Supporting development and management of smart office applications: a DYAMAND case study

To realize the Internet of Things (IoT) vision, tools are needed to ease the development and deployment of practical applications. Several standard bodies, companies, and ad-hoc consortia are proposing their own solution for inter-device communication. In this context, DYnamic, Adaptive MAnagement of Networks and Devices (DYAMAND) was presented in a previous publication to solve the interoperability issues introduced by the multitude of available technologies. In this paper a DYAMAND case study is presented: in cooperation with a large company, a monitoring application was developed for flexible office spaces in order to reliably reorganize an office environment and give real-time feedback on the usage of meeting rooms. Three wireless sensor technologies were investigated to be used in the pilot. The solution was deployed in a "friendly user" setting at a research institute (iMinds) prior to deployment at the large company's premises. Based on the findings of both installations, requirements for an application platform supporting development and management of smart (office) applications were listed. DYAMAND was used as the basis of the implementation. Although the local management of networked devices as provided by DYAMAND enables easier development of intelligent applications, a number of remote services discussed in this paper are needed to enable reliable and up-to-date support (of new technologies)

Model Driven Development and Analysis for Embedded Automotive Software

Mudelipõhine arendamine ja analüüs on autotööstuses kasutatav uus meetod. Seda rakendatakse mootorsõidukite tootjate poolt, kuna hajusale komponentide arendusele sobib olemuslikult spetsifitseerimine musta-kasti printsiibil. Muud põhjused tulenevad survest toota kvaliteetset tarkvara, mis vastab kõigile regulatiivsetele standarditele, kuid mis sobib autotööstuse tootjate hinnamudeliga. Mudeli kasutamisel saab komponentide kehtivuse ja standardse vastavuse kontrollida enne, kui tegelik tarkvara on autosse paigaldatud.Mudeli kasutamine tekitab ka väljakutseid, et toota lõpuks tarkvara, mis kajastab täpselt mudeli toimimist. Mudelist automaatselt genereeritud tarkvara loetakse vastuseks, kuna see on stabiilne ja pärit juba kontrollitud mudelist. Kuna tarkvara muutub autotööstuses üha olulisemaks, muutuvad tarkvara loomise mudel ja genereerimise protsess üha keerulisemaks.Käesolev töö uurib mudelipõhist autotööstuse tarkvara arendamise ja analüüsimise protsessi - teisendades MATLAB/Simulink mudel AUTOSAR mudeliks. Lõputöö raames loodud programmid teostavad analüüsi erinevate teisendussammude tarbeks. Protsessi analüüsides selgus, et teisenduse meetoodika mõjutab oluliselt mudeli esitust ning ka lõpptulemuseks saadud AUTOSAR mudeli struktuuri. Näeme erinevaid võimalikke alternatiive sellele, kuidas mudelit saab vaadata ja muuta AUTOSAR-failiks. Selles lõputöös vaadeldud iteratiivne protsess pole lõplik ja seda saab veel täiustada.Model-driven development and analysis is the state of the art method in the automotive industry. One of the reasons for its heavy utilization is coming from the black box nature of the components developed by the automotive vehicle manufacturers. The other reasons are coming from the pressure to produce quality software that complies with all regulatory standards but can fit the pricing model of automotive vehicle manufacturers.Validity and standard compliance of the components can be verified using models before the actual piece of software is deployed into an automotive vehicle. The utilization of the model also creates challenges: how to produce final software that precisely reflects how the model works. An automatically generated software from a model is deemed as an answer since it is coming from the already verified model and also will inherently retain consistency with the model. As software gets more and more critical inside an automotive vehicle, a model to create the software is getting more and more complicated and along with the automated software generation process.This thesis examines the model-driven development and analysis process for automotive software by conducting model conversion from MATLAB/Simulink model into AUTOSAR. The application developed for this thesis provides analysis and insights for every step of the conversion process. From the insights gathered along the process, it shows that the different model and transformation method creates a different model representation that affects the final structure of the AUTOSAR result. In the end, there are several possible alternatives on the way a model can be seen and transformed into an AUTOSAR file. It is also concluded that the iterative process in this project is not final and can be further improved

Evaluation and testing system for automotive LiDAR sensors

The world is facing a great technological transformation towards fully autonomous vehicles, where optimists predict that by 2030 autonomous vehicles will be sufficiently reliable, affordable, and common to displace most human driving. To cope with these trends, reliable perception systems must enable vehicles to hear and see all their surroundings, with light detection and ranging (LiDAR) sensors being a key instrument for recreating a 3D visualization of the world in real time. However, perception systems must rely on accurate measurements of the environment. Thus, these intelligent sensors must be calibrated and benchmarked before being placed on the market or assembled in a car. This article presents an Evaluation and Testing Platform for Automotive LiDAR sensors, with the main goal of testing both commercially available sensors and new sensor prototypes currently under development in Bosch Car Multimedia Portugal. The testing system can benchmark any LiDAR sensor under different conditions, recreating the expected driving environment in which such devices normally operate. To characterize and validate the sensor under test, the platform evaluates several parameters, such as the field of view (FoV), angular resolution, sensor’s range, etc., based only on the point cloud output. This project is the result of a partnership between the University of Minho and Bosch Car Multimedia Portugal.This work was supported by the European Structural and Investment Funds in the FEDER component through the Operational Competitiveness and Internationalization Programme (COM-PETE 2020), Project nº 037902, Funding Reference POCI-01-0247-FEDER-037902

Display Viewer 5:n Savutestaaminen

In this thesis, software industry testing standards were reviewed to help improve NAPCON Display Viewer 5 (DV5) test coverage. DV5 is the graphical user interface part of the NAPCON Operator Training Simulator (OTS) and has a large existing codebase with no existing testing procedures. To help improve build stability a shallow and wide smoke testing approach would be adopted to cover as much core functionality as possible, before more rigorous end-to-end testing could be implemented. The thesis study was conducted using the design science methodology. As part of this thesis, a smoke testing tool was created to test DV5 display and faceplate opening functionality. DV5 simulator configurations can consist of up to several hundreds of displays and display construction requires a lot of manual work. Using the created tool, tests were conducted in several display repositories to determine the functionality of the display sets. With only shallow automated testing, minor errors were found in most of the tested repositories, demonstrating the usefulness of automated testing with large display sets. In addition to the display testing results the display testing execution helped demonstrate some of the DV5 performance issues. Based on these findings, the smoke testing scenarios could prove useful in the future in DV5 performance testing

Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs

In support of the growing interest in quantum computing experimentation, programmers need new tools to write quantum algorithms as program code. Compared to debugging classical programs, debugging quantum programs is difficult because programmers have limited ability to probe the internal states of quantum programs; those states are difficult to interpret even when observations exist; and programmers do not yet have guidelines for what to check for when building quantum programs. In this work, we present quantum program assertions based on statistical tests on classical observations. These allow programmers to decide if a quantum program state matches its expected value in one of classical, superposition, or entangled types of states. We extend an existing quantum programming language with the ability to specify quantum assertions, which our tool then checks in a quantum program simulator. We use these assertions to debug three benchmark quantum programs in factoring, search, and chemistry. We share what types of bugs are possible, and lay out a strategy for using quantum programming patterns to place assertions and prevent bugs.Comment: In The 46th Annual International Symposium on Computer Architecture (ISCA '19). arXiv admin note: text overlap with arXiv:1811.0544

Automated Test Input Generation for Android: Are We There Yet?

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android