@inproceedings{63652, abstract = {{In dynamic environments, product management plays a key role in aligning innovation, customer needs, and strategic decision-making. Digitalization offers significant opportunities to enhance this role by enabling data-driven insights for improved customer and product understanding—yet its successful implementation requires a fundamental transformation. Based on a systematic literature review, this study synthesizes key advantages, challenges, and design fields that shape this transformation. The results highlight performance benefits across business, product, process, and decision-making dimensions, while also uncovering barriers rooted in strategy, organization, people, and technology. To address these barriers, critical enablers and conditions for success are identified. Four overarching design fields provide orientation for structuring digitalization efforts and guiding organizational change in industrial practice. The paper provides both a conceptual foundation and a practical guide for companies seeking to digitalize their product management effectively.}}, author = {{Fichtler, Timm and Petzke, Lisa Irene and Grigoryan, Khoren and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{Proceedings of the 59th Hawaii International Conference on System Sciences}}, location = {{Maui, Hawaii}}, title = {{{Enhancing Product Management Performance through Digitalization: Advantages, Challenges, Design Fields}}}, year = {{2026}}, } @inproceedings{63890, abstract = {{The computation of highly contracted electron repulsion integrals (ERIs) is essential to achieve quantum accuracy in atomistic simulations based on quantum mechanics. Its growing computational demands make energy efficiency a critical concern. Recent studies demonstrate FPGAs’ superior performance and energy efficiency for computing primitive ERIs, but the computation of highly contracted ERIs introduces significant algorithmic complexity and new design challenges for FPGA acceleration.In this work, we present SORCERI, the first streaming overlay acceleration for highly contracted ERI computations on FPGAs. SORCERI introduces a novel streaming Rys computing unit to calculate roots and weights of Rys polynomials on-chip, and a streaming contraction unit for the contraction of primitive ERIs. This shifts the design bottleneck from limited CPU-FPGA communication bandwidth to available FPGA computation resources. To address practical deployment challenges for a large number of quartet classes, we design three streaming overlays, together with an efficient memory transpose optimization, to cover the 21 most commonly used quartet classes in realistic atomistic simulations. To address the new computation constraints, we use flexible calculation stages with a free-running streaming architecture to achieve high DSP utilization and good timing closure.Experiments demonstrate that SORCERI achieves an average 5.96x, 1.99x, and 1.16x better performance per watt than libint on a 64-core AMD EPYC 7713 CPU, libintx on an Nvidia A40 GPU, and SERI, the prior best-performing FPGA design for primitive ERIs. Furthermore, SORCERI reaches a peak throughput of 44.11 GERIS (109 ERIs per second) that is 1.52x, 1.13x, and 1.93x greater than libint, libintx and SERI, respectively. SORCERI will be released soon at https://github.com/SFU-HiAccel/SORCERI.}}, author = {{Stachura, Philip and Wu, Xin and Plessl, Christian and Fang, Zhenman}}, booktitle = {{Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA '26)}}, isbn = {{9798400720796}}, keywords = {{electron repulsion integrals, quantum chemistry, atomistic simulation, overlay architecture, fpga acceleration}}, pages = {{224--234}}, publisher = {{Association for Computing Machinery}}, title = {{{SORCERI: Streaming Overlay Acceleration for Highly Contracted Electron Repulsion Integral Computations in Quantum Chemistry}}}, doi = {{10.1145/3748173.3779198}}, year = {{2026}}, } @inproceedings{64075, author = {{Humpert, Lynn and Graunke, Jannis and Cichon, Gerrit and Ammanagi, Anuradha and Schierbaum, Anja and Dumitrescu, Roman}}, booktitle = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}}, publisher = {{IEEE}}, title = {{{Generative AI in Systems Engineering: Automated Creation of System Architectures and Early-Stage Calculation in the B2B Sector}}}, doi = {{10.1109/isse65546.2025.11370000}}, year = {{2026}}, } @article{63834, abstract = {{Abstract Many Android apps collect data from users, and the European Union’s General Data Protection Regulation (GDPR) mandates clear disclosures of such data collection. However, apps often use third-party code, complicating accurate disclosures. This paper investigates how accurately current Android apps fulfill these requirements. In this work, we present 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. Based on this, we implement a semi-automated prototype that detects and labels privacy-related data collected by a given Android app. 