TY - INPR A1 - Krüger, Tom A1 - Mauerer, Wolfgang T1 - Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation N2 - The Quantum Approximate Optimisation Algorithm (qaoa) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While qaoa targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the qaoa optimisation landscape from solution space structures, we derive a new algorithmic variant: Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth qaoa for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in qaoa. By ensuring generality, we link existing empirical observations on qaoa parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation. Y1 - 2024 N1 - er Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/3/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/out+of+the+loop/docId/8843 ER - TY - CHAP A1 - Schönhärl, Stefan A1 - Fuxen, Philipp A1 - Graf, Julian A1 - Schmidt, Jonas A1 - Hackenberg, Rudolf A1 - Mottok, Jürgen T1 - An Automotive Penetration Testing Framework for IT-Security Education T2 - Cloud Computing 2022: The Thirteenth International Conference on Cloud Computing, GRIDs, and Virtualization, Special Track FAST-CSP, Barcelona, Spain, 24.-28.04.2022 N2 - Automotive Original Equipment Manufacturer (OEM) and suppliers started shifting their focus towards the security of their connected electronic programmable products recently since cars used to be mainly mechanical products. However, this has changed due to the rising digitalization of vehicles. Security and functional safety have grown together and need to be addressed as a single issue, referred to as automotive security, in the following article. One way to accomplish security is automotive security education. The scientific contribution of this paper is to establish an Automotive Penetration Testing Education Platform (APTEP). It consists of three layers representing different attack points of a vehicle. The layers are the outer, inner, and core layers. Each of those contains multiple interfaces, such as Wireless Local Area Network (WLAN) or electric vehicle charging interfaces in the outer layer, message bus systems in the inner layer, and debug or diagnostic interfaces in the core layer. One implementation of APTEP is in a hardware case and as a virtual platform, referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theory, such as constructivism and problem-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH)Regensburg experienced the challenges as part of a special topic course and provided positive feedback. KW - IT-Security KW - Education KW - Automotive KW - Penetration testing KW - Education framework Y1 - 2022 UR - https://www.thinkmind.org/index.php?view=article&articleid=cloud_computing_2022_1_10_28001 SN - 978-1-61208-948-5 SP - 1 EP - 6 PB - IARIA CY - [Wilmington, DE, USA] ER - TY - CHAP A1 - Nadimpalli, Vamsi Krishna A1 - Hauser, Florian A1 - Bittner, Dominik A1 - Grabinger, Lisa A1 - Staufer, Susanne A1 - Mottok, Jürgen ED - Mottok, Jürgen T1 - Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems T2 - Proceedings of the 5th European Conference on Software Engineering Education, Seeon/Germany, June 19 - 21, 2023 N2 - Nowadays, learning management systems are widely employed in all educational institutions to instruct students as a result of the increasing in online usage. Today’s learning management systems provide learning paths without personalizing them to the characteristics of the learner. Therefore, research these days is concentrated on employing AI-based strategies to personalize the systems. However, there are many different AI algorithms, making it challenging to determine which ones are most suited for taking into account the many different features of learner data and learning contents. This paper conducts a systematic literature review in order to discuss the AI-based methods that are frequently used to identify learner characteristics, organize the learning contents, recommend learning paths, and highlight their advantages and disadvantages. Y1 - 2023 SN - 978-1-4503-9956-2 U6 - https://doi.org/10.1145/3593663.3593681 SP - 83 EP - 92 PB - Association for Computing Machinery CY - New York ER - TY - RPRT A1 - Schultz, Maximilian T1 - 1.Bericht für das Regensburg Center of Health Sciences and Technology (RCHST) N2 - Der vorliegende Bericht zeigt den aktuellen Stand des Projekts „Künstliche Intelligenz für Nichtregierungsorganisationen (KINiro) – Bedarf, Akzeptanz und Umsetzungsmöglichkeiten“ an der OTH Regensburg am Institut für Sozialforschung und Technikfolgenabschätzung (IST). Im Projektbericht wird das Projekt KINiro mit besonderem Blick auf die Erkenntnisse und Tätigkeiten im Gesundheitswesen aufgezeigt. Das Projekt KINiro wird mit Laufzeit Januar 2023 bis Dezember 2025 durch das Bundesministerium für Familie, Senioren, Frauen und Jugend (BMFSFJ) gefördert. Eine zusätzliche Unterstützung bietet das Regensburg Center of Health Sciences and Technology (RCHST) KW - Künstliche Intelligenz KW - Gesundheit Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-73125 VL - 2024 PB - OTH Regensburg CY - Regensburg ER - TY - CHAP A1 - Schwägerl, Felix A1 - Uhrig, Sabrina A1 - Westfechtel, Bernhard T1 - A Graph-based Algorithm for Three-way Merging of Ordered Collections in EMF Models T2 - Science of Computer Programming N2 - In EMF models, ordered collections appear as the values of multi-valued structural features. Traditional, text-based version control systems do not sufficiently support three-way merging of ordered collections inside EMF models since