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Auflistung Software Engineering nach Titel

1 - 10 von 57
  • Konferenzbeitrag
    19th Workshop on Automotive Software Engineering (ASE'22)
    (Software Engineering 2022 Workshops, 2022) Dörr, Heiko; Helke, Steffen
    Preface of the 19th Workshop on Automotive Software Engineering (ASE'22)
  • Konferenzbeitrag
    20th Workshop on Automotive Software Engineering (ASE'23)
    (Software Engineering 2023 Workshops, 2023) Kugele, Stefan; Grunske, Lars
    Software-based systems play an increasingly important role and enable most innovations in modern cars. This workshop will address various topics related to automotive software development. The participants will discuss appropriate methods, techniques, and tools needed to address the most current challenges for researchers and practitioners.
  • Konferenzbeitrag
    4th Workshop on Avionics Systems and Software Engineering
    (Software Engineering 2022 Workshops, 2022) Annighöfer, Björn; Schweiger, Andreas; Reich, Marina
    Preface of the Workshop on Avionics Systems and Software Engineering
  • Konferenzbeitrag
    5th Workshop on Avionics Systems and Software Engineering (AvioSE'23)
    (Software Engineering 2023 Workshops, 2023) Annighoefer, Bjoern; Schweiger, Andreas; Poulaine, Stéphane
    Systems and software engineering in aerospace is subject to special challenges. For their resolution the AvioSE'23 workshop connects academia and industry with selected scientific presentations of high quality, motivating keynote talks, and an interactive panel discussion.
  • Konferenzbeitrag
    Adaption des Software-Qualitätsmanagements im Automotive-Bereich für eine Nutzung von Fremdkomponenten
    (Software Engineering 2023 Workshops, 2023) Schlosser, Joachim; Mattausch, Alexander; Neukirchner, Moritz; Holve, Rainer
    OEMs verlangen für alle zugekaufte Software die Einhaltung von Standards wie ASPICE und oft auch ihrer eigenen. In High Performance Controllern wird jedoch bereits in großem Umfang nicht-konforme Software eingesetzt. Softwarelieferungen werden eher aufgrund von Erfahrungswerten als aufgrund einer systematischen Risikobewertung akzeptiert. Viele der vorhandenen Open-Source-Software-Komponenten haben sich als zuverlässig erwiesen und läuft sogar auf kritischen Teilen der Internet-Infrastruktur. Keine dieser Komponenten ist nach automobilen Entwicklungsprinzipien entwickelt worden, dennoch handelt es sich um insgesamt robuste und qualitativ hochwertige Implementierungen. Wie kann das Qualitätsmanagement von diesen Softwareprojekten lernen und dadurch die Entwicklungseffizienz steigern, ohne dass die Codequalität darunter leidet?
  • Konferenzbeitrag
    Anforderungen an ein Vorgehensmodell zur Auswahl von Unternehmens-Software
    (Software Engineering 2023 Workshops, 2023) Weiss, Christoph; Keckeis, Johannes; Weiss, Elisabeth
    Im vorliegenden Paper wird, anhand eines Literaturreviews, der Frage nachgegangen, welchen Anforderungen Vorgehensmodelle bei deren Erstellung unterliegen. Aufgrund der zum großen Teil fragmentarischen Darstellung der Genese von Vorgehensmodellen konnten lediglich vier Anforderungen, welche in mehreren Quellen Niederschlag gefunden hatten, identifiziert werden. Diese Erkenntnis führt zu weiteren Fragen, welche einer wissenschaftlichen Untersuchung zugeführt werden sollten.
