Daniel Skarin

GOOFI-2: A tool for experimental dependability assessment

This paper presents GOOFI-2, a comprehensive fault injection tool for experimental dependability assessment of embedded systems. The tool includes a large number of extensions and improvements over its predecessor, GOOFI. These include support for three widely used fault injection techniques, two target processors, and a variety of new features for storing, disseminating and analyzing experimental data. We report on our experiences and lessons learned from the use and development of GOOFI-2. In particular, we compare and discuss properties of three fault injection techniques: Nexus-based, exception-based and instrumentation-based injection. The comparison relies on several sets of experiments with two target processors, Freescales MPC565 and MPC5554.

Comparing and Validating Measurements of Dependability Attributes

This paper investigates sources of uncertainty in measurement results obtained using three different fault injection techniques. Two software-implemented and one test port-based technique are characterized and compared. The three techniques can be used to inject the same faults, which are defined in a shared database. Due to the uncertainties associated with the techniques, which we identify and discuss, the results of injecting a given fault may differ to some extent. The paper analyzes the results of using the three techniques to inject faults into two experimental targets: a brake-by-wire controller and a partitioning operating system. The objective of the experiments is to determine whether the results of the different techniques are metrologically compatible and, consequently, meaningful when disseminated and compared. Our observations indicate that, even though the outcome of many individual experiments is affected by uncertainties, the three techniques produce similar average results over a large number of experiments.

Visualization of Model-Implemented Fault Injection Experiments

MODIFI is a fault injection tool targeting software developed as Simulink models. In this paper, we describe three techniques for visualizing fault injection results obtained using the MODIFI tool. The first technique shows the progress of a fault injection campaign, and the outcome of individual experiments, using a 3D visualization of the fault injection campaign. The second technique, referred to as sensitivity profiling, identifies parts of a model that are sensitive for a specific fault model. The third technique shows how error propagates in a Simulink model. The sensitivity profiling and error propagation techniques are based on intuitive coloring of Simulink blocks. The three visualization techniques are demonstrated using a Brake-by-Wire system.

Towards Benchmarking of Functional Safety in the Automotive Industry

Functional safety is becoming increasingly important in the automotive industry to deal with the growing reliance on the electrical and/or electronic (E/E) systems and the associated complexities. The introduction of ISO 26262, a new standard for functional safety in road vehicles, has made it even more important to adopt a systematic approach of evaluating functional safety. However, standard assessment methods of benchmarking functional safety of automotive systems are not available as of today. This is where the BeSafe (Benchmarking of Functional Safety) project comes into the picture. BeSafe project aims to lay the foundation for benchmarking functional safety of automotive E/E systems. In this paper, we present a brief overview of the project along with the benchmark targets that we have identified as relevant for the automotive industry, assuming three abstraction layers (model, software, hardware). We then define and discuss a set of benchmark measures. Next, we propose a benchmark framework encompassing fault/error models, methods and the required tool support. This paper primarily focuses on functional safety benchmarking from the Safety Element out of Context (SEooC) viewpoint. Finally, we present some preliminary results and highlight potential future works.

Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

We present a quadcopter platform built with commodity hardware that is able to do localization in GNSS-denied areas and avoid collisions by using a novel easy-to-setup and inexpensive ultrasound-localization system. We address the challenge to accurately estimate the copters position and not hit any obstacles, including other, moving, quadcopters. The quadcopters avoid collisions by placing contours that represent risk around static and dynamic objects and acting if the risk contours overlap with ones own comfort zone. Position and velocity information is communicated between the copters to make them aware of each other. The shape and size of the risk contours are continuously updated based on the relative speed and distance to the obstacles and the current estimated localization accuracy. Thus, the collision-avoidance system is autonomous and only interferes with human or machine control of the quadcopter if the situation is hazardous. In the development of this platform we used our own simulation system using fault-injection (sensor faults, communication faults) together with automatically-generated tests to identify problematic scenarios for which the localization and risk contour parameters had to be adjusted. In the end, we were able to run thousands of simulations without any collisions, giving us confidence that also many real quadcopters can manoeuvre collision free in space-constrained GNSS-denied areas.

Shared Data from a Study of Measurement Uncertainty in Fault Injection

Experimental dependability studies usually produce an amount of data substantially greater than what can be presented in a research paper or a technical report. For this reason, authors condensate the results into more succinct forms that allow them to convey their message. Since a large amount of the original data is left unexplored, sharing it allows other teams to discover additional facts (as well as to compare the results to other studies). In a previous paper, we investigated sources of uncertainty in measurement results obtained using three different fault injection techniques. The resulting experimental data was shared in the AMBER raw data repository. This paper gives an overview of the study and makes an attempt at further exploring the shared data.