Open Access

Context: Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications. Objective: This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization. Method: A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established. Results: Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed. Conclusion: It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps’ aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.

In the field of software engineering, graph-based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.

As humans, we tend to use models to describe reality. Modeling languages provide the formal frameworks for creating such models. Usually, the graphical design of individual model elements is based on subjective decisions; their suitability is determined at most by the prevalence of the modeling language. With other words: there is no objective way to compare different designs of model elements. The present paper addresses this issue: it introduces a systematic approach for evaluating the elements of graph-based modeling languages comprising 14 criteria – derived from standards, usability analyses, or the design theories ‘Physics of Notations’ and ‘Cognitive Dimensions of Notations’. The criteria come with measurement procedures and evaluation schemes based on reasoning, eye tracking, and questioning. The developed approach is demonstrated with a specific use case: three distinct sets of node elements for causal graphs are evaluated in an eye tracking study with 41 subjects.

This study examines how Klingsieck’s LIST-K questionnaire [22] can be shortened and adapted to the requirements of an online learning management system. In a study with 213 participants, the questionnaire is subjected to an exploitative factor analysis. In a next step, the results are evaluated in terms of their reliability. This process creates a modified factor structure for the LIST-K, comprising a total of eight factors. The reliability of the modified questionnaire is at an α of .770. The shortened version of the LIST-K questionnaire is currently being used on an experimental basis in different courses.

This study investigates the impact of eye movement modeling examples in Software Engineering education. Software Engineering is a highly visual domain. The daily tasks of a software engineer (e.g., formulating requirements, creating UML diagrams, or conducting a code review) require in many cases the use of certain visual strategies. Although these strategies can be found for experts, it has been observed in different eye tracking studies that students have difficulties in learning and applying them. To familiarize students with these visual strategies and to provide them with a better understanding for the cognitive processes involved, a total of seven eye movement modeling examples was created. The seven eye movement modeling examples cover relevant parts of an introductory Software Engineering lecture; they are focused on typical situations in which visual strategies are applied. The results of a questionnaire-based evaluation shows that students consider the eye movement modeling examples as useful, feel supported in their learning process, and would like to see more use of them in the Software Engineering lecture. Furthermore, the students suggested that eye movement modeling examples should also be used in other lectures.

This paper assesses the relation between personality, demographics, and learning style. Hence, data is collected from 200 participants using 1) the BFI-10 to obtain the participant’s expression of personality traits according to the five-factor model, 2) the ILS to determine the participant’s learning style according to Felder and Silverman, and 3) a demographic questionnaire. From the obtained data, we train and evaluate a Bayesian network. Using Bayesian statistics, we show that age and gender slightly influence personality and that demographics as well as personality have at least a minor effect on learning styles. We also discuss the limitations and future work of the presented approach.

In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.

This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).

Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students – based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms. Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students. In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire. In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates.

Today's cyberphysical systems are increasingly prone to misuse. To secure existing and future software systems, introducing concepts of IT-Security and Secure Software Engineering (SecSE) in Software Engineering (SE) courses is essential for academic education of future software engineers. This is not only important for computer science students, but also for engineering students studying topics of computing and SE. However, only little research exists on integrating these topics into traditional SE courses, especially for engineering students in non-computer science majors. To narrow this gap, this paper contributes with the design and evaluation of an exercise on modeling misuse cases alongside use cases, based on the inductive teaching method problem-based learning (PBL). The exercise is part of an educational design research investigating which learning content and teaching methods are suitable for integrating IT-Security and SecSE topics into traditional SE education of engineering students to convey factual knowledge as well as raise awareness and interest for both topics during software development. We present the integration of the exercise design into a traditional SE course for engineering students and its evaluation to examine its suitability. We evaluated the exercise design regarding the suitability of the design components, the learning content of misuse cases and the intended learning goals as well as its impact on students' motivation, and their interest in IT-security. The paper then presents indications on the feasibility and success of the exercise design for teaching misuse cases to engineering students and sparking their interest in IT-Security.

