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Auflistung nach Autor:in "Dmitriev, Konstantin"

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  • Konferenzbeitrag
    Enhancing DO-178C/DO-331 Based Process-Oriented Build Tool: Integration of System Composer and Automated PIL Simulation
    (SE 2024 - Companion, 2024) Panchal, Purav; Dmitriev, Konstantin; Myschik, Stephan
    The growing utilization of software in safety-critical systems can be attributed to advancing technology and substantial interest within aerospace and space industries. However, this increased reliance on software to enhance avionic system functionality raises crucial safety questions, emphasizing the need for compliance with standards like DO-178C/DO-331. To facilitate development, a process-oriented build tool was created in MATLAB/Simulink. This tool enhances development efficiency and ensures adherence to established processes, offering benefits like modular software management, systematic artifact handling with traceability, seamless integration with various verification tools, automated model and code verification, and a well-defined design environment. Recently, two new advancements have been made to the tool, integration of System Composer for developing software architecture and automated processor-in-the-loop (PIL) verification using Trace32. This paper presents these new developments along with examples.
  • Konferenzbeitrag
    Tool Qualification Aspects in ML-Based Airborne Systems Development
    (Software Engineering 2023 Workshops, 2023) Dmitriev, Konstantin; Kaakai, Fateh; Ibrahim, Mohamad; Durak, Umut; Potter, Bill; Holzapfel, Florian
    Machine Learning (ML) technology can provide the best results in many highly complex tasks such as computer vision and natural language processing and quickly evolving further. These unique ML capabilities and apparent potential can enable the next epoch of automation in airborne systems including single pilot or even autonomous operation of large commercial aircraft. The main problems to be solved towards ML deployment in commercial aviation are safety and certification, because there are several major incompatibilities between ML development aspects and traditional design assurance practices, in particular traceability and coverage verification issues. In this paper, we study the qualification aspects of tools used for development and verification of ML-based systems (ML tools) and propose mitigation measures for some known ML verification gaps through ML tools qualification. In particular, we review the DO-330 and DO-200B tool classification approach with respect to ML-specific workflows and propose to extend the tool qualification criteria for ML data management and ML model training tools.