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- KonferenzbeitragSoftware Engineering und Software Management 2018(Software Engineering und Software Management 2018, 2018)
- Konferenzbeitrag1st Workshop on Software Engineering for Applied Embedded Real-Time Systems (SEERTS)(Software Engineering und Software Management 2018, 2018) Höttger, Robert; Teßmer, JörgSoftware engineering for research-intensive domains such as driver-assisted systems or autonomous driving in the automotive industry increasingly require highly sophisticated architectures as well as an optimized, safe, and secure interaction of a large number of actuators, sensors, and networked software components. In addition, connectivity, electric mobility, and heterogeneous development processes introduce new challenges for developers. The corresponding requirements in terms of real-time, causality, security, modularity, scalability or the use of various standards require appropriate domain-specific tools. Model-driven software development often plays an essential role for such tools. The SEERTS Workshop presents adequate technologies for the consideration of diverse and specific requirements within the embedded real-time domain (e.g. for robotics, automotive systems, etc.) and gives insights into their utilization in industrial applications.
- KonferenzbeitragUsing Architecture Knowledge to Improve Automated Software Architecture Design Space Exploration(Software Engineering und Software Management 2018, 2018) Busch, Axel; Koziolek, AnneDie Qualität eines Software-Systems hängt zum großen Teil von der Güte seiner zugrundeliegenden Software-Architektur ab. Während des Software-Entwicklungsprozesses müssen Entwickler häufig Kompromisse zwischen verschiedenen Qualitätsattributen eingehen. In aller Regel stehen einige dieser Qualitätsattribute miteinander in Konflikt. Beispielsweise kann häufig ein besseres Antwortzeitverhalten des Systems nur mit höheren Kosten bei der Entwicklung oder bei der verwendeten Hardware erreicht werden. Entwickler können sich bei Abwägungsentscheidungen dieser Art bereits von verschiedenen Lösungen unterstützen lassen und so ihre Software-Architektur optimieren. Dabei besteht die Möglichkeit entweder quantifizierte oder nicht-quantifizierte Qualitätsattribute auszuwerten. Sollen gut automatisierbare Ansätze verwendet werden ist es in aller Regel nötig alle Qualitätsattribute zu quantifizieren. Die hohen Kosten dieses Ansatzes zur Anwendung der dafür nötigen Metriken machen eine vollständige Quantifizierung aller nötigen Qualitätsattribute allerdings häufig nicht möglich. Der Ansatz dieser Arbeit kombiniert daher Methoden, die quantifizierte Qualitätsattribute voraussetzen, mit qualitativen Ansätzen, um den Aufwand zu reduzieren alle tatsächlich nötigen Qualitätsattribute bei der Entscheidungsunterstützung berücksichtigen zu können. Dabei kann der Entwickler entscheiden welche Qualitätsattribute sich im Einzelfall lohnen (aufwendig) zu quantifizieren und bei welchen eine qualitative Abschätzung ausreichend ist. Der genannte Ansatz erlaubt es anschließend sowohl quantifizierte, als auch nicht-quantifizierte Qualitätsattribute in Kombination zu verarbeiten und Entwurfsenscheidungen zu unterstützen. Der Ansatz wurde an zwei Systemen demonstriert und das Laufzeitverhalten als quantifizierte Qualität zusammen mit Sicherheit und Bedienbarkeit als nicht-quantifizierte Qualität betrachtet. Publiziert auf QoSA 2016 (http://ieeexplore.ieee.org/document/7515435/)
- KonferenzbeitragHazard Relation Diagrams(Software Engineering und Software Management 2018, 2018) Tenbergen, Bastian; Weyer, Thorsten; Pohl, KlausThis talk is based on a paper published in the Requirements Engineering Journal in May 2017. During the development of safety-critical systems, the development process must ensure that requirements, which are defined to mitigate a hazard, are adequate. Adequacy of such hazard-mitigating requirements (HMRs) means that the requirements may not oppose the system’s operational purpose and must sufficiently avoid, reduce, or control, the occurrence of the conditions that trigger the hazard. However, information about the occurrence of the hazard’s trigger conditions are a work product of hazard analyses during early stages of safety assessment, while HMRs are a work product of requirements engineering. Dependencies between HMRs and hazard analysis results are implicit and tacit. In consequence, there’s a risk that during validation, inadequacy of HMRs regarding their ability to mitigate a hazard remains covert. The result may be that the system is assumed to be safe, but in fact may still cause injury or death. We introduced Hazard Relation Diagrams (HRDs) as a means to integrate and graphically visualize hazard analysis results with HMRs. Herein, we also provide insights into their empirical evaluation and show that HRDs increase objectivity in rationales containing adequacy judgments.
