@inproceedings{57550,
  author       = {{Schott, Stefan and Ponta, Serena Elisa and Fischer, Wolfram and Klauke, Jonas and Bodden, Eric}},
  booktitle    = {{38th European Conference on Object-Oriented Programming (ECOOP 2024)}},
  location     = {{Vienna}},
  title        = {{{Java Bytecode Normalization for Code Similarity Analysis}}},
  doi          = {{10.4230/LIPIcs.ECOOP.2024.37}},
  year         = {{2024}},
}

@inproceedings{58716,
  author       = {{Schott, Stefan and Fischer, Wolfram and Ponta, Serena Elisa and Klauke, Jonas and Bodden, Eric}},
  booktitle    = {{2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)}},
  publisher    = {{IEEE}},
  title        = {{{Compilation of Commit Changes Within Java Source Code Repositories}}},
  doi          = {{10.1109/icsme58944.2024.00038}},
  year         = {{2024}},
}

@inproceedings{56863,
  author       = {{Schiebel, Fabian Benedikt and Sattler, Florian and Schubert, Philipp Dominik and Apel, Sven and Bodden, Eric}},
  booktitle    = {{38th European Conference on Object-Oriented Programming (ECOOP 2024)}},
  editor       = {{Aldrich, Jonathan and Salvaneschi, Guido}},
  isbn         = {{978-3-95977-341-6}},
  issn         = {{1868-8969}},
  pages        = {{36:1–36:28}},
  publisher    = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}},
  title        = {{{Scaling Interprocedural Static Data-Flow Analysis to Large C/C++ Applications: An Experience Report}}},
  doi          = {{10.4230/LIPIcs.ECOOP.2024.36}},
  volume       = {{313}},
  year         = {{2024}},
}

@article{63059,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees for end-to-end inference latency service-level objectives (SLO). Jellyfish handles the network variability by utilizing both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to continuously map users and adapt DNNs at runtime, so that we fulfill latency SLOs while maximizing the overall inference accuracy. We further investigate <jats:italic>dynamic</jats:italic> DNNs, i.e., DNNs that encompass multiple architecture variants, and demonstrate their potential benefit through preliminary experiments. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.
</jats:p>}},
  author       = {{Nigade, Vinod and Bauszat, Pablo and Bal, Henri and Wang, Lin}},
  issn         = {{0922-6443}},
  journal      = {{Real-Time Systems}},
  number       = {{2}},
  pages        = {{239--290}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Inference serving with end-to-end latency SLOs over dynamic edge networks}}},
  doi          = {{10.1007/s11241-024-09418-4}},
  volume       = {{60}},
  year         = {{2024}},
}

@inproceedings{56137,
  abstract     = {{Many Android applications collect data from users. The European Union's General Data Protection Regulation (GDPR) requires vendors to faithfully disclose which data their apps collect. This task is complicated because many apps use third-party code for which the same information is not readily available. Hence we ask: how accurately do current Android apps fulfill these requirements?
In this work, we first expose a multi-layered definition of privacy-related data to correctly report data collection in Android apps. We further create a dataset of privacy-sensitive data classes that may be used as input by an Android app. This dataset takes into account data collected both through the user interface and system APIs.
We manually examine the data safety sections of 70 Android apps to observe how data collection is reported, identifying instances of over- and under-reporting. Additionally, we develop a prototype to statically extract and label privacy-related data collected via app source code, user interfaces, and permissions. Comparing the prototype's results with the data safety sections of 20 apps reveals reporting discrepancies. Using the results from two Messaging and Social Media apps (Signal and Instagram), we discuss how app developers under-report and over-report data collection, respectively, and identify inaccurately reported data categories.
Our results show that app developers struggle to accurately report data collection, either due to Google's abstract definition of collected data or insufficient existing tool support. }},
  author       = {{Khedkar, Mugdha and Mondal, Ambuj Kumar and Bodden, Eric}},
  booktitle    = {{In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW ’24)}},
  location     = {{Sacramento, California}},
  title        = {{{Do Android App Developers Accurately Report Collection of Privacy-Related Data?}}},
  doi          = {{10.1145/3691621.3694949}},
  year         = {{2024}},
}

@article{53622,
  abstract     = {{<jats:p>In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students’ perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students’ answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.</jats:p>}},
  author       = {{Höper, Lukas and Schulte, Carsten}},
  issn         = {{1648-5831}},
  journal      = {{Informatics in Education}},
  keywords     = {{Computer Science Applications, Communication, Education, General Engineering}},
  publisher    = {{Vilnius University Press}},
  title        = {{{Empowering Students for the Data-Driven World: A Qualitative Study of the Relevance of Learning about Data-Driven Technologies}}},
  doi          = {{10.15388/infedu.2024.19}},
  year         = {{2024}},
}

@inproceedings{57209,
  author       = {{Höper, Lukas and Schulte, Carsten}},
  booktitle    = {{Proceedings of the 24th Koli Calling International Conference on Computing Education Research}},
  location     = {{Koli, Finnland}},
  publisher    = {{ACM}},
  title        = {{{New Perspectives on the Future of Computing Education: Teaching and Learning Explanatory Models}}},
  doi          = {{10.1145/3699538.3699558}},
  year         = {{2024}},
}

@inproceedings{55481,
  author       = {{Höper, Lukas and Schulte, Carsten and Mühling, Andreas}},
  booktitle    = {{Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1}},
  location     = {{Mailand, Italien}},
  publisher    = {{ACM}},
  title        = {{{Students' Motivation and Intention to Engage with Data-Driven Technologies from a CS Perspective in Everyday Life}}},
  doi          = {{10.1145/3649217.3653625}},
  year         = {{2024}},
}