  • Konferenzbeitrag
    An Anthropomorphic Approach to establish an Additional Layer of Trustworthiness of an AI Pilot
    (Software Engineering 2022 Workshops, 2022) Regli, Christoph; Annighoefer, Björn
    AI algorithms promise solutions for situations where conventional, rule-based algorithms reach their limits. They perform in complex problems yet unknown at design time, and highly efficient functions can be implemented without having to develop a precise algorithm for the problem at hand. Well-tried applications show the AI’s ability to learn from new data, extrapolate on unseen data, and adapt to a changing environment — a situation encountered in fl ight operations. In aviation, however, certifi cation regulations impede the implementation of non-deterministic or probabilistic algorithms that adapt their behaviour with increasing experience. Regulatory initiatives aim at defining new development standards in a bottom-up approach, where the suitability and the integrity of the training data shall be addressed during the development process, increasing trustworthiness in eff ect. Methods to establish explainability and traceability of decisions made by AI algorithms are still under development, intending to reach the required level of trustworthiness. This paper outlines an approach to an independent, anthropomorphic software assurance for AI/ML systems as an additional layer of trustworthiness, encompassing top-down black-box testing while relying on a well-established regulatory framework.
  • Konferenzbeitrag
    Brake or Drive: On the Relation Between Morality and Traffic Rules when Driving Autonomously
    (Software Engineering 2023 Workshops, 2023) Rakow, Astrid; Schwammberger, Maike
    For a safe and successful future with autonomous traffic agents (ATAs), these ATAs need to be enabled to understand and abide by traffic rules. However, purely formalising and analysing traffic rules is not enough to solve this task. In this paper, we discuss the role of moral for ATAs that follow traffic rules. In particular, moral values may enable an ATA to prioritise traffic rules, in case of conflicts. We outline an approach that uses formal verification to identify situations where traffic rules are in conflict with each other, with moral values or with specific goals of an ATA. We sketch how moral values and reasoning can help an ATA to resolve such conflicts autonomously.
  • Konferenzbeitrag
    Build Your Own Training Data - Synthetic Data for Object Detection in Aerial Images
    (Software Engineering 2022 Workshops, 2022) Laux, Lea; Schirmer, Sebastian; Schopferer, Simon; Dauer, Johann
    Machine learning has become one of the most widely used techniques in artificial intelligence, especially for image processing. One of the biggest challenges in developing an accurate image processing model is to collect large amounts of data that are suffi ciently close to the real-world scenario. Ideally, real-world data is therefore used for model training. Unfortunately, real-world data is often insuffi ciently available and expensive to generate. Therefore, models are trained using synthetic data. However, there is no standardized method of how training data is generated and which properties determine the data quality. In this paper, we present fi rst steps towards the generation of large amounts of data for human detection based on aerial images. To create labeled aerial images, we are using Unreal Engine and AirSim. We report on fi rst impressions of the generated labeled aerial images and identify future challenges – current simulation tools can be used to create realistic and diverse images including labeling, but native support would be benefi cial to ease their usage.
  • Konferenzbeitrag
    Case Study: Securing MMU-less Linux Using CHERI
    (SE 2024 - Companion, 2024) Almatary, Hesham; Mazzinghi, Alfredo; Watson, Robert N. M.
    MMU-less Linux variant lacks security because it does not have protection or isolation mechanisms. It also does not use MPUs as they do not fit with its software model because of the design drawbacks of MPUs (i. e. coarse-grained protection with fixed number of protected regions). We secure the existing MMU-less Linux version of the RISC-V port using CHERI. CHERI is a hardware-software capability-based system that extends the ISA, toolchain, programming languages, operating systems, and applications in order to provide complete pointer and memory safety. We believe that CHERI could provide significant security guarantees for high-end dynamic MMU-less embedded systems at lower costs, compared to MMUs and MPUs, by: 1) building the entire software stack in pure-capability CHERI C mode which provides complete spatial memory safety at the kernel and user-level, 2) isolating user programs as separate ELFs, each with its own CHERI-based capability table; this provides spatial memory safety similar to what the MMU offers (i. e. user programs cannot access each other’s memory), 3) isolating user programs from the kernel as the kernel has its own capability table from the users and vice versa, and 4) compartmentalising kernel modules using CompartOS’ linkage-based compartmentalisation. This offers a new security front that is not possible using the current MMU-based Linux, where vulnerable/malicious kernel modules (e. g. device drivers) executing in the kernel space would not compromise or take down the entire system. These are the four main contributions of this paper, presenting novel CHERI-based mechanisms to secure MMU-less embedded Linux.