ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps

We consider the problem of generating relevant execution traces to test rich interactive applications. Rich interactive applications, such as apps on mobile platforms, are complex stateful and often distributed systems where sufficiently exercising the app with user-interaction (UI) event sequences to expose defects is both hard and time-consuming. In particular, there is a fundamental tension between brute-force random UI exercising tools, which are fully-automated but offer low relevance, and UI test scripts, which are manual but offer high relevance. In this paper, we consider a middle way---enabling a seamless fusion of scripted and randomized UI testing. This fusion is prototyped in a testing tool called ChimpCheck for programming, generating, and executing property-based randomized test cases for Android apps. Our approach realizes this fusion by offering a high-level, embedded domain-specific language for defining custom generators of simulated user-interaction event sequences. What follows is a combinator library built on industrial strength frameworks for property-based testing (ScalaCheck) and Android testing (Android JUnit and Espresso) to implement property-based randomized testing for Android development. Driven by real, reported issues in open source Android apps, we show, through case studies, how ChimpCheck enables expressing effective testing patterns in a compact manner.Comment: 20 pages, 21 figures, Symposium on New ideas, New Paradigms, and Reflections on Programming and Software (Onward!2017

Sobolev spaces with non-Muckenhoupt weights, fractional elliptic operators, and applications

We propose a new variational model in weighted Sobolev spaces with non-standard weights and applications to image processing. We show that these weights are, in general, not of Muckenhoupt type and therefore the classical analysis tools may not apply. For special cases of the weights, the resulting variational problem is known to be equivalent to the fractional Poisson problem. The trace space for the weighted Sobolev space is identified to be embedded in a weighted $L^2$ space. We propose a finite element scheme to solve the Euler-Lagrange equations, and for the image denoising application we propose an algorithm to identify the unknown weights. The approach is illustrated on several test problems and it yields better results when compared to the existing total variation techniques

Test-driven development of embedded control systems: application in an automotive collision prevention system

With test-driven development (TDD) new code is not written until an automated test has failed, and duplications of functions, tests, or simply code fragments are always removed. TDD can lead to a better design and a higher quality of the developed system, but to date it has mainly been applied to the development of traditional software systems such as payroll applications. This thesis describes the novel application of TDD to the development of embedded control systems using an automotive safety system for preventing collisions as an example. The basic prerequisite for test-driven development is the availability of an automated testing framework as tests are executed very often. Such testing frameworks have been developed for nearly all programming languages, but not for the graphical, signal driven language Simulink. Simulink is commonly used in the automotive industry and can be considered as state-of-the-art for the design and development of embedded control systems in the automotive, aerospace and other industries. The thesis therefore introduces a novel automated testing framework for Simulink. This framework forms the basis for the test-driven development process by integrating the analysis, design and testing of embedded control systems into this process. The thesis then shows the application of TDD to a collision prevention system. The system architecture is derived from the requirements of the system and four software components are identified, which represent problems of particular areas for the realisation of control systems, i.e. logical combinations, experimental problems, mathematical algorithms, and control theory. For each of these problems, a concept to systematically derive test cases from the requirements is presented. Moreover two conventional approaches to design the controller are introduced and compared in terms of their stability and performance. The effectiveness of the collision prevention system is assessed in trials on a driving simulator. These trials show that the system leads to a significant reduction of the accident rate for rear-end collisions. In addition, experiments with prototype vehicles on test tracks and field tests are presented to verify the system’s functional requirements within a system testing approach. Finally, the new test-driven development process for embedded control systems is evaluated in comparison to traditional development processes

Sobolev spaces with non-Muckenhoupt weights, fractional elliptic operators, and applications

