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- TextdokumentArguing machine learning assurance for certification(Software Engineering 2025 – Companion Proceedings, 2025) Varchev, Tihomir; Staudacher, Stephan; Daw, Zamira; Holloway, MichaelAviation certification traditionally relies on standards shaped by expert consensus on best practices. However, for emerging technologies like machine learning (ML), establishing these practices is difficult, particularly when in-flight testing is not feasible before certification, which can slow innovation. Argument-based certification offers a promising solution to bridge this gap by providing a structured and transparent framework for discussions between applicants and certification authorities. It allows authorities to compare proposed compliance methods across similar technologies from different applicants, helping to identify best practices and assess risks associated with new innovations. In this paper, we apply the Overarching Properties organizing principle to define and assess the means of compliance (MoC) for three publicly available ML aviation applications. This paper demonstrates how OPs can be used to support the demonstration of compliance. Although we observed some commonalities in the three arguments, the specificities of the technology and its application highlight the differences in the strategies used to demonstrate assurance.
- TextdokumentAutomotive Security Engineering: A Demonstration of an Integrated Approach to EAST-ADL and Security Modeling(Software Engineering 2025 – Companion Proceedings, 2025) Fischer, Alexander; Kolagari, Ramin TavakoliThe automotive industry’s increasing reliance on software innovations has introduced significant security challenges. With connected vehicles and semi-autonomous systems becoming commonplace, automotive cyberattacks are frequently covered in the media. Public awareness is growing that cybersecurity plays a crucial role, akin to the life-saving innovations of the 1970s, such as mandatory seatbelts and speed limits. It is evident that security must be integrated throughout the entire development lifecycle and operation of automotive software systems, with every stakeholder considering security aspects. However, this is complicated by the vast and often complex nature of the security landscape and the limited accessibility of essential security frameworks like MITRE ATT&CK, which are primarily oriented towards implementation rather than serving as an entry point for comprehensive security analysis. In response to these challenges, this paper presents the Security Abstraction Model (SAM), a metamodel-based approach designed to address security concerns across all stages of automotive software development, focusing on the early phases and on understandability among all stakeholders. SAM offers a structured framework for defining, analyzing, and implementing security requirements within automotive systems, leveraging established metamodeling techniques. This paper demonstrates how SAM can be integrated with EAST-ADL to create a systematic approach for securing complex automotive software architectures. In order to illustrate its applicability, an integrated demonstration is developed, showcasing the combined use of EAST-ADL and security modeling.
- TextdokumentAutomotive Software Engineering in an increasingly Data-Driven Automotive Sector(Software Engineering 2025 – Companion Proceedings, 2025) Denninger, Oliver; Axmann, Joachim K.; Kacianka, Severin; Westphal, BerndAutomotive trends such as power-train electrification, personalization, connectivity, and automated driving are not well supported by the classical approach to hardware/software architectures that centre around numerous, dedicated electronic control units (ECUs) where software is delivered as part of the ECU and it and its environment does not change much after vehicle assembly. Similarly, current electronic architectures and vehicles do not exploit data-driven software development practices and do not have the capability to make use of unprecedented amounts of data on the vehicle, but also its environment and the Internet. These trends ask for a data-driven approach where the development, production, and operation data of automotive software feed back into continuous correction, improvement, and personalization. In this paper, we report findings from the Transformation Hub Automotive Software Engineering (TASTE) with two years of intensive discussions and workshops with a wide range of companies regarding the challenges facing the German based automotive industry in general, as well as individual companies from Original Equipment Manufacturers (OEM) to different suppliers (TIER-n). We discuss how previously different approaches need to be integrated into new software-centr
- TextdokumentBounds for Quantum Circuits using Logic-Based Analysis(Software Engineering 2025 – Companion Proceedings, 2025) Fauseweh, Benedikt; Hermann, Ben; Howar, FalkWe explore ideas for scaling verification methods for quantum circuits using SMT (Satisfiability Modulo Theories) solvers. We propose two primary strategies: (1) decomposing proof obligations via compositional verification and (2) leveraging linear over-approximation techniques for gate effects. We present two examples and demonstrate the application of these ideas to proof Hamming weight preservation.
- TextdokumentBridge the Gap Between HPC Systems and Various Quantum Platforms: A Unified Quantum Platform(Software Engineering 2025 – Companion Proceedings, 2025) Elsharkawy, Amr; Guo, Xiaorang; Schulz, MartinThis paper presents the development of a unified quantum platform (UQP) and how it is being integrated into high-performance computing (HPC) infrastructure at both software and hardware levels. Building on previous work in hybrid HPC-QC workflows, the UQP establishes a unified low-level interface between classical HPC systems and emerging quantum hardware. Key contributions include a unified HPCQC runtime library that bridges quantum intermediate representation (QIR) programming systems with a hybrid ISA, along with a scalable QCP micro-architecture. Verified for correctness, the UQP runtime library demonstrates super-linear scalability in execution time and memory with the number of qubits, addressing critical needs for QC hardware scalability.