Lately, parallel task models have received much attention in the development of real-time multiprocessor systems, as they allow highly compute-intensive tasks to have shorter deadlines which is very much required in modern reactive systems. However, missing modularity and portability can make parallel programming a cumbersome endeavor. As a consequence, compute-intensive sectors in the desktop and server segment have relied on parallelism frameworks such as Intel Threading Building Blocks, Cilk and OpenMP. These parallelism frameworks, however, are optimized for decent average case performance and consequently, do not meet the strict requirements imposed by real-time systems. In this paper, we present a proof-of-concept parallelism framework which was implemented in particular for soft real-time systems and having tight timing and safety requirements of such critical systems in mind. The proposed runtime system implements static memory allocation in a work-stealing environment that conforms to the strict space and tight probabilistic time bounds of work-stealing schedulers. Furthermore, we evaluate the performance of this framework by conducting multiprogrammed benchmarks on a real-time embedded multicore architecture.

The modular addition is used as a non-linear operation in ARX ciphers because it achieves the requirement of introducing non-linearity in a cryptographic primitive while only taking one clock cycle to execute on most modern architectures. This makes ARX ciphers especially fast in software implementations, but comes at the cost of making it harder to protect against side-channel information leakages using Boolean masking: the best known 2-shares masked adder for ARM Thumb micro-controllers takes 83 instructions to add two 32-bit numbers together. Our approach is to operate in bitsliced mode, performing 32 additions in parallel on a 32-bit microcontroller. We show that, even after taking into account the cost of bitslicing before and after the encryption, it is possible to achieve a higher throughput on the tested ciphers (CRAX and ChaCha20) when operating in bitsliced mode. Furthermore, we prove that no first-order information leakage is happening in either simulated power traces and power traces acquired from real hardware, after sufficient countermeasures are put into place to guard against pipeline leakages.

The discipline of causal inference uses so-called causal graphs to model cause and effect relations of random variables. As those graphs only encode a relation structure there is no hard rule concerning their alignment. The present paper presents a study with the aim of working out the optimal alignment of causal graphs with respect to comprehensibility and interestingness. In addition, the study examines whether the central gestalt principles of psychology apply for causal graphs. Data from 29 participants is acquired by triangulating eye tracking with a questionnaire. The results of the study suggest that causal graphs should be aligned downwards. Moreover, the gestalt principles proximity, similarity and closure are shown to hold true for causal graphs.

The paper presents a penetration testing framework for automotive IT security education and evaluates its realization. The automotive sector is changing due to automated driving functions, connected vehicles, and electric vehicles. This development also creates new and more critical vulnerabilities. This paper addresses a possible countermeasure, automotive IT security education. Some existing solutions are evaluated and compared with the created Automotive Penetration Testing Education Platform (APTEP) framework. In addition, the APTEP architecture is described. It consists of three layers representing different attack points of a vehicle. The realization of the APTEP is a hardware case and a virtual platform referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theories, such as constructivism and problem-based/challenge-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH) Regensburg experienced the challenges as part of a elective subject. In an online survey evaluated in this paper, they gave positive feedback. Also, a part of the evaluation is the mapping of the ANSKo and the maturity levels in the Software Assurance Maturity Model (SAMM) practice Education & Guidance as well as the SAMM practice Security Testing. The scientific contribution of this paper is to present an APTEP, a corresponding learning concept and an evaluation method.

Development and verification of modern, dependable automotive systems require appropriate modelling approaches. Classic automotive safety is described by the normative regulations ISO 26262, its relative ISO/PAS 21448, and their respective methodologies. In recent publications, an emerging demand to combine environmental influences, machine learning, or reasoning under uncertainty with standard-compliant analysis techniques can be noticed. Therefore, adapting established methods like FTA and proper tool support is necessary. We argue that Bayesian Networks (BNs) can be used as a central component to address and merge these demands. In this paper, we present our Open-Source Python package BayesianSafety. First, we review how BNs relate to data-driven methods, model-to-model transformations, and causal reasoning. Together with FTA and ETA, these models form the core functionality of our software. After describing currently implemented features and possibilities of combining individual modelling approaches, we provide an informal view of the tool’s architecture and of the resulting software ecosystem. By comparing selected publicly available safety and reliability analysis libraries, we outline that many relevant methodologies yield specialized implementations. Finally, we show that there is a demand for a flexible, unifying analysis tool that allows researching system safety by using multi-model and multi-domain approaches.