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. We compare our prototype’s results with the data safety sections of 20 apps revealing 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. A broader study of 7,500 Android apps reveals that apps most frequently collect data that can partially identify users. Although system APIs consistently collect large amounts of privacy-related data, user interfaces exhibit some more diverse data collection patterns. A more focused study on various domains of apps reveals that the largest fraction of apps collecting personal data belong to the domain of Messaging and Social Media . Our findings show that location is collected frequently by apps, specially from the E-commerce and Shopping domain. However, it is often under-reported in app data safety sections. Our results highlight the need for greater consistency in privacy-aware app development and reporting practices. }}, author = {{Khedkar, Mugdha and Kumar Mondal, Ambuj and Bodden, Eric}}, issn = {{0928-8910}}, journal = {{Automated Software Engineering}}, number = {{2}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A study of privacy-related data collected by Android apps}}}, doi = {{10.1007/s10515-025-00589-3}}, volume = {{33}}, year = {{2026}}, } @article{64223, abstract = {{The complexity and interconnectivity of modern automotive systems are rapidly increasing, particularly with the rise of distributed and cooperative driving functions. These developments increase exposure to a range of disruptions, from technical failures to cyberattacks, and demand a shift towards resilience-by-design. This study addresses the early integration of resilience into the automotive design process by proposing a structured method for identifying gaps and eliciting resilience requirements. Building upon the concept of resilience scenarios, the approach extends traditional hazard and threat analyses as defined in ISO 26262, ISO 21448 and ISO/SAE 21434. Using a structured, graph-based modeling method, these scenarios enable the anticipation of functional degradation and its impact on driving scenarios. The methodology helps developers to specify resilience requirements at an early stage, enabling the integration of resilience properties throughout the system lifecycle. Its practical applicability is demonstrated through an example in the field of automotive cybersecurity. This study advances the field of resilience engineering by providing a concrete approach for operationalizing resilience within automotive systems engineering.}}, author = {{Mpidi Bita, Isaac and Ugur, Elif and Hovemann, Aschot and Dumitrescu, Roman}}, issn = {{1999-5903}}, journal = {{Future Internet}}, number = {{1}}, publisher = {{MDPI AG}}, title = {{{Resilience-by-Design: Extracting Resilience Requirements Using the Resilience Graph in the Automotive Concept Phase}}}, doi = {{10.3390/fi18010051}}, volume = {{18}}, year = {{2026}}, } @inproceedings{64224, author = {{Yee, Jingye and Hermelingmeier, Dominik and Thederajan, Abishai Asir A. and Low, Cheng Yee and Gossen, Alexander and Dumitrescu, Roman}}, booktitle = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}}, publisher = {{IEEE}}, title = {{{System Architecture and Analytical Inverse Kinematics for Autonomous Docking of Passenger Boarding Bridges}}}, doi = {{10.1109/isse65546.2025.11370093}}, year = {{2026}}, } @inproceedings{64226, author = {{Hermelingmeier, Dominik and Graunke, Jannis and Menne, Leon and Schierbaum, Anja Maria and Dumitrescu, Roman}}, booktitle = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}}, publisher = {{IEEE}}, title = {{{Process Model for the Development of Physical Prototypes in Context of Hardware Start-Ups using Maker Systems Engineering}}}, doi = {{10.1109/isse65546.2025.11370109}}, year = {{2026}}, } @inproceedings{64225, author = {{Grote, Eva-Maria and Koldewey, Christian and Voelk, Thomas Alexander and Schwarz, Stefan Eric and Dumitrescu, Roman and Albers, Albert}}, booktitle = {{2025 IEEE International Symposium on Systems Engineering (ISSE)}}, publisher = {{IEEE}}, title = {{{From Generic to Specific: Scalable Role Modeling for Engineering Advanced Systems}}}, doi = {{10.1109/isse65546.2025.11370103}}, year = {{2026}}, } @inproceedings{64221, author = {{Lick, Jonas and Kattenstroth, Fiona and van der Valk, Hendrik and Trienens, Malte and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{2025 Winter Simulation Conference (WSC)}}, publisher = {{IEEE}}, title = {{{Characterizing Digital Factory Twins: Deriving Archetypes for Research and Industry}}}, doi = {{10.1109/wsc68292.2025.11338979}}, year = {{2026}}, } @inproceedings{64228, author = {{Hanke, Fabian and von Heißen, Oliver and Feld, Markus and Heuwinkel, Tim and Hovemann, Aschot and Dumitrescu, Roman}}, booktitle = {{2025 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}}, publisher = {{IEEE}}, title = {{{Cross-View Trace Link Prediction with Multi-Feature GNNs: Creating and maintaining Traceability from Requirements to Components}}}, doi = {{10.1109/ictmod66732.2025.11371884}}, year = {{2026}}, } @inproceedings{64227, author = {{Könemann, Ulf and Niemeyer, Marcel and Schierbaum, Anja and Dumitrescu, Roman}}, booktitle = {{2025 IEEE 4th German Education Conference (GECon)}}, publisher = {{IEEE}}, title = {{{Status quo and challenges of professional Systems Engineering education in industrial practice}}}, doi = {{10.1109/gecon64629.2025.11369324}}, year = {{2026}}, } @inproceedings{64625, author = {{Fergusson, Anna and Podworny, Susanne and Fleischer, Yannik and