We propose a new variational model in weighted Sobolev spaces with non-standard weights and applications to image processing. We show that these weights are, in general, not of Muckenhoupt type and therefore the classical analysis tools may not apply. For special cases of the weights, the resulting variational problem is known to be equivalent to the fractional Poisson problem. The trace space for the weighted Sobolev space is identified to be embedded in a weighted L2 space. We propose a finite element scheme to solve the Euler-Lagrange equations, and for the image denoising application we propose an algorithm to identify the unknown weights. The approach is illustrated on several test problems and it yields better results when compared to the existing total variation techniques

CloudScout: A deep neural network for on-board cloud detection on hyperspectral images

The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. On the test set we achieve 92% of accuracy with 1% of False Positives (FP). The Phisat-1 mission will start in 2020 and will operate for about 6 months. It represents the first in-orbit demonstration of Deep Neural Network (DNN) for data processing on the edge. The innovation aspect of our work concerns not only cloud detection but in general low power, low latency, and embedded applications. Our work should enable a new era of edge applications and enhance remote sensing applications directly on-board satellite

TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs

Dynamic and Adaptive Training for Enhanced Aviation Knowledge Transfer and Retention

The world of aviation is rapidly evolving through increased automation on the flight deck, new air traffic control tools and procedures, and expanded applications of Unmanned Aircraft Systems (UAS). These enhancements may increase training requirements on operational personnel and potentially introduce the opportunity for the degradation of knowledge, skills, and abilities (KSAs) that are not routinely applied. The resources required for simulator-based training results in using computer-based training (CBT) for many infrequently used KSAs. Field studies and academic literature consistently show that users find this training boring, easily forgettable, and are perceived as “check the box” training. Furthermore, most CBT is standardized and does not adjust to the trainee’s learning preferences or existing familiarity with the content. In this presentation, we describe a new approach to training delivery. Our Dynamic Adaptive Training & Evaluation System (DATES) approach is designed to increase engagement, long-term retention, and decrease training time by adjusting to trainees’ learning preferences and proficiency levels. DATES presents training material in different formats and orders based on trainee performance on embedded assessments and real-time analysis of user engagement. The system starts by administering a pre-test on the topic and then presents a random order of short, individual modules in visual (video-based), verbal (text-based), or scenario-based formats. Based on response time on embedded assessments, question response accuracy, and proprietary user-engagement metrics, the system’s algorithm will present tailored training styles and modalities to maximize impact for individual trainees. We will discuss the key considerations and implementation recommendations

On-Line Dependability Enhancement of Multiprocessor SoCs by Resource Management

This paper describes a new approach towards dependable design of homogeneous multi-processor SoCs in an example satellite-navigation application. First, the NoC dependability is functionally verified via embedded software. Then the Xentium processor tiles are periodically verified via on-line self-testing techniques, by using a new IIP Dependability Manager. Based on the Dependability Manager results, faulty tiles are electronically excluded and replaced by fault-free spare tiles via on-line resource management. This integrated approach enables fast electronic fault detection/diagnosis and repair, and hence a high system availability. The dependability application runs in parallel with the actual application, resulting in a very dependable system. All parts have been verified by simulation

Implementation of packaged integrated antenna with embedded front end for Bluetooth applications

The design, integration and realization of system in enhanced package approach towards fully functional system level integration by using a compact Bluetooth USB dongle as the demonstrator is presented here. The integration was done on FR4 substrates, which is totally compatible with today’s printed circuit board manufacturing capability. A commercially available Bluetooth integrated chip was chosen as the chipset of our demonstrator, and a package integrated antenna together with an embedded front end completes the system in package integration. The front end developed here is based on an embedded meander line combline filter and an embedded transformer balun. The filter has a 35% area reduction when compared with the classical combline filter and similar performance. The balun has the coils distributed on three layers that minimized the board area needed it and optimizes the performances. The proposed packaged integrated antenna approach is successfully demonstrated here and the new module shows excellent performance when compared with a commercial solution, surpassing the normal Bluetooth class II dongle range which is up to 10 m and increasing the module range up to 120 m without an extra power amplifier

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India