In this work, we present our benchmarking results for the ten finalist ciphers of the Lightweight Cryptography (LWC) project initiated by National Institute of Standards and Technology (NIST). We evaluate the speed and code size of various software implementations on five different platforms featuring four different architectures. Moreover, we benchmark the dynamic memory utilization of the remaining NIST LWC algorithms on one 32-bit ARM controller. We describe our test cases and methodology and provide some information regarding the design and properties of the finalists before showing and discussing our results. Altogether, we evaluated almost 300 implementations of the 3rd round candidates and pick the most appropriate and best (primary) implementation of each cipher for our comparisons. We include a variant of AES-GCM in our benchmarking in order to be able to compare the state-of-the-art to the novel LWC ciphers. Our research gives an overview over the performance of the latest software implementations of the NIST LWC finalists and shows under which circumstances which candidate is performing the best in our individual test cases. Additionally, we make all benchmarking results, the code for our test framework and every tested implementation available to the public to ensure a transparent testing process.

Diagnostic protocols in automotive systems can offer a huge attack surface with devastating impacts if vulnerabilities are present. This paper shows the application of active automata learning techniques for reverse engineering system state machines of automotive systems. The developed black-box testing strategy is based on diagnostic protocol communication. Through this approach, it is possible to automatically investigate a highly increased attack surface. Based on a new metric, introduced in this paper, we are able to rate the possible attack surface of an entire vehicle or a single Electronic Control Unit (ECU). A novel attack surface metric allows comparisons of different ECUs from different Original Equipment Manufacturers (OEMs), even between different diagnostic protocols. Additionally, we demonstrate the analysis capabilities of our graph-based model to evaluate an ECUs possible attack surface over a lifetime.

Modern high-end embedded systems nowadays have to process enormous amounts of data. In order to speed up the computations and fully exploit the resources of the underlying hardware architectures, software developers can avail parallelism frameworks such as Intel Threading Building Blocks or compiler extensions as OpenMP. They ease the development of parallel applications by providing interfaces for common parallel design patterns and by internally distributing the work among the workers of a thread pool. However, such frameworks and compiler extensions do not yet support the stringent timing requirements of real-time systems and therefore, an adaption of their computation model to the sector of real-time systems needs to be conducted. In this paper, we address the problem of scheduling parallel real-time directed acyclic graphs tasks on multiprocessor architectures where the subtasks are dispatched among and executed by the workers of a thread pool. In contrast to existing work in the state-of-the-art, we limit the maximum parallelism of real-time tasks not by the number of processors in the system, but by the number of worker threads used in the thread pool of each real-time application. For this model, we derive a worst-case response time analysis for task sets scheduled by a preemptive global fixed-priority scheduler. In order to evaluate the performance of our response time analysis, we further perform schedulability tests on generated task sets and compare the results to existing feasibility analyses in the current state-of-the-art.

In recent years, our society faced a massive interconnection of computer-based everyday objects, which opens these items for cyber-attacks. Dependent on the physical capabilities, successful attacks can vary from data exposure or a loss of functionality to a threat to life and limb. Connected- and autonomous vehicles are extremely safety-critical systems with a huge damage potential. This global trend, together with existing and upcoming regulations (ISO 270xx, ISO 21434, UNECE WP.29, UNECE R155), and the lack of qualified professionals create a tension field for the entire automotive industry. Hence, new education concepts for engineers of safety-critical and connected systems are necessary to secure our daily and future systems against cyber-attacks and raise awareness and knowledge of the topic of IT-Security. Existing automotive security education systems have one common problem: All systems are hardware-based and therefore have very steep learning curves for beginners. Hardware-based systems, in general, are expensive in their initial costs, require regular maintenance, and add diverse operational difficulties independent of the aspired education goal. Additionally, the global pandemic increased the necessity of virtual education concepts for security training in cyber-physical systems. Therefore, we present a novel concept for the education of cyber-security professionals for automotive systems based on discovery and problem-based learning in a virtual learning environment (VLE). Our concept contains individual exercises focusing on the topics of vulnerabilities and attacks in automotive networks and systems. Each exercise relates those topics to the corresponding security goals and countermeasures for mitigation. The learners work collaboratively in a self-contained manner within the VLE to acquire the necessary information to answer questions or find a solution to the given problem. To consider the heterogeneous background (e.g. knowledge, experience, preconceptions) of the learners, the topics can be presented in different difficulties, enabling an adaptable learning environment and different learning trajectories within the exercises. The concept is based on a VLE, consisting of automotive networks and components, which simulate the behavior of a vehicle. This environment provides a hands-on, "real-life" scenario, which allows discovery and problem-based learning in a realistic, but cheap and scalable education environment. Furthermore, virtualization removes common difficulties, always present in training on real hardware. This aims to decrease complexity, prevent learning obstacles related to hardware handling, and enables a location-independent learning environment. The target group of our education concept is Bachelor and Master students of computer science, engineering (e.g. electrical engineering, mechatronics), or similar studies, and (experienced) engineers from the industry. In summary, our publication contains two contributions. We present an adaptable virtual learning environment for automotive security education, combined with an educational concept based on discovery and problem-based learning techniques. The goals of our concept are the education of cyber-security professionals for safety-critical, connected automotive systems and the support of life-long learning reaching from academic education to training in the industry.

