This paper aims to solve the challenge of quantifying the perfor- mance of Hardware-in-the-Loop (HIL) computer systems used for data re-injection. The system can be represented as a multiple queue and server system that operates on a First-In, First-Out (FIFO) basis. The task at hand involves establishing tight bounds on end-to-end delay and system backlog. This is necessary to optimise buffer and pre-buffer time configurations. Network Calculus (NC) is chosen as the basic analytical framework to achieve this. In the literature, there are different techniques for estimating arrival and service curves from measurement data which can be used for NC calcu- lations. We have selected four of these methods to be applied to datasets of industrial Timestamp Logging (TL). The problem arises because these conventional methods often produce bounds that are much larger (by a factor of 1000 or more) than the measured maximum values, resulting in inefficient design of HIL system pa- rameters and inefficient resource usage. The proposed approach, called TBASCEM, introduces a reverse engineering approach based on linear NC equations for estimating the parameters of arrival and service curves. By imposing constraints on the equation variables and employing non-linear optimization, TBASCEM searches for a burst parameter estimation which derives tight global delay bounds. In addition, TBASCEM simplifies the run-time measurement pro- cess, supporting real-time data acquisition to evaluate and optimise HIL system performance, and enhancing observability to adapt the HIL configuration to new sensor data. The benefits of TBASCEM are clearly that it enables an efficient performance logging of arrival and service curve parameters and with deriving tighter bounds in HIL systems, compared to evaluated state-of-the-art methods, mak- ing TBASCEM an invaluable tool for optimising and monitoring streaming applications in non-hard-real-time environments.

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical.Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations.Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best.

We consider regression testing of safety-critical systems consisting of black-box components. This scenario is common for automotive electronic systems where testing time is expensive and should be reduced without uncontrolled reduction of reliability. This requires a methodology to select test cases from a larger test suite for which a defined quality level of the resulting regression test cycle can be provided. With this in mind, we propose a method to select test cases based on a stochastic model. We are modeling test case failure probabilities as dependent random variables, therefore already observed test results have an influence on the estimation of further test case failure probabilities. Based on an information theoretical approach, we validate the mutual differential information degree between test case failure probabilities and compute a function which returns the risk for not selecting a test case, i.e., the probability of not selecting a failing test case. Depending on the mutual differential information significant reductions of testing time can be achieved while testing reliability is preserved at a quantifiable high level. We will validate theoretically our results and will show in an industrial case study the benefits of our method.

The validation of simulation models (e.g., of electronic control units for vehicles) in industry is becoming increasingly challenging due to their growing complexity. To systematically assess the quality of such models, software metrics seem to be promising. In this paper we explore the use of software metrics and outlier analysis as a means to assess the quality of model-based software. More specifically, we investigate how results from regression analysis applied to measurement data received from size and complexity metrics can be mapped to software quality. Using the moving averages approach, models were fit to data received from over 65,000 software revisions for 71 simulation models that represent different electronic control units of real premium vehicles. Consecutive investigations using studentized deleted residuals and Cook’s Distance revealed outliers among the measurements. From these outliers we identified a subset, which provides meaningful information (anomalies) by comparing outlier scores with expert opinions. Eight engineers were interviewed separately for outlier impact on software quality. Findings were validated in consecutive workshops. The results show correlations between outliers and their impact on four of the considered quality characteristics. They also demonstrate the applicability of this approach in industry.

Many reasons lead to embedded systems getting more complex than ever. The higher integration and interconnection and the additional effort spend for safety-critical functions, require new techniques to support the testing process. Beneath using model-driven test techniques, test engineers need support to take the right decisions on test case selection and prioritization during the whole development process. Available approaches on test selection and prioritization lack in adaptability to different testing techniques. We previously presented our approach of modeling components and dependences of a system, such as black-box systems, using Component-Dependency-Models (CDMs) as shown in earlier publications. In this paper we present our tool CDM-Suite in detail and show the way it can be used by test engineers. We therefore start with the presentation of our approach and discussing related tools. The next section describes the graphical user interface (GUI) and the different ways of interaction for data generation. This point includes the introduction of metrics build upon fuzzy logic and graph analysis. At last we show how to interpret analysis results and derive knowledge for test case selection and prioritization. We sum the paper up by giving a conclusion and a presentation outline.

Embedded systems in automotive engineering are getting more and more complex due to a higher rate of integration and shared usage of sensor signals. A common solution to testing these systems is deriving test cases from models, so called model-based testing. In practice, generated test suites are typically very huge and have to be reduced by methods of regression-test selection and prioritization. In our field of application, we additionally suffer from the lack of knowledge on system internals like the source code. Therefore, our approach is based on dependences between the components of embedded systems. The model we use is derived from the system architecture and system requirements. We are using graph algorithms for selecting and prioritizing test cases to run on a certain system version. First statistical evaluations and the current implementation already have shown promising reduction of test-cases for regression testing.

Modern cars comprise tens of electronic control units, serving for various purposes like entertainment, driver's comfort or occupant protection. To connect these networked embedded systems inside the car, widespread bus systems like CAN or FlexRay are used, each one offering projecting abilities and specific limitations for certain application areas. In order to point out the suitability of a certain in-car network topology for the transmission of highly timeand safety-critical data content, we investigated effects of routing by a central gateway on the communication delay. In cooperation with the German car manufacturer Audi, we installed a prototype measurement infrastructure and collected meaningful communication data samples for up-to-date and future in-car networks. The results we obtained from the data samples allow for a sophisticated evaluation of the in-car communication network with respect to the demands of time- and safety-critical functions at an early stage of system development.

Modern cars comprise a multitude of electronic features which are implemented in tens of communicating control units. To connect these in-car embedded systems, the CAN bus offers a sustainable performance, hence it is used as a widespread communication infrastructure, even for safety critical applications. However, CAN media access is priority based and performed competitive and non-preemptive. Thus, assessing the worst case end-to-end delay is inevitable in order to provide safe and efficient operation of functions with hard real-time properties. In this paper, we use the analytical method of network calculus to determine guaranteed upper bounds for transmission delays of all CAN priorities. We demonstrate the applicability of our approach by investigating current real-life CAN communication data from the German car manufacturer Audi.

This paper is a sequel of previous work, in which we proposed a concept based on direct mobile-to-mobile (m2m) data exchange on uplink channels for distribution of large popular content in dynamic wireless environment. We showed the feasibility and performance benefit of m2m file sharing applications in terms of thereby released overall downlink capacity in UTRA-FDD systems, where multiple m2m user groups coexist in the network. A pure m2m traffic scenario was investigated and the wireless interference only among the neighboring groups was taken into account. The present paper extends the basic model by introducing a hybrid case of traffic. We consider an UMTS system scenario with coexisting conventional UMTS users and m2m file sharing participants and analyze the cross-traffic impact in order to find out the degree of m2m data transfer performance degradation due to increased uplink interference. The results indicate a slight performance degradation of m2m file sharing applications, in terms of relative losses of m2m link quality (number of corrupted data) caused by additional interference from cross-traffic. Nevertheless, no dramatic decrease in service probability and overall throughput for m2m applications in an UMTS network was observed. Furthermore, results show that by a well-designed m2m routing policy the ability to interconnect wireless devices on the partly unused uplink carrier frequencies (due to typical asymmetric uplink/downlink traffic load) might be a profitable goal and offers a promising alternative for distribution of popular content.