7_12 PB - Springer-Nature ER - TY - INPR A1 - Ammermann, Joshua A1 - Mauerer, Wolfgang A1 - Schäfer, Ina T1 - Towards View-based Development of Quantum Software N2 - Quantum computing is an interdisciplinary field that relies on the expertise of many different stakeholders. The views of various stakeholders on the subject of quantum computing may differ, thereby complicating communication. To address this, we propose a view-based quantum development approach based on a Single Underlying Model (SUM) and a supporting quantumIntegrated Development Environment (IDE). We highlight emerging challenges for future research. KW - Quantum Computing KW - View-based Development KW - Integrated Development Environment Y1 - 2024 ER - TY - CHAP A1 - Biersack, Stefan A1 - Schmailzl, Marc A1 - Linner, Thomas A1 - Eder, Friedrich A1 - Obergrießer, Mathias ED - Schermer, Detleff ED - Brehm, Eric T1 - Von der Handarbeit zur Hochtechnologie im Reallabor: Die Möglichkeit der BIM-basierten Planung und effizienten Produktion von Ziegelwänden durch Roboter T2 - Mauerwerk-Kalender 2025 KW - Mauerwerk KW - Mauerwerksbau Y1 - 2025 SN - 978-3-433-03445-3 U6 - https://doi.org/10.1002/9783433612019.ch7 VL - 2025 PB - Ernst & Sohn ET - 1. Aufl ER -