Car manufacturers define proprietary protocols to be used inside their vehicular networks, which are kept an industrial secret, therefore impeding independent researchers from extracting information from these networks. This article describes a statistical and a neural network approach that allows reverse engineering proprietary controller area network (CAN)-protocols assuming they were designed using the data base CAN (DBC) file format. The proposed algorithms are tested with CAN traces taken from a real car. We show that our approaches can correctly reverse engineer CAN messages in an automated manner.

Aufgrund der immer weiter anwachsenden Vernetzung der Stromnetze wird die Kommunikation zwischen der Leitstelle des Energieversorgers und den Infrastrukturkomponenten innerhalb eines Umspannwerks immer bedeutsamer. Dabei werden sowohl Steuerbefehle als auch Daten für Überwachungsfunktionen übertragen. In den aktuellen Netzwerkarchitekturen findet diese Kommunikation ohne eine kryptografische Absicherung statt, was einen Angriffspunkt für gezielte Attacken und damit eine potenzielle Gefährdung der Energieversorgung darstellt. Um solchen Angriffen in Zukunft entgegenzuwirken, wird das ES³M-Sicherheitsmodul entwickelt. Dieses soll in das Netzwerk zwischen den beiden Kommunikationspartnern eingesetzt werden und so den Datenverkehr absichern. Mithilfe einer Bedrohungsanalyse wurden Anforderungen abgeleitet, die neben kryptografischen Maßnahmen auch Themen wie funktionale Sicherheit und Langlebigkeit umfassen. Um diese zu erfüllen, wurde eine spezielle Systemarchitektur auf Basis einer Aufgabenteilung entworfen. Diese Architektur und korrespondierende Designentscheidungen werden präsentiert.

The number of IoT devices in SCADA and ICS systems is rising quickly, especially in the domain of critical infrastructures. But these kinds of systems are performing mission critical tasks like controlling devices in industrial facilities or substations in the smart grid. Therefore, they are subject to a lot of regulatory standards. Yet, to provide remote access over the internet, special architectures are developed to integrate a network interface into these devices without inferring with the actual functionality. However, these architectures either lack security measures against cyber-attacks or do not offer the necessary performance for time-critical communication interfaces. To solve that, an architecture consisting of three units is introduced in this paper to provide a network interface with extensive security measures and a high performance. The main feature is the isolation of the cryptographic functionality onto an additional MCU. After proposing the basic concept, the paper presents many implementation details. Based on the current state of implementation, a concept validation of the realized architecture is described.

In the age of the Internet of Things, it is becoming increasingly important to integrate knowledge about the development of secure systems (Secure Software Engineering) into academic teaching. However, teaching IT Security and Secure Software Engineering to non-computer scientists is rare. Therefore, we focus our research on the integration of IT Security into software engineering education of non-computer scientists, particularly electrical engineers, by means of inductive teaching- and learning-arrangements. After collecting students' preconceptions of IT Security and Secure Software Engineering in prior work, this paper now contributes with a first mapping of these preconceptions with corresponding learning content as well as suitable inductive teaching methods to be able to create new lecture and exercise units and improve academic learning and teaching in both areas.

