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- 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)
- Konferenzbeitrag4th Workshop on Avionics Systems and Software Engineering(Software Engineering 2022 Workshops, 2022) Annighöfer, Björn; Schweiger, Andreas; Reich, MarinaPreface of the Workshop on Avionics Systems and Software Engineering
- 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.
- 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.
- KonferenzbeitragComparison of Aviation and Automotive Standards and Methods in Terms of Safety and Cybersecurity(Software Engineering 2022 Workshops, 2022) Akkus, Yusuf; Annighoefer, BjoernSafety and security methods from the aviation and automotive are compared. Current safety and security standards and regulations for both product development aspects like systems engineering, hardware/software development and their management are considered. Methods and processes are investigated. The main purpose is to figure out and understand the backgrounds and to characterize the similarities and differences. Moreover, potential opportunities for transferring methods from one industry to another are identified. Aviation has more systematic development and involves authorities throughout the complete development lifecycle. Huge volumes in automotive leads to quality-driven development. Assessment structure and process activities provide potential transfer. For security both areas face same challenges and standardization activities and development run parallel. Methods are being mainly taken over from safety and assessment is incorporated into the safety assessment lifecycle today. For certification process, authorities must take action in both areas since the security ecosystem includes a bigger scope like infrastructure, communication devices, traffic control.
- KonferenzbeitragErweiterung von Gefahren- und Risikoanalysen der Funktionalen Sicherheit um Aspekte der Produktsicherheit nach ISO 12100(Software Engineering 2022 Workshops, 2022) Emig, Moritz; Metz, Pierre; Reißing, RalfBei der Brose Fahrzeugteile SE & Co. KG wird eine Gefährdungs- und Risikoanalyse (HARA) nach der ISO 26262 [DI18] angewandt. Diese Analyse betrachtet zufällige oder systematische Fehlfunktionen, die durch E/E-Probleme (Elektrik/Elektronik) verursacht werden. Die HARA soll nun um die Produktsicherheit (ProSi) erweitert werden. Diesbezüglich existiert bereits ein Brose-interner Ansatz, mit welchem allerdings nur funktionale Aspekte ermittelbar sind. Bei der ProSi-HARA sollen allerdings auch nicht-funktionale Gefährdungen (NFH) berücksichtigt werden. Damit bei der ProSi-HARA-Durchführung so viele NFH wie möglich ermittelt werden können, wurde als Hilfsmittel eine Checkliste generiert. Diese basiert auf der Tabelle B.1 der ISO 12100 [DI11], die sich mit der Sicherheit von Maschinen beschäftigt. Anschließend wurden drei alternative Konzepte zur Integration der Checkliste in die ProSi-HARA erarbeitet. Die Konzepte wurden in einem Pilotprojekt bei Brose erprobt. Dabei hat sich eines der Konzepte klar mit dem besten Aufwand-Nutzenverhältnis durchgesetzt. Mittels dieser Vorgehensweise konnten mit der Checkliste eine Vielzahl eigenständiger NFH identifiziert werden, die mit der herkömmlichen HARA nicht erkannt worden wären. Mit eigenständig sind hier NFH gemeint, die nicht in Zusammenhang mit funktionalen Fehlern oder Use Cases (funktionalen Anwendungsfälle) stehen.
- KonferenzbeitragExtending Behaviour-Driven Development of Avionic Systems to Flight Simulators(Software Engineering 2022 Workshops, 2022) Ibrahim, Mohamad; Durak, Umut; Tariq, HaseebThe role of avionics is crucial for several aircraft functions, from communication to collision avoidance. Amongst others, modern air transportation is the result of the continuous improvement in avionics technology. One of the recent trends in avionics software engineering is streamlining the process with practices that remove the disconnects between the activities to achieve a cost-eff ective and agile engineering life cycle, allowing to cope with the volatile requirements found in emerging segments such as Advanced Air Mobility. Behaviour-Driven Development (BDD) is one of these practices; it connects requirements to (acceptance-) testing. There are already eff orts in adapting BDD for avionic systems. This paper proposes extending BDD of avionic systems to fl ight simulation. It presents an approach for using the requirement specifi cation language of BDD, Gherkin, for setting up the test scenarios and the test framework of BDD, Cucumber, as a test oracle.
- 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.
- 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.
- KonferenzbeitragMessage from the SE’22 Workshop Chairs(Software Engineering 2022 Workshops, 2022) Michael, Judith; Pfeiffer, Jérôme; Wortmann, AndreasPreface of the SE’22 Workshop Proceedings