Hüsing, Sven and Puloka, Malia S. and Biehler, Rolf and Pfannkuch, Maxine and Budgett, Stephanie and Dalrymple, Michelle}}, booktitle = {{Proceedings of the IASE 2025 Satellite Conference - Statistics and Data Science Education in STEAM}}, publisher = {{International Association for Statistics Education}}, title = {{{Branching out data science education: Developing task and computational environment design principles for teaching data science at the high school level through an international research collaboration}}}, doi = {{10.52041/iase25.138}}, year = {{2026}}, } @misc{64783, author = {{Berger, Thilo }}, title = {{{Comparing Existing Methods to Efficiently Place Drones to Connect Isolated Communication Clusters}}}, year = {{2026}}, } @article{60990, abstract = {{Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their effectiveness in low-resource languages remains underexplored, particularly in complex tasks such as end-to-end Entity Linking (EL), which requires both mention detection and disambiguation against a knowledge base (KB). In earlier work, we introduced IndEL — the first end-to-end EL benchmark dataset for the Indonesian language — covering both a general domain (news) and a specific domain (religious text from the Indonesian translation of the Quran), and evaluated four traditional end-to-end EL systems on this dataset. In this study, we propose ELEVATE-ID, a comprehensive evaluation framework for assessing LLM performance on end-to-end EL in Indonesian. The framework evaluates LLMs under both zero-shot and fine-tuned conditions, using multilingual and Indonesian monolingual models, with Wikidata as the target KB. Our experiments include performance benchmarking, generalization analysis across domains, and systematic error analysis. Results show that GPT-4 and GPT-3.5 achieve the highest accuracy in zero-shot and fine-tuned settings, respectively. However, even fine-tuned GPT-3.5 underperforms compared to DBpedia Spotlight — the weakest of the traditional model baselines — in the general domain. Interestingly, GPT-3.5 outperforms Babelfy in the specific domain. Generalization analysis indicates that fine-tuned GPT-3.5 adapts more effectively to cross-domain and mixed-domain scenarios. Error analysis uncovers persistent challenges that hinder LLM performance: difficulties with non-complete mentions, acronym disambiguation, and full-name recognition in formal contexts. These issues point to limitations in mention boundary detection and contextual grounding. Indonesian-pretrained LLMs, Komodo and Merak, reveal core weaknesses: template leakage and entity hallucination, respectively—underscoring architectural and training limitations in low-resource end-to-end EL.11Code and dataset are available at https://github.com/dice-group/ELEVATE-ID.}}, author = {{Gusmita, Ria Hari and Firmansyah, Asep Fajar and Zahera, Hamada Mohamed Abdelsamee and Ngonga Ngomo, Axel-Cyrille}}, issn = {{0169-023X}}, journal = {{Data & Knowledge Engineering}}, keywords = {{LLMs, Evaluation, End-to-end EL, Indonesian}}, pages = {{102504}}, title = {{{ELEVATE-ID: Extending Large Language Models for End-to-End Entity Linking Evaluation in Indonesian}}}, doi = {{https://doi.org/10.1016/j.datak.2025.102504}}, volume = {{161}}, year = {{2026}}, } @unpublished{62723, abstract = {{Structural measures of graphs, such as treewidth, are central tools in computational complexity resulting in efficient algorithms when exploiting the parameter. It is even known that modern SAT solvers work efficiently on instances of small treewidth. Since these solvers are widely applied, research interests in compact encodings into (Q)SAT for solving and to understand encoding limitations. Even more general is the graph parameter clique-width, which unlike treewidth can be small for dense graphs. Although algorithms are available for clique-width, little is known about encodings. We initiate the quest to understand encoding capabilities with clique-width by considering abstract argumentation, which is a robust framework for reasoning with conflicting arguments. It is based on directed graphs and asks for computationally challenging properties, making it a natural candidate to study computational properties. We design novel reductions from argumentation problems to (Q)SAT. Our reductions linearly preserve the clique-width, resulting in directed decomposition-guided (DDG) reductions. We establish novel results for all argumentation semantics, including counting. Notably, the overhead caused by our DDG reductions cannot be significantly improved under reasonable assumptions.}}, author = {{Mahmood, Yasir and Hecher, Markus and Groven, Johanna and Fichte, Johannes K.}}, booktitle = {{Pre-print of paper accepted at AAAI 2026}}, title = {{{Structure-Aware Encodings of Argumentation Properties for Clique-width}}}, year = {{2026}}, } @unpublished{62721, abstract = {{We introduce the notion of contrastive ABox explanations to answer questions of the type "Why is a an instance of C, but b is not?". While there are various approaches for explaining positive entailments (why is C(a) entailed by the knowledge base) as well as missing entailments (why is C(b) not entailed) in isolation, contrastive explanations consider both at the same time, which allows them to focus on the relevant commonalities and differences between a and b. We develop an appropriate notion of contrastive explanations for the special case of ABox reasoning with description logic ontologies, and analyze the computational complexity for different variants under different optimality criteria, considering lightweight as well as more expressive description logics. We implemented a first method for computing one variant of contrastive explanations, and evaluated it on generated problems for realistic knowledge bases.