The National Institute of Standards and Technology (NIST) started the standardization process for lightweight cryptography algorithms in 2018. By the end of the first round, 32 submissions have been selected as 2nd round candidates. NIST allowed designers of 2nd round submissions to provide small updates on both their specifications and implementation packages. In this work, we introduce a benchmarking framework for evaluating the performance of NIST Lightweight Cryptography (LWC) candidates on embedded platforms. We show the features and application of the framework and explain its design rationale. Moreover, we provide information on how we aim to present up-to-date performance figures throughout the NIST LWC competition. In this paper, we present an excerpt of our software benchmarking results regarding speed and memory requirements of selected ciphers. All up-to-date results, including benchmarking different test cases for multiple variants of each 2nd round algorithm on five different microcontrollers, are periodically published to a public website. While initially only the reference implementations were available, the ability of automatically testing the performance of the candidate algorithms on multiple platforms becomes especially relevant as more optimized implementations are developed. Finally, we show how the framework can be extended in different directions: support for more target platforms can be easily added, different kinds of algorithms can be tested, and other test metrics can be acquired. The focus of this paper should rather lay on the framework design and testing methodology than on the current results, especially for reference code.

Software process models are important in software projects in order to give the work of a project guidelines or a framework. However, teaching process models in higher education seems to be quite challenging. This has to do with the fact that undergraduates have no experience with projects in which process models are used. The theoretical mediation of process models is initially on a very abstract level. For this reason, we chose to combine two didactic approaches, namely problem-based learning and project work. Various traditional plan-driven process models have been expanded in courses in Software Engineering with agile process models. The Scrum Framework is the focus of consideration of this paper. Three Universities of Applied Sciences which cooperate in the EVELIN project focused on Scrum as a process model and integrated it into their teaching. Since the respective concepts of implementation differ, they should be presented and compared in this article to presents some practice approaches. The goal of this presentation of is a uniform evaluation in order to obtain insights from different perspectives. This comparison can draw conclusions for possible necessary improvements of the respective concepts.

UML (Unified Modeling Language) is the current de facto as well as de jure standard (ISO/IEC 19505:2012) notation to visualize models in software development. UML provides essential guidelines and rules to visualize and understand complex software systems. This is the reason why it has become part of curricula for software engineering courses at many universities worldwide. It is well known, however, that UML is hard to grasp for novices, mainly due to its complexity. In order to tackle the problem of teaching UML to novice students appropriately, it is inevitable to understand their needs and problems much better than we do now. This paper presents empirical insights into students' problems when developing common UML diagrams. Identified problems are generalized, giving rise to a problem catalogue that is derived from our empirical findings, thus establishing a basis for addressing these problems through focused learning arrangements.

We briefly describe a tutorial on the application of Eye-Tracking technology for Software Engineering Education. We will showcase our setup of a large-scale Eye-Tracking-Classroom and its usage for real-time improvement of traditional learning scenarios in Software Engineering Education. We will focus on the integration of gaze data into modern integrated development environments (IDEs) and demonstrate a complete workflow for its usage in co-located multi-user Eye-Tracking-Environments.

Over the past few years, ontology merging, and ontology semantic alignment has gained significant interest as research topics in automotive application domain for finding solutions to semantic data heterogeneity. To accomplish the complex and novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication), etc. the automotive applications involve collaborations of several platform-specific data from heterogeneous enterprises component frameworks and consequently there has been increase in data interoperability issues. At the application component level, data interoperability relies on the semantic alignment or mapping between the various component framework interfaces data models represented as XML schemas (XSD). With the XML schemas being the preferred standard for the interface description exchange between most of the automotive application domain components, however, the data interoperability between the semantically equivalent but structurally different data constructs of multiple heterogeneous XSDs stands as a challenge in the absence of an ontology-based approach. To confront this crucial requirement for data interoperability and to increase in effect the reuse of existing components through their interfaces, we propose an approach to semantically map the various component framework interface data models when expressed as ontology schemas, based on the exploration of semantic synergies. The transformation between XSD and RDF (Resource Description Framework) schema representations and the use of queries over the ontology schemas for semantic mapping are demonstrated including a real-world case study.

The enormous amounts of data modern real-time systems have to process lead to expensive, long-lasting calculations. In order to manage those computations in a timely manner, parallel task models have gained a lot of popularity lately. However, parallel programming can be very cumbersome and verbose. Other computationally intensive sectors have dealt with parallel computing for decades and have accumulated their experience in the development of parallel frameworks. Examples of well known parallel runtime systems are OpenMP, Intel Threading Building Blocks (TBB) and Microsoft Parallel Pattern Library (PPL). These runtime systems allow developers to enhance parallelism in their applications in a straightforward fashion. However, those parallel frameworks and the patternbased interfaces they provide might not be easily applicable in real-time systems. In this paper, we investigate the use of parallel programming frameworks in time-critical systems. On that account, we discuss considerations for the design of real-time applications that make use of such parallel runtime systems. Furthermore, we evaluate three library-based frameworks from different computing sectors, namely Intel Threading Building Blocks, Embedded Multicore Building Blocks (EMBB) and High Performance ParalleX (HPX), by conducting benchmarks of various parallel algorithms on an embedded multicore architecture.