@inproceedings{55656,
  author       = {{Höper, Lukas and Schulte, Carsten and Mühling, Andreas}},
  booktitle    = {{Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1}},
  publisher    = {{ACM}},
  title        = {{{Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing Education}}},
  doi          = {{10.1145/3632620.3671118}},
  volume       = {{10}},
  year         = {{2024}},
}

@inproceedings{57109,
  author       = {{Grote, Eva-Maria and Wilke, Daria and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE International Symposium on Systems Engineering (ISSE)}},
  location     = {{Perugia}},
  publisher    = {{IEEE}},
  title        = {{{Roleplaying made easy – A Meta-model for Roles in Systems Engineering}}},
  doi          = {{10.1109/isse63315.2024.10741132}},
  year         = {{2024}},
}

@inproceedings{57110,
  author       = {{Koldewey, Christian and Ebel, Martin and Winter, Johannes and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 57th Hawaii International Conference on System Sciences}},
  location     = {{Hawaii}},
  title        = {{{Data-driven Services and Servitization in Manufacturing: Innovation, Engineering, Transformation, and Management}}},
  year         = {{2024}},
}

@misc{57319,
  author       = {{Raj, Roja}},
  title        = {{{Hexagon Shape Formation in the Amoebot Model with Immobilized Particles}}},
  year         = {{2024}},
}

@inbook{57323,
  author       = {{Karalis, Nikolaos and Bigerl, Alexander and Demir, Caglar and Heidrich, Liss and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783031703645}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Evaluating Negation with Multi-way Joins Accelerates Class Expression Learning}}},
  doi          = {{10.1007/978-3-031-70365-2_12}},
  year         = {{2024}},
}

@misc{57416,
  abstract     = {{The increased complexity of modern software has led to much more
sophisticated attack vectors. As a result, we require newer vulnerability detection
methods to ensure software security without compromising efficiency.
The Code Property Graph (CPG) is a program representation that provides a comprehensive overview of program behavior, combining abstract syntax trees, control flow
graphs, and program dependence graphs. With such a detailed data structure, we can
detect patterns that characterize known vulnerabilities and identify various security
threats. Querying the combined data structure instead of the individual graphs enables the detection of multidimensional scenarios.
This work aims to integrate the advantages of CPGs into software systems that utilize
the Jimple intermediate representation. We introduce JimNode, a novel approach for
generating CPGs specifically tailored to Jimple. Despite the model incompatibility, our
evaluation, which covered approximately 50,800 methods, reveals an 88.07% similarity
of the inter-statement edges compared to Joern, the state-of-the-art tool for CPG
generation. We provide a detailed analysis of our methodology and discuss why it is
better suited for Jimple programs than Joern’s language-agnostic approach.}},
  author       = {{Youkeim, Michael Hany Fawzy}},
  publisher    = {{Paderborn University}},
  title        = {{{Tailoring Code Property Graphs to Jimple}}},
  year         = {{2024}},
}

@inproceedings{54312,
  author       = {{Büttner, Markus and Alt, Christoph and Kenter, Tobias and Köstler, Harald and Plessl, Christian and Aizinger, Vadym}},
  booktitle    = {{Proceedings of the Platform for Advanced Scientific Computing Conference (PASC)}},
  publisher    = {{ACM}},
  title        = {{{Enabling Performance Portability for Shallow Water Equations on CPUs, GPUs, and FPGAs with SYCL}}},
  doi          = {{10.1145/3659914.3659925}},
  year         = {{2024}},
}

@inproceedings{57738,
  author       = {{Hüsing, Sven and Schönbrodt, Sarah}},
  title        = {{{Förderung von Epistemic Agency – Entwicklung von Computational Essays bei der Bearbeitung datengetriebener, realer Problemstellungen}}},
  doi          = {{10.17877/DE290R-25018}},
  year         = {{2024}},
}

@inproceedings{55404,
  abstract     = {{Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee{’}s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum {Explain Like I am Five} and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.}},
  author       = {{Alshomary, Milad and Lange, Felix and Booshehri, Meisam and Sengupta, Meghdut and Cimiano, Philipp and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}},
  editor       = {{Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}},
  pages        = {{11523–11536}},
  publisher    = {{ELRA and ICCL}},
  title        = {{{Modeling the Quality of Dialogical Explanations}}},
  year         = {{2024}},
}

@misc{58132,
  author       = {{Hartinger, Maximilian}},
  publisher    = {{Paderborn University}},
  title        = {{{Controlling I/O Devices from Hardware-Mapped ReconROS Nodes}}},
  year         = {{2024}},
}

@article{59074,
  author       = {{Hu, Jiahai and Wang, Lin and Wu, Jing and Pei, Qiangyu and Liu, Fangming and Li, Bo}},
  issn         = {{1389-1286}},
  journal      = {{Computer Networks}},
  publisher    = {{Elsevier BV}},
  title        = {{{A Comparative Measurement Study of Cross-Layer 5G Performance Under Different Mobility Scenarios}}},
  doi          = {{10.1016/j.comnet.2024.110952}},
  volume       = {{257}},
  year         = {{2024}},
}

@inproceedings{54291,
  author       = {{Kablo, Emiram and Kader, Katharina and Arias Cabarcos, Patricia}},
  booktitle    = {{Extended Abstracts of the CHI Conference on Human Factors in Computing Systems}},
  publisher    = {{ACM}},
  title        = {{{"I'm actually going to go and change these passwords": Analyzing the Usability of Credential Audit Interfaces in Password Managers}}},
  doi          = {{10.1145/3613905.3650889}},
  year         = {{2024}},
}