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- KonferenzbeitragMethode zur Bewertung der Risikobilanz autonomer Fahrzeuge aus Sicht von Fahrzeughaltern(Software Engineering 2022 Workshops, 2022) Potdevin, Yannik; Nowotka, DirkWir stellen eine Methode zur Ermittlung der Risikobilanz autonomer Fahrzeuge im Vergleich zu durchschnittlicher menschlicher Fahrleistung vor. Mithilfe unserer Methode werden Fahrzeugtests zur Prüfung von autonomen Fahrzeugen entwickelt und die zugehörigen Testergebnisse bewertet. Im Rahmen von zwei Fallstudien erproben wir unsere Methode und stellen Auszüge der Ergebnisse vor.
- KonferenzbeitragLean Management for the Digital Workplace: A literature review on the support of Lean thinking and Lean Management methods for working in a Digital Workplace(Software Engineering 2022 Workshops, 2022) Friedemann, Robert; Moises, SimoneThis paper discusses effectiveness of Lean Management applied on Digital Workplaces. Over decades, methods and thinking of Lean Management has emerged in the production area and were transferred to others business areas thereinafter. Applicability of Lean Management thinking and acting on a digital workplace is going to be analyzed herein. Definitions and examples will be discussed.
- Konferenzbeitrag19th Workshop on Automotive Software Engineering (ASE'22)(Software Engineering 2022 Workshops, 2022) Dörr, Heiko; Helke, SteffenPreface of the 19th Workshop on Automotive Software Engineering (ASE'22)
- KonferenzbeitragWann fahren wir autonom? Eine Untersuchung aus technischer und rechtlicher Sicht.(Software Engineering 2022 Workshops, 2022) Birkemeyer, Lukas; Delventhal, Marlene; Schaefer, Ina; Schmieder, FabianAutonomes Fahren rückt immer mehr in den Fokus der Gesellschaft. Im Gesetz zum autonomen Fahren sieht die deutsche Bundesregierung einen Meilenstein, der die Realisierung des autonomen Fahrens ermöglicht. Dennoch sind keine serienreifen, autonomen Fahrfunktionen auf deutschen Straßen zugelassen. Wie weit sind wir noch vom Traum vom autonomen Fahren entfernt? In diesem Beitrag wird untersucht, welche offenen Fragen es aus juristischer und technischer Sicht gibt und in welchen Punkten beide Disziplinen aufeinander warten. Der aktuelle Stand wird in den Bereichen Typgenehmigung/Zulassung, Fahrerlaubnis (Führerschein) und Haftung systematisch untersucht. Mit Hilfe eines Gedankenexperiments, bei dem der menschliche Fahrer durch einen Roboterfahrer ersetzt wird, wird eine einheitliche Basis geschaffen, um automatisierte Fahrfunktionen und Fahrerassistenzsysteme zu vergleichen. Dieser Beitrag deckt offene Fragen auf, die für die Realisierung des vollständig autonomen Fahrens noch beantwortet werden müssen. Eine praktische Umsetzung erfordert eine enge interdisziplinäre Zusammenarbeit, insbesondere von Expert*innen aus den Bereichen Regulierung, Technik und Sozialwissenschaften.
- KonferenzbeitragPraxisbericht: Anforderungsmanagement zur Unterstützung im Aufbau von modernen Enterprise Architekturen in der Rubner Gruppe(Software Engineering 2022 Workshops, 2022) Alton, SeverinHistorisch gewachsene Enterprise Architekturen stellen jedes Unternehmen vor komplexe Heraus-forderungen. Die Transformation ganzer Systemlandschaften ist kostenintensiv und dauert mitunter Jahre. Die Fachbereiche der Unternehmen müssen auf ständige Änderungen am Markt in kürzester Zeit reagieren. Auch moderne IT-Organisationen müssen deshalb in der Lage sein, neue Anforderungen der Fachbereiche in kurzen Zeiten umzusetzen und dabei den Ressourceneinsatz so effizient wie möglich zu gestalten. Dieser Artikel beschreibt, welchen Ansatz die IT-Organisation der Rubner Gruppe gewählt hat, um die Fachbereiche mit den bestmöglichen IT-Lösungen zu versorgen.
- KonferenzbeitragRequirement Management in Enterprise Systems Projects(Software Engineering 2022 Workshops, 2022) Weiss, Christoph; Keckeis, JohannesPreface of the Workshop Requirement Management in Enterprise Systems Projects (AESP - Anforderungsmanagement in Enterprise Systems-Projekten)
- KonferenzbeitragBuild Your Own Training Data - Synthetic Data for Object Detection in Aerial Images(Software Engineering 2022 Workshops, 2022) Laux, Lea; Schirmer, Sebastian; Schopferer, Simon; Dauer, JohannMachine 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.
- KonferenzbeitragProfessionalisation in ERP Selection Revisited II(Software Engineering 2022 Workshops, 2022) Humpl, StefanThe implementation of new ERP systems or the adaptation of existing ERP systems is a central challenge for modern companies, which is also reflected in the development of relevant job advertisements. A specific analysis of relevant job advertisements in Austria shows a professionalisation in ERP selection, but also a shift towards employing such experts in the own company versus outsourcing this expertise. Job titles and job specific requirements (competences) mentioned in job advertisements in 2017 and 2021 show a respective development. But it also becomes visible that the labour market for relevant experts is marked by a growing demand and insufficient supply, which implies that the more recent job advertisements are broader, more detailed in asking for competences, and therefore showing a hope for somehow fitting candidates.
- KonferenzbeitragImportant Factors for Implementing a Resilient System(Software Engineering 2022 Workshops, 2022) Ploder, Christian; Janetschek, Julian; Dilger, Thomas; Bernsteiner, ReinhardProduction systems in the context of Industry 4.0 can react flexibly to changes and failures of components by equipping the system components with some intelligence. Cyber-Physical Systems (CPS) represent a crucial technology of Industry 4.0, characterized by the integration of computation and physical processes. Future production and manufacturing plants should therefore have resilient properties in order to be able to react to faults without human intervention. In this paper, a concept for a resilient production system is discussed using the example of the Fischertechnik Learning Factory 4.0 (FTLF). In the course of this, the incidents occurring in continuous operation are determined in an observation. Based on this observation, the guidelines, strategies, prerequisites, and principles relating to the concept of resilience will be shown and discussed for the resilient architecture. The prerequisites for a resilient architecture include the absence of single points of failure and independence between the sub-components of a system. A resilient production system also requires process disruption management to handle failures with re-configurations based on previously defined possible solutions. A resilient architecture should already be taken into account in the planning and design phase, at which point all incidents that can occur in the system should be known. This fact represents a significant challenge when implementing a resilient architecture in any system.
- KonferenzbeitragAn Anthropomorphic Approach to establish an Additional Layer of Trustworthiness of an AI Pilot(Software Engineering 2022 Workshops, 2022) Regli, Christoph; Annighoefer, BjörnAI 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.