Although there has been much speculation about the potential of Augmented Reality (AR) in teaching for learning material, there is a significant lack of empirical proof about its effectiveness and implementation in higher education. We describe a software to integrate AR using the Microsoft Hololens into UML (Unified Modeling Language) teaching. Its user interface is laid out to overcome problems of existing software. We discuss the design of the tool and report a first evaluation study. The study is based upon effectiveness as a metric for students performance and components of motivation. The study was designed as control group experiment with two groups. The experimental group had to solve tasks with the help of the AR modeling tool and the control group used a classic PC software. We identified tendencies that participants of the experimental group showed more motivation than the control group. Both groups performed equally well

We investigated the potential of augmented reality (AR) to enable visualization of abstract concepts and present the first iteration of a teaching experiment that evaluates the use of AR as support for abstraction skills. Students were confronted with the task to present and explain information to different groups of stakeholders at the example of a coffee machine. Results show that students find it helpful to have a visual app-prototype and especially one that can be disassembled in different levels. The main goal was to sensitize students for the need to think about and to abstract information for certain roles and perspectives.

As the world is getting more connected, the demands of services in automotive industry are increasing with the requirements such as IoT (Internet of Things) in cars, automated driving, etc. Eventually, the automotive industry has evolved to a complex network of servi ces, where each organization depends on the other organizations, to satisfy its service requirements in different phases of the vehicle life cycle. Because of these heterogeneous and complex development environments, most of the vehicle component interface models need to be specified in various manifest ations to satisfy the semantic and syntactic requirements, specific to different application development environments or frameworks. This paperdescribes an approach to semantic analysis of components interfaces description models of heterogeneous frameworks, that are used for vehicle applications. The proposed approach intends to ensure that interface description models of different service-based vehicle frameworks can be compared, correlated and re-used based on semantic synergies, across different vehicle platforms, development environments and organizations. The approach to semantic synergy exploration could further provide the knowledge base for the increase in interoperability, overall efficiency and development of an automotive domain specific general software solutions, by facilitating coexistence of components of heterogeneous frameworks in the same high-performance ECU for future vehicle software.

New computing-intensive applications such as assisted or highly automated driving are rapidly expanding the domain of safety-critical embedded systems, driven by the vision of the driverless car. This development makes it necessary to use high performance multi-core systems which are commercially available and provide more parallelism in terms of redundant execution units, however, at the cost of being less reliable. With the continuous down-scaling of semiconductor technology, computing hardware exhibits an increasing vulnerability against random hardware faults. Since these high-performance controller provide less or no hardware redundancy to ensure a safe execution of the application, software-only fault tolerance approaches are under current investigation. Our Scalable Software Support for Dependable Embedded Systems (S3DES) approach achieves fault tolerance by utilizing software-based triple modular redundancy for computational and optimized arithmetic encoded voter processes to ensure fault detection and error handling on application level. In S3DES voters are replicated to allow the compensation of voting failures. However, new challenges with regard to error propagation and multiple voting result outputs are introduced by this extension. We describe how mutual voter monitoring and threshold value checks could be used to establish a hierarchy among the replicated voters without re-introducing a reliability bottleneck in the sense of a single point of failure and resolve the aforementioned challenges.

Sharing data across multiple tasks in multiprocessor systems has intensively been studied in the past decades. Various synchronization protocols, the most well-known being the Priority Inheritance Protocol or the Priority Ceiling Protocol, have been established and analyzed so that blocking times of tasks waiting to access a shared resource can be upper bounded. To the best of our knowledge, all of these protocols share one commonality: Tasks that want to enter a critical section, that is already being executed by another task, immediately get blocked. In this paper, we introduce the Asynchronous Priority Ceiling Protocol (A-PCP), which makes use of aperiodic servers to execute the critical sections asynchronously, while the calling task can continue its work on non-critical section code. For this protocol, we provide a worst-case response time analysis of the asynchronous computations, as well as necessary and sufficient conditions for a feasibility analysis of a set of periodic tasks using the proposed synchronization model on a system that preemptively schedules the tasks under the rate-monotonic priority assignment.

