106 research outputs found
Unified devs-based platform for modelling and simulation of hybrid control systems
Recent robotic research has led to different architectural approaches that support enactment of automatically synthesized discrete event controllers from user specifications over low-level continuous variable controllers. Simulation of these hybrid control approaches to robotics can be a useful validation tool for robot users and architecture designers, but presents the key challenge of working with discrete and continuous representations of the robot, its environment and its mission plans. In this work we address this challenge showcasing a unified DEVS-based hybrid simulation platform. We model and simulate the hybrid robotic software architecture of a fixed-wing UAV, including the full stack of controllers involved: discrete, hybrid and continuous. We validate the approach experimentally on a typical UAV mapping mission and show that with our unified approach we are able to achieve simulation speed-ups up to one order of magnitude above our previous Software In The Loop simulation setup
Executable Architecture Research at Old Dominion University
Executable Architectures allow the evaluation of system architectures not only regarding their static, but also their dynamic behavior. However, the systems engineering community do not agree on a common formal specification of executable architectures. To close this gap and identify necessary elements of an executable architecture, a modeling language, and a modeling formalism is topic of ongoing PhD research. In addition, systems are generally defined and applied in an operational context to provide capabilities and enable missions. To maximize the benefits of executable architectures, a second PhD effort introduces the idea of creating an executable context in addition to the executable architecture. The results move the validation of architectures from the current information domain into the knowledge domain and improve the reliability of such validation efforts. The paper presents research and results of both doctoral research efforts and puts them into a common context of state-of-the-art of systems engineering methods supporting more agility
Synthesizing Executable Simulations from Structural Models of Component-Based Systems
Experts in robotics systems have developed substantial software tools for simulation, execution, and hardware-in-the-loop testing. Unfortunately, many of these robotics-domain software infrastructures pose challenges for a robotics expert to use, unless that robotics expert is also familiar with middleware programming, and the integration of heterogeneous simulation tools. In this paper, we describe a novel modeling language designed to bridge these two domains in an intuitive visual representation. Using this metamodel-defined modeling language, we can design and build structural models of robotics systems, and synthesize experiments from these constructed models. The restrictions implicit (and explicit) in the visual language guide modelers to build only models that can be synthesized, a "correct by construction" approach. We discuss the impact of this language with a running example of an autonomous ground vehicle, and the hundreds of configuration parameters and several simulation tools that are necessary in order to simulate this complex example
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Uncertainty representation in software models: a survey
This paper provides a comprehensive overview and analysis of research work on how uncertainty is currently represented in software models. The survey presents the definitions and current research status of different proposals for addressing uncertainty modeling and introduces a classification framework that allows to compare and classify existing proposals, analyze their current status and identify new trends. In addition, we discuss possible future research directions, opportunities and challenges.This work is partially supported by the European Commission (FEDER) and the Spanish Government under projects APOLO (US1264651), HORATIO (RTI2018-101204-B-C21), EKIPMENT-PLUS (P18-FR-2895) and COSCA (PGC2018-094905-B-I00)
Toward composing variable structure models and their interfaces: a case of intensional coupling definitions
In this thesis, we investigate a combination of traditional component-based and variable structure modeling. The focus is on a structural consistent specification of couplings in modular, hierarchical models with a variable structure. For this, we exploitintensional definitions, as known from logic, and introduce a novel intensional coupling definition, which allows a concise yet expressive specification of complex communication and interaction patterns in static as well as variable structure models, without the need to worryabout structural consistency.In der Arbeit untersuchen wir ein Zusammenbringen von klassischer komponenten-basierter und variabler Strukturmodellierung. Der Fokus liegt dabei auf der Spezifikation von strukturkonsistenten Kopplungen in modular-hierarchischen Modellen mit einer variablen Struktur. DafĂĽr nutzen wir intensionale Definitionen, wie sie aus der Logik bekannt sind, und fĂĽhren ein neuartiges Konzept von intensionalen Kopplungen ein, welches kompakte gleichzeitig ausdrucksstarke Spezifikationen von komplexen Kommunikations- und Interaktionsmuster in statischen und variablen Strukturmodellen erlaubt
Systems Modeling for novice engineers to comprehend software products better
One of the key challenges for a novice engineer in a product company is to
comprehend the product sufficiently and quickly. It can take anywhere from six
months to several years for them to attain mastery but they need to start
delivering results much before. SaaS (Software-as-a-Service) products have
sophisticated system architecture which adds to the time and effort of
understanding them. On the other hand, time available to new hires for product
understanding continues to be short and getting shorter, given the pressure to
deliver more in less time. Constructivist theory views learning as a personal
process in which the learner constructs new knowledge for themselves. Building
and refining a mental model is the key way in which they learn, similar to how
the brain operates. This paper presents an approach to improve system
comprehension process by using a system model that a) acts as a transitional
object to aid and refine the mental model of the learner, and b) captures the
current understanding of the dynamics of the software system in a way that can
be reasoned with and simulated.
We have adapted discrete systems modeling techniques and used a transition
system as a lightweight modeling language. Such a model can be used by novice
engineers during their product ramp-up phase to build a model of the software
system that captures their knowledge of the system and aid their mental model.
The paper also presents a learning approach in which the learners create and
refine these models iteratively using the available and newly uncovered
knowledge about the software system. We hypothesize that by leveraging this
modeling language and approach, novice engineers can reduce the time it takes
them to achieve desired proficiency level of system comprehension. This paper
presents early ideas on this language and approach.Comment: 5 page
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