- KonferenzbeitragModeling and Verification of Evolving Cyber-Physical Spaces(Software Engineering und Software Management 2018, 2018) Tsigkanos, Christos; Kehrer, Timo; Ghezzi, CarloIn this work, we report about recent research results on the Modeling and Verification of Evolving Cyber-Physical Spaces, published in ESEC/FSE17. We increasingly live in cyber-physical spaces – spaces that are both physical and digital, and where the two aspects are intertwined. Such spaces are highly dynamic and typically undergo continuous change. Software engineering can have a profound impact in this domain, by defining suitable modeling and specification notations as well as supporting design-time formal verification. In this paper, we present a methodology and a technical framework which support modeling of evolving cyber-physical spaces and reasoning about their spatio-temporal properties. We utilize a discrete, graph-based formalism for modeling cyber-physical spaces as well as primitives of change, giving rise to a reactive system consisting of rewriting rules with both local and global application conditions. Formal reasoning facilities are implemented adopting logic-based specification of properties and according model checking procedures, in both spatial and temporal fragments. We evaluate our approach using a case study of a disaster scenario in a smart city.
- Konferenzbeitrag5th Collaborative Workshop on Evolution and Maintenance of Long-Living Systems (EMLS)(Software Engineering und Software Management 2018, 2018) Heinrich, Robert; Jung, Reiner; Konersmann, Marco; Schmieders, Eric
- Konferenzbeitrag3rd Workshop on Continuous Software Engineering (CSE)(Software Engineering und Software Management 2018, 2018) Lichter, Horst; Krusche, Stephan; Riehle, Dirk; Steffens, Andreas
- KonferenzbeitragSoftware Engineering für E-Learning-Systeme (SEELS)(Software Engineering und Software Management 2018, 2018) Striewe, Michael; Strickroth, Sven; Lucke, UlrikeDer Workshop „Software Engineering für E-Learning-Systeme“ (SEELS) widmet sich softwaretechnischen Fragestellungen rund um die Entwicklung von E-Learning-Systemen und die Realisierung von vernetzten E-Learning-Landschaften an Schulen und Hochschulen. Ziel des Workshops ist es, aktuelle Forschungsfragen z. B. zu Schnittstellen von E-Learning-Systemen, Sicherheit in heterogenen E-Learning-Landschaften und das Management fachspezifischer Anforderungen in universellen E-Learning-Systemen zu identifizieren und zu diskutieren.
- Konferenzbeitrag1st Workshop on Innovative Software Engineering Education (ISEE)(Software Engineering und Software Management 2018, 2018) Krusche, Stephan; Kuhrmann, Marco; Schneider, KurtDue to the growing numbers of students, courses can no longer be offered in high quality without systematic approaches. Hence, this workshop aims at presenting and discussing innovative teaching approaches in software engineering education, which are highly relevant for teaching at universities, colleges, and in online courses.
- KonferenzbeitragAlgorithm Accountability, Algorithm Literacy and the hidden assumptions from algorithms(Software Engineering und Software Management 2018, 2018) Siebert, JulienOur societies are facing problems that are more and more complex so that decision making is often helped or even delegated to algorithms. Algorithmic decision making (ADM) processes are complex socio-technical systems which interact with society on a large scale. Credit scoring, automatic job candidate selection, predictive policing, or recidivism risk assessment are examples, among others, of already used ADM systems. In this talk, I will start with an overview of what is so far understood as Algorithm Accountability and Algorithm Literacy. I will then focus on algorithms that carry with them modeling assumptions (e.g., machine learning, data-mining algorithms...) and show what effects this has on the interpretation of the algorithms’ results and how we could, from a software engineering point of view, bring more explainability.