- TextdokumentCase Study: Creating a Reusable Execution Environment for WiKoDa(Software Engineering 2025 – Companion Proceedings, 2025) Blessing, Christoph Benjamin; Khan, Sabih Ahmed; Bernoth, JanEnhancing reusability is a key challenge in implementing FAIR principles for research software. This case study explores strategies to boost the reusability of the WiKoDa research project by creating a Reusable Execution Environment (REE). The REE facilitates software execution in an independent, reproducible setting.
- TextdokumentA Classification Framework for Scientific Documents to Support Knowledge Graph Population(Software Engineering 2025 – Companion Proceedings, 2025) Kaplan, Angelika; Keim, Jan; Greiner, Lukas; Koziolek, Anne; Reussner, RalfResearch papers are a central communication medium to share new scientific insights and progress and are, nowadays, stored as PDF files. However, little effort is spent on reorganizing information with effective knowledge classification and comprehensive representation during the publication process. In terms of software engineering (SE), those papers are also aligned to software artifacts and research data. Aggregating knowledge and empirical evidence is done with systematic literature studies that tend to be very time-consuming and require a manual inspection of the respective research artifacts (paper-and data-wise). Research knowledge graphs like the Open Research Knowledge Graph (ORKG) aim to contribute to and rethink scholarly communication by providing formats while easing the processing of semantic information. Therefore, ORKG offers templates to summarize and structure a research artifact’s content, providing metadata as well. Based on this, researchers can connect similar papers, reuse replication artifacts, and generate literature studies more easily. However, adding papers to ORKG is still a tedious manual process. Moreover, selecting suitable template formats is challenging and can be highly domain-specific. To support the manual process, we aim to provide an automated classification framework for scientific papers, supporting the knowledge graph population. This framework intends to be flexible, allowing various input data and schemas, so it can be applied and trained in a multitude of research fields in SE. In this paper, we present the concept of the framework’s implementation, and an excerpt of the evaluation result in one of the research subfields in SE, namely software architecture and design.
- TextdokumentCode Generation for Niche Programming Languages with Large Language Models(Software Engineering 2025 – Companion Proceedings, 2025) Kogler, Philipp; Chen, Wei; Wallner, StefanCode generation is a prominent use-case for Large Language Models (LLMs). Specialized LLMs such as CodeLlama or Codestral are trained on a large variety of programming languages and achieve a strong performance on coding tasks. However, when applied to less common programming languages which are not included in their pre-training corpus, their performance decreases. In this work, we describe an approach to integrate a LLM in the context of a coding copilot for specific applications where code shall be generated in a niche general-purpose programming language. We study the use of an intermediate domain-specific-language to limit the scope to the application-specific needs, and to enable the LLM to reliably generate code in such an application-specific scenario. We evaluate this method on two use-cases: Generating constraints in the context of product configuration using the MiniZinc constraint language, and generating test specifications in the context of railway infrastructure using the Balise Telegram Test Language. Our results show that defining an intermediate scope-limited DSL improves the performance of an LLM in our evaluated application-specific code generation scenarios. However, we can not guarantee that the presented performance results are generalizable to all scenarios.
- TextdokumentComputational Capabilities and Compiler Development for Neutral Atom Quantum Processors --- Connecting Tool Developers and Hardware Experts(Software Engineering 2025 – Companion Proceedings, 2025) Schmid, Ludwig; Locher, David F.; Rispler, Manuel; Blatt, Sebastian; Zeiher, Johannes; Müller, Markus; Wille, Robert
- TextdokumentDiagrammatic Quantum Circuit Compression for Hamiltonian Simulation(Software Engineering 2025 – Companion Proceedings, 2025) Wadewitz, Victoria; Szasz, Aaron; Camps, Daan; Klymko, Katherine; Stollenwerk, TobiasOne of the promising applications for early quantum computers is the simulation of of dynamical quantum systems. Due to the limited coherence time of such devices, the depth-compression of quantum circuits is crucial to facilitate useful results. It has been shown that certain quantum models can even be compressed to constant depth, meaning it is only linearly dependent on the number of qubits, but independent of the simulation time and the number of Trotter steps. This has been done by extracting the circuit structure derived from the model characteristics via Hamiltonian simulation. Based on these results, we present a diagrammatic approach to circuit compression utilizing a powerful technique for reasoning about quantum circuits called ZX-calculus. We demonstrate our approach by deriving constant-depth circuit compressions for quantum models known to be constant-depth, as well as novel models previously unstudied. Our method could serve as a first step toward the development of more advanced circuit compression methods, that could be employed to enable Hamiltonian simulation of a larger variety of quantum models, and beyond.