}}, author = {{Koopmann, Patrick and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille and Tiwari, Balram}}, booktitle = {{Pre-print of paper accepted at AAAI 2026}}, title = {{{Can You Tell the Difference? Contrastive Explanations for ABox Entailments}}}, year = {{2026}}, } @inproceedings{64823, abstract = {{Current legal frameworks enforce that Android developers accurately report the data their apps collect. However, large codebases can make this reporting challenging. This paper employs an empirical approach to understand developers' experience with Google Play Store's Data Safety Section (DSS) form. We first survey 41 Android developers to understand how they categorize privacy-related data into DSS categories and how confident they feel when completing the DSS form. To gain a broader and more detailed view of the challenges developers encounter during the process, we complement the survey with an analysis of 172 online developer discussions, capturing the perspectives of 642 additional developers. Together, these two data sources represent insights from 683 developers. Our findings reveal that developers often manually classify the privacy-related data their apps collect into the data categories defined by Google-or, in some cases, omit classification entirely-and rely heavily on existing online resources when completing the form. Moreover, developers are generally confident in recognizing the data their apps collect, yet they lack confidence in translating this knowledge into DSS-compliant disclosures. Key challenges include issues in identifying privacy-relevant data to complete the form, limited understanding of the form, and concerns about app rejection due to discrepancies with Google's privacy requirements. These results underscore the need for clearer guidance and more accessible tooling to support developers in meeting privacy-aware reporting obligations. }}, author = {{Khedkar, Mugdha and Schlichtig, Michael and Soliman, Mohamed Aboubakr Mohamed and Bodden, Eric}}, booktitle = {{Proceedings of the IEEE/ACM 13th International Conference on Mobile Software Engineering and Systems (MOBILESoft '26). Association for Computing Machinery, New York, NY, USA, 65–68.}}, keywords = {{static analysis, data collection, data protection, privacy-aware reporting}}, location = {{Rio de Janeiro, Brazil}}, title = {{{Challenges in Android Data Disclosure: An Empirical Study.}}}, year = {{2026}}, } @article{64821, author = {{Khedkar, Mugdha and Schlichtig, Michael and Atakishiyev, Nihad and Bodden, Eric}}, journal = {{Automated Software Engineering }}, number = {{2}}, publisher = {{Springer US}}, title = {{{Between Law and Code: Challenges and Opportunities for Automating Privacy Assessments}}}, doi = {{10.1007/s10515-026-00601-4}}, volume = {{33}}, year = {{2026}}, } @inproceedings{64909, author = {{Khedkar, Mugdha and Schlichtig, Michael and Bodden, Eric}}, booktitle = {{IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2026)}}, title = {{{Source Code-Driven GDPR Documentation: Supporting RoPA with Assessor View}}}, year = {{2026}}, } @unpublished{65017, abstract = {{Static Application Security Testing (SAST) tools play a vital role in modern software development by automatically detecting potential vulnerabilities in source code. However, their effectiveness is often limited by a high rate of false positives, which wastes developer's effort and undermines trust in automated analysis. This work presents a Graph Convolutional Network (GCN) model designed to predict SAST reports as true and false positive. The model leverages Code Property Graphs (CPGs) constructed from static analysis results to capture both, structural and semantic relationships within code. Trained on the CamBenchCAP dataset, the model achieved an accuracy of 100% on the test set using an 80/20 train-test split. Evaluation on the CryptoAPI-Bench benchmark further demonstrated the model's practical applicability, reaching an overall accuracy of up to 96.6%. A detailed qualitative inspection revealed that many cases marked as misclassifications corresponded to genuine security weaknesses, indicating that the model effectively reflects conservative, security-aware reasoning. Identified limitations include incomplete control-flow representation due to missing interprocedural connections. Future work will focus on integrating call graphs, applying graph explainability techniques, and extending training data across multiple SAST tools to improve generalization and interpretability.}}, author = {{Ohlmer, Tom and Schlichtig, Michael and Bodden, Eric}}, booktitle = {{arXiv:2603.10558}}, title = {{{FP-Predictor - False Positive Prediction for Static Analysis Reports}}}, year = {{2026}}, }