Combining Fault-Injection with Property-Based Testing

Vedder, Benjamin; Arts, Thomas; Vinter, Jonny; Jönsson, Magnus

doi:10.1145/2589650.2559629

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…The CopterSim library can either be used from a Graphical User Interface (GUI) to manually add and move copters, or from a program that auto-generates tests and injects faults. All fault injection is done with probes from the FaultCheck tool [10], linked to the simulator.…”

Section: Simulation and Fault Injectionmentioning

confidence: 99%

“…This way, we could randomly generate pilot control commands and inject faults during thousands of automatically generated simulations to see when a collision occurs. For fault injection, we used the FaultCheck tool [10] and for generating tests we used the Erlang QuickCheck tool [11]. When we had a sequence of pilot and fault injection commands that led to a collision, we used the shrinking feature of QuickCheck to get a shorter test sequence of commands that leads to a collision.…”

Section: Introductionmentioning

confidence: 99%

“…• We show how performance and fault tolerance can be evaluated with automatically generated tests using our previously proposed platform that utilizes fault injection and property-based testing [10]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

Vedder¹,

Eriksson²,

Skarin³

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-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 ultrasoundlocalization system. We address the challenge to accurately estimate the copter's 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 spaceconstrained GNSS-denied areas.

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Section: Simulation and Fault Injectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

Vedder¹,

Eriksson²,

Skarin³

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

Self Cite

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“…FI can e.g. be carried out early in the development process for models of hardware [2]- [4], models of software [5]- [8], source code [9], [10], and at later stages of the development process, for software deployed on target hardware [11]- [16]. When working with FI, it is common to manually create input stimuli for a System Under Test (SUT) and run it without faults, and save the state of the SUT during this run as the golden run.…”

Section: Introductionmentioning

confidence: 99%

“…When doing PBT, inputs are automatically generated and a model of the software is used to evaluate whether it fulfils its specification, whereby the golden run is generated automatically for each test sequence based on the model. Previously, we have introduced the concept of combining techniques from the areas of PBT and FI using the commercially available PBT-tool QuickCheck [18] and our FI-tool FaultCheck to test functional and non-functional requirements simultaneously [10]. By using techniques from PBT while doing FI, we can automatically generate golden runs during our experiments and test the SUT using thousands of input sequences and fault combinations.…”

Section: Introductionmentioning

confidence: 99%

Using Simulation, Fault Injection and Property-Based Testing to Evaluate Collision Avoidance of a Quadcopter System

Vedder¹,

Vinter²,

Jönsson

2015

2015 IEEE International Conference on Dependable Systems and Networks Workshops

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Abstract-In this work we use our testing platform based on FaultCheck and QuickCheck that we apply on a quadcopter simulator. We have used a hardware platform as the basis for the simulator and for deriving realistic fault models for our simulations. The quadcopters have a collision-avoidance mechanism that shall take over control when the situation becomes hazardous, steer away from the potential danger and then give control back to the pilot, thereby preventing collisions regardless of what the pilot does. We use our testing platform to randomly generate thousands of simulations with different input stimuli (using QuickCheck) for hundreds of quadcopters, while injecting faults simultaneously (using FaultCheck). This way, we can effectively adjust system parameters and enhance the collision-avoidance mechanism.

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