This paper introduces a custom framework for benchmarking software implementations from the National Institute of Standards and Technology (NIST) Lightweight Cryptography (LWC) project on embedded devices. We present the design and core functions of the framework and apply it to various NIST LWC authenticated encryption with associated data (AEAD) ciphers. Altogether, we evaluate the speed of 213 submitted algorithm vari-ants on four different microcontroller units (MCUs), including 32 bit ARM and 8 bit AVR architectures. To allow a more meaningful comparison, we also conduct code size tests on all four boards and RAM utilization tests on one test platform.

The used technology in the power system is subject of great change. Through the use of smart devices, the systems in the power grid get interconnected between each other and remote networks. Especially the remote access to the critical smart grid environment involves new challenges in the area of security. To ensure the transmitted data and the access to the system is secured, cryptographic mechanisms have to be implemented. One critical part in this task is the management of cryptographic keys. This paper explains and defines a set of basic requirements for cryptographic key management in the smart grid. These requirements are derived from challenges present in common corporate environments. Then, basic approaches in the field of key management are stated and evaluated for the applicability in the power system. Different protocols for the implementation of key management strategies are shown and an assessment regarding their suitability for the defined requirements is conducted.

Die am weitesten verbreiteten autonomen Systeme der Zukunft sind aller Voraussicht nach intelligente Fahrzeuge, welche selbständig im Straßenverkehr navigieren und mit der Umgebung interagieren. Diese neuen Funktionen erfordern den Einsatz von performanten Mehrkernprozessoren sowie von komplexen (POSIX-kompatiblen) Betriebssystemen. Gleichzeitig erfordert der Einsatz im Automobil hohe funktionale Sicherheit (ASIL-Level), was unter anderem robuste Echtzeiteigenschaften der verwendeten Hard- und Software voraussetzt. Den Echtzeiteigenschaften steht die erhöhte Komplexität mit neuen Quellen für nichtdeterministische Latenzen gegenüber. In diesem Paper präsentieren wir eine Übersicht über diese neuen Einflussfaktoren, und vermessen anschließend Containerlaufzeitumgebungen und deren Latenzverhalten. Wir zeigen dabei, das Netzwerkbrücken unter Last erheblichen Einfluss (Faktor 4–5) auf die Netzwerklatenz ausüben können.

With the increase in demand of services in the automotive industry, automotive enterprises prefer to collaborate with other qualified cross-domain partners to provide complex automotive functions (or services), such as autonomous driving, OTA (Over The Air) vehicle update, V2X (Vehicle-to-Vehicle communication), etc. One key element in cross-domain enterprise collaboration is the mutual agreement between interfaces of software components. In this context, model-to-model mappings of software component models of heterogeneous frameworks for automotive services and to explore the synergies in their interface semantics, have become an essential factor in improving the interoperability among the automotive and other cross-domain enterprises. However, one of the challenges in achieving cross-domain component interface model-to-model mappings at an application level lies in detecting the interface semantics and the semantic relations that are conveyed in different component models in different frameworks. This paper addresses this challenge using a Model Driven Architecture (MDA) based analytical approach to explore interface semantic synergies in the cross-domain component meta-models that are used for automotive services. The approach applies manual semantic checking measurements at an application interface level to understand the meanings and relations between the different meta-model entities of cross-domain framework software components. In this research, we attempt to ensure that interface description models of software components from heterogeneous frameworks can be compared, correlated and re-used for automotive services based on semantic synergies. We have demonstrated our approach using component meta-models from cross-domain enterprises, that are used for the automotive application domain.

