Representation of safety standards with semantic technologies used in industrial environments

Proceedings of: 36th International Conference on Computer Safety, Reliability, and Security, (SAFECOMP 2017). Trento, Italy, September 13-15, 2017Understanding and following safety standards with their text can be difficult. Ambiguity and inconsistency, among other issues, can easily arise. As a solution, several authors argue for the explicit representation of the standards with models, which can be created with semantic technologies such as ontologies. However, this possibility has received little attention. The few authors that have addressed it have also only dealt with a subset of safety standard aspects and have used technologies not usually applied for critical systems engineering. As a first step towards addressing these issues, this position paper presents our initial work on the representation of safety standards with Knowledge Manager, a tool used in industrial environments that exploits semantic technologies to manage domain information. The proposal also builds on prior work on the specification of safety compliance needs with a holistic generic metamodel. We describe how to use Knowledge Manager to specify the concepts and relationships of the metamodel for a given safety standard, and discuss the application and benefits of the corresponding representation.The research leading to this paper has received funding from the AMASS project (H2020-ECSEL no. 692474; Spain’s MINECO ref. PCIN-2015-262)

Automated identification and qualitative characterization of safety concerns reported in UAV software platforms

Unmanned Aerial Vehicles (UAVs) are nowadays used in a variety of applications. Given the cyber-physical nature of UAVs, software defects in these systems can cause issues with safety-critical implications. An important aspect of the lifecycle of UAV software is to minimize the possibility of harming humans or damaging properties through a continuous process of hazard identification and safety risk management. Specifically, safety-related concerns typically emerge during the operation of UAV systems, reported by end-users and developers in the form of issue reports and pull requests. However, popular UAV systems daily receive tens or hundreds of reports of varying types and quality. To help developers timely identifying and triaging safety-critical UAV issues, we (i) experiment with automated approaches (previously used for issue classification) for detecting the safety-related matters appearing in the titles and descriptions of issues and pull requests reported in UAV platforms, and (ii) propose a categorization of the main hazards and accidents discussed in such issues. Our results (i) show that shallow machine learning-based approaches can identify safety-related sentences with precision, recall, and F-measure values of about 80\%; and (ii) provide a categorization and description of the relationships between safety issue hazards and accidents

A compositional semantics for Repairable Fault Trees with general distributions

Fault Tree Analysis (FTA) is a prominent technique in industrial and scientific risk assessment. Repairable Fault Trees (RFT) enhance the classical Fault Tree (FT) model by introducing the possibility to describe complex dependent repairs of system components. Usual frameworks for analyzing FTs such as BDD, SBDD, and Markov chains fail to assess the desired properties over RFT complex models, either because these become too large, or due to cyclic behaviour introduced by dependent repairs. Simulation is another way to carry out this kind of analysis. In this paper we review the RFT model with Repair Boxes as introduced by Daniele Codetta-Raiteri. We present compositional semantics for this model in terms of Input/Output Stochastic Automata, which allows for the modelling of events occurring according to general continuous distribution. Moreover, we prove that the semantics generates (weakly) deterministic models, hence suitable for discrete event simulation, and prominently for Rare Event Simulation using the FIG tool

Agile Processes in Software Engineering and Extreme Programming: 18th International Conference, XP 2017, Cologne, Germany, May 22-26, 2017, Proceedings

agile software development; lean development; scrum; project management; software developmen

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Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years