they cannot guarantee a consistent result. The operation three-way merging is defined as follows: based on a common base version b, two alternative versions and were developed by copying and modifying the base version. To reconcile these changes, a merged version m is to be created as a common successor of and . In this paper, we present a graph algorithm to solve the problem of three-way merging of ordered collections in EMF models. Each version of a collection can be represented by means of a linearly ordered graph. To create the merged version, these graphs are combined to a merged collection graph using set formula. To create the merged collection, a generalized topological sort is performed on the merged collection graph. Conflicts occur in case the order of elements cannot be deduced automatically; these conflicts are resolved either interactively or by default rules. We have implemented the merge algorithm in our tool BTMerge, which performs a consistency-preserving three-way merge of versions of EMF models being instances of arbitrary Ecore models. Our implementation relies on an alternative form of representing multiple versions of a collection, namely a versioned collection graph which forms a superimposition of collection versions. The algorithm presented here is purely state-based. Matching and merging of collections are clearly separated sub-problems. Insertions and deletions performed on the elements of the collection are propagated into the merged version in a consistent way. Our algorithm makes only minimal assumptions with regard to the underlying product model and thus may be applied to ordered collections inside plain text or XML files. By taking arbitrary move operations into account, the algorithm considerably goes beyond the functionality of contemporary merge tools which cannot adequately handle move operations. KW - EMF Models KW - Model Merging KW - Graph Algorithms Y1 - 2014 U6 - https://doi.org/10.1016/j.scico.2015.02.008 VL - 113 IS - Part 1 SP - 51 EP - 81 ER - TY - CHAP A1 - Buchmann, Thomas A1 - Schwägerl, Felix T1 - Breaking the Boundaries of Meta Models and Preventing Information Loss in Model-Driven Software Product Lines T2 - Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering, Vol 1, 2016, Rome, Italy N2 - Model-driven software product line engineering is an integrating discipline for which tool support has become available recently. However, existing tools are still immature and have several weaknesses. Among others, limitations in variability, caused by meta model restrictions, and unintended information loss are not addressed. In this paper, we present two conceptual extensions to model-driven product line engineering based on negative variability, being alternative mappings and surrogates. Alternative mappings allow for unconstrained variability, mitigating meta model restrictions by virtually extending the underlying multi-variant domain model. Surrogates prevent unintended information loss during product derivation based on a contextsensitive product analysis, which can be controlled by a declarative OCL-based language. Both extensions have been implemented in FAMILE, a model-driven product line tool that is based on EMF, provides dedicated consistency repair mechanisms, and completely automates application engineering. The added value of alternative mappings and surrogates is demonstrated by a running example. KW - Software Product Lines KW - Model-Driven Development KW - Unconstrained Variability KW - Information Loss KW - Negative Variability KW - Application Engineering Y1 - 2016 U6 - https://doi.org/10.5220/0005789100730083 SP - 83 PB - SCITEPRESS - Science and and Technology Publications ER - TY - CHAP A1 - Schwägerl, Felix A1 - Uhrig, Sabrina A1 - Westfechtel, Bernhard T1 - Model-based tool support for consistent three-way merging of EMF models T2 - ACME '13, Proceedings of the workshop on ACadeMics Tooling with Eclipse N2 - Inadequate version control has been identified as a major obstacle to the application of model-driven software engineering. In particular, sophisticated support for merging model versions is urgently needed. We present a tool for merging of EMF models which may be applied to instances of arbitrary Ecore models. The tool advances the state of the art by guaranteeing a consistent merge result. Furthermore, it detects and resolves not only context-free, but also context-sensitive conflicts. Our merge tool is model-based; it relies on EMF for its implementation. Initial experiences gained from its application confirm that the merge tool operates both accurately and efficiently. Y1 - 2013 U6 - https://doi.org/10.1145/2491279.2491281 PB - ACM CY - New York, NY, USA ER - TY - GEN A1 - Mauerer, Wolfgang A1 - Joblin, Mitchell A1 - Tamburri, Damian Andrew A1 - Paradis, Carlos A1 - Kazman, Rick A1 - Apel, Sven T1 - In Search of Socio-Technical Congruence: A Large-Scale Longitudinal Study [Data set] Y1 - 2021 U6 - https://doi.org/10.5281/zenodo.4766388 N1 - Archive of the accompanying website including all datasets for "In Search of Socio-Technical Congruence: A Large-Scale Longitudinal Study" (IEEE Transactions on Software Engineering): https://doi.org/10.1109/TSE.2021.3082074 ER - TY - GEN A1 - Mauerer, Wolfgang A1 - Scherzinger, Stefanie T1 - Nullius in Verba - Reproducibility Tutorial [Data set] N2 - Reproduction package (docker image) for the ICDE 2021 tutorial "Nullius in Verba". Y1 - 2021 U6 - https://doi.org/10.5281/zenodo.4730023 N1 - In Verbindung mit dem Konferenzbeitrag: Nullius in Verba: Reproducibility for Database Systems Research, Revisited - https://doi.org/10.1109/ICDE51399.2021.00270 ER - TY - GEN A1 - Schmidbauer, Lukas A1 - Lobe, Elisabeth A1 - Schäfer, Ina A1 - Mauerer, Wolfgang T1 - It's Quick to be Square: Fast Quadratisation for Quantum Toolchains [Data set] N2 - Many of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads to degrade the computation may arise, possibly negating any possible quantum advantage. Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be executed on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy-intermediate scale quantum hardware (NISQ), these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper we consider a specific class of higher-level representations (polynomial unconstrained binary optimisation problems), and devise novel automatic transformation mechanisms into widely used quadratic unconstrained binary optimisation problems that substantially improve efficiency and versatility over the state of the art. We also identify what influence factors of lower-level details can be abstracted away in the transformation process, and which details must be made available to higher-level abstractions. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.14245588 N1 - Paper: 10.48550/arXiv.2411.19934 Software: https://github.com/lfd/Fast_Quadratisation ER - TY - GEN A1 - Bugert, Flemming T1 - Applying Ariadne: Dataset on Learning Styles and Moodle-Based Learning Paths [Data set] N2 - This dataset contains information queried from 22 students inside a Moodle based learning management system during the winter term 2023/24 at a German university. Abstract With the use of learning management systems students benefit from being recommended suitable learning elements based on their individual needs. In doing so, recommendation algorithms are applied which first query the student’s learning style. To improve the recommendation of learning elements a continuous analysis of the individual’s learning style is required. A frequent questionnaire assessment would however be too time consuming. Instead, in a prior study an algorithm has been designed to identify changes in learning styles from the student’s selection of learning elements. In this paper, we investigate the functionality of that algorithm by applying it on real student data. In particular, we test if the algorithm correctly indicates changes in learning styles. The utilised data is collected in our learning management system. To be precise, the data is obtained from 22 students enrolled in a software engineering course during the winter term of 2023/24. The data comprises two types of information for each student: 1) learning style collected at the start and end of the term, and 2) the user’s actual selection of learning elements inside the learning management system. The uniqueness of this study lies in the data and the evaluation strategy based on it. Having the learning style at the end of the semester period as ground truth allows us to test if the algorithm operates correctly with actual user data from our learning management system. The results validate the behaviour of our algorithm, yet they strongly suggest the need for an adaptation. Further research is required on how to parameterise the underlying models. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.12594911 ER - TY - GEN A1 - Schmidbauer, Lukas A1 - Mauerer, Wolfgang T1 - SAT Strikes Back: Parameter and Path Relations in Quantum Toolchains [Data set] N2 - In the foreseeable future, toolchains for quantum computing should offer automatic means of transforming a high level problem formulation down to a hardware executable form. Thereby, it is crucial to find (multiple) transformation paths that are optimised for (hardware specific) metrics. We zoom into this pictured tree of transformations by focussing on k-SAT instances as input and their transformation to QUBO, while considering structure and characteristic metrics of input, intermediate and output representations. Our results can be used to rate valid paths of transformation in advance—also in automated (quantum) toolchains. We support the automation aspect by considering stability and therefore predictability of free parameters and transformation paths. Moreover, our findings can be used in the manifesting era of error correction (since considering structure in a high abstraction layer can benefit error correcting codes in layers below). We also show that current research is closely linked to quadratisation techniques and their mathematical foundation. Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.15464391 N1 - Repositoriy URL: https://github.com/lfd/QSW25-SAT-Strikes-Back ER - TY - GEN A1 - Stocker, Philipp A1 - Lehrer, Tobias T1 - Machine Learning Dataset of U-Channel Sheet Metal Geometry Representations with Supervision Information for Drawability Assessment and Part Segmentation [Data set] N2 - The dataset contains 2533 geometries from four different parametric CAD models sampled from the U-Channel python package. The representations include the original CAD geometries (.step), graph binaries (.bin), meshes (.off), and point clouds (.xyz). Additionally, we provide labels for supervised learning use cases of local and global drawability assessment and part segmentation (.json) for all representations. Custom label computation for drawability assessment is enabled by the provided strains.zip file, which contains minor and major true strains for the given meshes. For more details, refer to the associated publication. Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.15327950 N1 - Zugehörige Publikation: Lehrer, Tobias and Stocker, Philipp and Duddeck, Fabian and Wagner, Marcus, UCSM: Dataset of U-Shaped Parametric CAD Geometries and Real-World Sheet Metal Meshes for Deep Drawing (May 21, 2025). https://doi.org/10.2139/ssrn.5268323 ER - TY - GEN A1 - Staufer, Susanne T1 - Persistence of Learning Style, Learning Strategy, and Personality Traits [Data set] N2 - This dataset contains the result of the survey to learning styles, learning strategies, and personality traits. The survey was executed in winter term 2023/24 and summer term 2023 in a German university (OTH Regensburg) during the course "Software Engineering". Examined questionnaires are ILS (learning styles), LIST-K (learning strategies), and BFI-10 (personality traits). The same three questionnaires were asked at two different survey periods three to four months apart while each survey period lasts one to two weeks. Pretest data were examined at the start of the term, while posttest data at the end. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.12743866 ER - TY - CHAP A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - González Martínez, Chelo ED - Lees, Joanna T1 - Unveiling the secrets of learning styles: decoding eye movements via machine learning T2 - Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023 N2 - Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students – based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms. Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students. In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire. In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates. Y1 - 2023 SN - 978-84-09-55942-8 U6 - https://doi.org/10.21125/iceri.2023.1291 N1 - Zugehöriges Datenset: https://doi.org/10.5281/zenodo.8349468 SP - 5153 EP - 5162 PB - IATED ER - TY - GEN A1 - Bittner, Dominik A1 - Ezer, Timur A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Eye Tracking based Learning Style Identification for Learning Management Systems [Data set] N2 - In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences. Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.8349468 N1 - Related works: Is new version of publication: 10.1145/3593663.3593680 (DOI) Is published in publication: 10.21125/iceri.2023.1291 (DOI) ER - TY - GEN A1 - Moser, Elisabeth A1 - Eberhardt, Matthias A1 - Meyer, Selina A1 - Schmidhuber, Maximilian A1 - Ketterer, Daniel T1 - ARGO ship classification dataset [Data set] N2 - The ARGO ship classification dataset holds 1750 labelled images from PlanetScope-4-Band satelites. The dataset creation process and results on the dataset are published in the demo paper: {CITE} The imagery is provided as numpy binary files. All image data is licensed by Planet Labs PBC. The channel ordering is BGRN. The dataset is provided in two folders named "ship" and "non_ship". Those folders correspond to the original labels created during automated dataset creation. The files are numbered. Two additional .csv files are provided. The shipsAIS_2017_Zone17.csv file holds the AIS information on the imagery contained in the ship folder. The data was retrieved from marinecadastre.gov. During the experiments errors in the automatically created dataset emerged which are further described in the paper. The manual relabelling is supplied in the corrected_labels.csv file. Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6058710 ER - TY - GEN A1 - Grabinger, Lisa A1 - Homann, Alexander A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Study: MISRA C coding guidelines [Data set] N2 - This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines". Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.7898606 ER - TY - GEN A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Study: Notation of Causal Graphs [Data set] N2 - This repository contains the material and obtained data of an eye tracking study on the topic "Notation of Causal Graphs". Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7241158 ER - TY - GEN A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Mottok, Jürgen T1 - Study: Layout of Causal Graphs [Data set] N2 - This repository contains the material and obtained data of an eye tracking study on the topic "Layout of Causal Graphs". Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7241097 ER - TY - JOUR A1 - Grabinger, Lisa A1 - Hauser, Florian A1 - Wolff, Christian A1 - Mottok, Jürgen T1 - On Eye Tracking in Software Engineering JF - SN Computer Science N2 - 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. KW - Eye tracking KW - Software engineering KW - Empirical KW - Systematic literature review Y1 - 2024 U6 - https://doi.org/10.1007/s42979-024-03045-3 SN - 2661-8907 N1 - Zugehöriges Datenset: doi:10.5281/zenodo.11279180 VL - 5 IS - 6 PB - Springer CY - Singapore ER - TY - GEN A1 - Grabinger, Lisa T1 - Data of a systematic literature research on eye tracking in software engineering [Data set] N2 - How did the data collection work? By systematic research in the IEEE Xplore and ACM digital libraries we extracted 125 papers. Further details on the procedure can be found in the journal paper. What data is provided? In this repository you can find the extracted data, i.e., file data.xlsx. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.11279180 N1 - Im Zusammenhang mit folgender Publikation: Grabinger, L., Hauser, F., Wolff, C. et al. On Eye Tracking in Software Engineering. SN COMPUT. SCI. 5, 729 (2024). https://doi.org/10.1007/s42979-024-03045-3 ER -