Writing, with no doubt, is besides reading a core competency which allows us to "exploit" knowledge in general. It also makes possible the exploration of software engineering's core issues. Especially within this context it is necessary to master the reading of complex texts as well as to be able to write in an appropriate academic expression. With regard to studies in software engineering this seems to be obvious, but in fact the opposite is the reality. Therefore measures to improve these skills seemed to be necessarily applied. At the Ostbayerische Technische Hochschule (OTH, University of Applied Sciences, Regensburg) a new format, the so called c*lab, was installed during the winter semester 2017. This was a course which followed the principle of "Writing Across the Curriculum" (WAC). Organized parallel to a lecture of learning how to program the language C, and addressing students of the first semester, the course was a complete voluntary offer in addition to the general standard courses and lectures of the faculty. Students who participated not only reflected on C and its principles, nor only on writing as a self purpose, but they also learned to express technical thoughts and ideas by the use of didactic methods. The idea to transfer also basic LTeX concepts to write a paper based on the IEEE bare_conf.tex-template were also planned. The course followed the idea of student's-centred learning. This paper presents the main structure, goals, and means of the c*lab, and the theory behind. It also embeds the course within the horizon of experiences of teaching writing skills at the Laboratory for Safe and Secure Systems (LaS 3 ) at the faculty of electronic and information engineering at the OTH Regensburg. First experiences have shown that participants increase writing skills and their idea of the importance of writing.

Learning tasks play an important role in education and especially in higher education. However, there is a significant lack in research regarding these in higher education. A learning task shows several characteristics, whereas the didactic function of a task is mostly considered. Two characteristics of learning tasks are focused in this paper: The didactic function and the type knowledge. Existing types of learning tasks are presented as well as a proposal for learning tasks in software engineering education that considers didactic functions, like elaboration, training or application and types of knowledge, i.e. factual, conceptual, procedural and metacognitive knowledge. This paper aims to serve as a guidance for lecturers who have the purpose to create learning tasks that address both characteristics -- the didactic function and the type of knowledge.

Today, due to the rapidly evolving technology within the automotive industry, the automation level of cars is continuously increasing. As a consequence, the software code base implementing the automated driving functionality is growing in both, complexity and size. Simultaneously, the semiconductor industry continues with structure and voltage downscaling due to diminishing design margins and stringent power constraints. This trend leads to highly integrated hardware on the one hand, whilst provoking an increase in sensitivity against external causes for hardware faults, e.g., radiation effects or electromagnetic interference. Among the available dependability assessment techniques, fault injection (FI) is widely adopted and ISO 26262 strongly recommends applying it to validate, that functional and technical safety mechanisms are implemented correctly and effectively. We present PyFI (Python backend for Fault Injection), a fault injection backend for the Infineon Aurix TriCore which utilizes an iSystem On-chip Analyzer to inject faults into the application data or instructions that are visible at the assembly level. PyFI allows the injection of bit flips and stuck-at faults in memory and register cells of the hardware which trigger our error symptoms on application level. Furthermore, it implements fault collapsing algorithms to reduce the number of faults and the duration for single experiments by gathering statistics about the static and dynamic application execution.

This study is based on the work of Uwano, Nakamura, Monden and Matsumoto (2006) who tried to identify programmers’ eye movements in source code reviews by using eye tracking technology. The researchers were able to identify certain eye movement patterns but due to the technical limitations of earlier eye tracking systems and a small sample they could not find a valid proof for their existence. Now, twelve years later, the eye tracking technology has made significant improvements and is able to capture programmers’ reading behavior in an unobtrusive and precise way. Now the goal is to verify the described patterns by using eye tracking data from expert and novice programmers. In the experiment they have to detect errors in six different codes and take part in a retrospective interview. At the moment, data collections are ongoing. At the time of the conference, we will present the results of our analyses.

This paper deals with the validation and modification of the German questionnaire "F-Komp". In its original version, it was intended to measure university students' research competences. In the beginning of this study, there were only a few tools available which were reliable. For the purposes of this study, they were not suitable. At the same time, there was no validated version of the F-Komp available, which made the whole validation process for further usage necessary. This questionnaire is based on a structure, which consist of different skills and knowledge and is focused on measuring research competence in general. The validation and modification of the F-Komp is therefore the aim of our contribution as well as a revised version of the questionnaire. We proceeded an explorative factor and a reliability analysis to do a general evaluation of the tool. Some modifications were done in the questionnaire to make it more suitable to the requirements of technical oriented universities of applied sciences [5]. Our revised version is slightly longer and contains several items to gather data about the participants demographics. The modified questionnaire is based on a more appropriate factor structure. This structure is more practically oriented and pays attention to ethical issues. In future cases, this questionnaire will be used in research oriented courses to measure students' progress in acquiring the knowledge and methods which are necessary to perform as a scientist in different research areas.