Extensible synthetic file servers? or: Structuring the glue between tester and system under test

We discuss a few simple scenarios of how we can design and develop a compositional synthetic file server that gives access to external processes – in particular, in the context of testing, gives access to the system under test – such that certain parts of said synthethic file server can be prepared as off-the-shelf components to which other specifically written parts can be added in a kind of plug-and-play fashion.\ud \ud The approaches only deal with the problem of accessing the system under test from the point of view of offered functionality, and compositionality, but do not consider efficiency or performance. \ud \ud The study is rather preliminary, and only very limited practical experiments have been performed

A Model-Driven Approach for Business Process Management

The Business Process Management is a common mechanism recommended by a high number of standards for the management of companies and organizations. In software companies this practice is every day more accepted and companies have to assume it, if they want to be competitive. However, the effective definition of these processes and mainly their maintenance and execution are not always easy tasks. This paper presents an approach based on the Model-Driven paradigm for Business Process Management in software companies. This solution offers a suitable mechanism that was implemented successfully in different companies with a tool case named NDTQ-Framework.Ministerio de Educación y Ciencia TIN2010-20057-C03-02Junta de Andalucía TIC-578

A path planning and path-following control framework for a general 2-trailer with a car-like tractor

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.Comment: Preprin

Iso-energy-efficiency: An approach to power-constrained parallel computation

Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making

Arcadia, a software development environment research project

The research objectives of the Arcadia project are two-fold: discovery and development of environment architecture principles and creation of novel software development tools, particularly powerful analysis tools, which will function within an environment built upon these architectural principles.Work in the architecture area is concerned with providing the framework to support integration while also supporting the often conflicting goal of extensibility. Thus, this area of research is directed toward achieving external integration by providing a consistent, uniform user interface, while still admitting customization and addition of new tools and interface functions. In an effort to also attain internal integration, research is aimed at developing mechanisms for structuring and managing the tools and data objects that populate a software development environment, while facilitating the insertion of new kinds of tools and new classes of objects.The unifying theme of work in the tools area is support for effective analysis at every stage of a software development project. Research is directed toward tools suitable for analyzing pre-implementation descriptions of software, software itself, and towards the production of testing and debugging tools. In many cases, these tools are specifically tailored for applicability to concurrent, distributed, or real-time software systems.The initial focus of Arcadia research is on creating a prototype environment, embodying the architectural principles, which supports Ada1 software development. This prototype environment is itself being developed in Ada.Arcadia is being developed by a consortium of researchers from the University of California at Irvine, the University of Colorado at Boulder, the University of Massachusetts at Amherst, TRW, Incremental Systems Corporation, and The Aerospace Corporation. This paper delineates the research objectives and describes the approaches being taken, the organization of the research endeavor, and current status of the work

Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation from expected sensory traces. These deviations in sensory traces can be used to drive the motion adaptation, and for this purpose, a feedback model is required. The feedback model maps the deviations in sensory traces to the motion plan adaptation. In this paper, we develop a general data-driven framework for learning a feedback model from demonstrations. We utilize a variant of a radial basis function network structure --with movement phases as kernel centers-- which can generally be applied to represent any feedback models for movement primitives. To demonstrate the effectiveness of our framework, we test it on the task of scraping on a tilt board. In this task, we are learning a reactive policy in the form of orientation adaptation, based on deviations of tactile sensor traces. As a proof of concept of our method, we provide evaluations on an anthropomorphic robot. A video demonstrating our approach and its results can be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on Robotics and Automation (ICRA) 201