Eye-tracking and questionnaires are typically treated as separate methods for measuring usability and user experience (UX). Recent studies show that machine learning models trained solely on eye movements can predict pragmatic and hedonic quality ratings. Building on this, this study examines which gaze patterns predict usability and UX and whether models can generalize across stimuli. Five models were trained on eye movements from 121 users browsing six websites. A feature-importance analysis revealed that saccadic patterns, such as regressions and successive forward movements, are more associated with UX, whereas longer consecutive saccades are indicative of usability. When trained separately for each website, the best-performing models achieve Matthews Correlation Coefficient scores of 0.751 and 0.780, with only small negative effect sizes on holdout data. Trained across websites, holdout scores dropped to 0.196 for usability and 0.338 for UX, suggesting that cross-stimuli generalizability is limited and, at best, achievable for hedonic interaction aspects.

Scanpaths and eye movements provide insight into how users perceive and interact with digital products. However, most studies assess user states using stimulus-dependent metrics, like fixations or areas of interest (AOIs). This paper examines whether stimulus-independent saccadic transitions — gaze movements not tied to predefined stimulus elements — carry predictive information about user states and experiences. To do so, eye-tracking data and perceived usability and UX ratings from a study with 121 participants interacting with websites were analyzed. Saccadic transitions were extracted from the scanpaths and analyzed using machine learning models to identify transition patterns predictive of the user ratings. Results show that models predict perceived usability and UX most accurately when saccade transitions are grouped into the eight inter-cardinal directions and further differentiated by median saccade length. This demonstrates that even brief, often-overlooked gaze shifts within the stimulus might provide valuable insight into how users perceive websites.

Eye-tracking is increasingly used in human–computer interaction (HCI) research to measure visual attention and user interaction. More recently, established eye-tracking metrics have been used as inputs to machine learning models. However, the transformation of traditional eye movement metrics into machine-learning-compatible features is rarely discussed in the literature. This limits the reproducibility and interpretability of how different eye-tracking metrics influence classification outcomes, particularly for researchers new to data-driven modeling. This paper provides a survey of how established third- and fourth-order scanpath metrics can be systematically converted into features suitable for classification and regression models. Building on existing metric categorizations, common gaze metrics are linked to concrete feature conversion techniques, including discretization, matrix unfolding, n-gram analysis, and scanpath segmentation.

Gaze offset in eye tracking studies biases AOI-based metrics. In UML studies, classes are often operationalized as AOIs and enlarged to mitigate offset, threatening reproducibility. We propose ClassShift, an ensemble-based post-hoc offset-compensation algorithm that improves fixation-to-class assignment in UML class diagrams without enlarging AOIs. We evaluate ClassShift on synthetic data with ground-truth fixation-to-class labels (9332 scenarios) and real eye tracking data derived from 32 participants. Evaluation on synthetic data shows a 19% Macro-F1 improvement relative to baseline containment-based labeling. In the 9.6% of scenarios where ClassShift underperforms, losses are small (median Δ Macro-F1 = -0.033). On real data, visual inspection suggests plausible fixation-to-class assignments. Participant self-reports further indicate which classes were reported as having been inspected particularly long. After applying ClassShift, relative dwell time for these classes increases significantly over the uncorrected data (p =.028, r = 0.50). Together, these results suggest ClassShift improves eye tracking analyses under systematic offset.

Data quality is essential in eye tracking. One of the most common aspects of it is the spatial difference between actual and reported gaze position, typically measured with accuracy and precision. While reporting these numbers is important for understanding and evaluating eye tracking studies, it is equally important to understand how these numbers came about. Without knowledge of the underlying operationalization choices, the numbers themselves are difficult to compare or interpret meaningfully. We focus on computational decisions that arise once 2D gaze data for a screen-based study has been established. Within this scope, we identify eight choices that researchers deliberately or implicitly make when computing data quality metrics: computation and reporting units, small-angle approximation, actual or normalized gaze origin, distance metric, central tendency function, coordinate system, precision measure, and aggregation method. For each choice, we formalize the mathematical alternatives and illustrate their practical consequences. Our goal is to showcase the variety of computational operationalization approaches, to help researchers understand what choices they have made, and to motivate making these choices explicit. This contributes to strengthening the comparability and reproducibility of data quality reports across eye tracking studies.

Modern education through Learning Management Systems (LMSs) provides learners with personalized learning paths. This is achieved by first querying the learning style according to the theory of Felder and Silverman to recommend suitable learning content. However, a rigid learning style representation is lacking of adaptability to the learners' choices. Therefore, the present study evaluates the idea of providing adaption to the representation of learning styles by using Hidden Markov Models (HMMs). Thus, data is collected from participants out of the Higher Education Area. The Index of Learning Styles questionnaire is used to obtain the learning style based on the theory of Felder and Silverman. Also, a questionnaire that asks the respondents to create a preferred learning path with the sequence length of nine learning elements is provided. From the given data, we initially evaluate the probability relationships between learning styles and learning elements. Then, we use the Viterbi algorithm in HMMs to identify alterations in learning styles from the provided learning paths. The alignment is then quantified by introducing a metric called support value. The findings imply that our concept can be used to adapt the learning style based on the user's real choice of learning elements. Thus, the proposed model also offers a way to integrate a feedback loop within LMSs leading to an improvement of learning path recommendation algorithms.

Most recently, the number of students dropping out of universities or higher education institutions increased dramatically. This might be partly because of the students’ limited capability exploring their own learning paths within a certain course. The introduction of adaptive learning management systems could be a potential solution to this issue. Based on individual’s learning styles, these systems recommend customised and tailored learning paths. These learning styles are commonly identified using questionnaires and learning analytics, but both methods are prone to errors. While questionnaires potentially give superficial answers due to e.g. time constraints, Learning Analytics cannot mirror offline behaviour. This paper proposes an alternative to classify the learning style of individuals through the integration and combination of eye tracking and artificial intelligence algorithms. The eye movement data, which is collected in a study including more than 100 participants, is processed with different methodolgies such as data scaling and subsequently classified using various models ranging from Logistic Regression to Neural Network. Moreover, this experiment setting discovers the interplay between preprocessing and classification techniques based on complex eye tracking metrics in order to determine the most promising solutions for learning style identification. Ultimately, this comprehensive analysis not only enables the understanding of individuals’ subconscious processes, but could also lead to improved educational outcomes.

This study uses holistic models of image perception originating from radiology and psychology to analyze and interpret eye movements during code reviews in the C++ programming language. The study design is based on former experiments, but is supplemented by approaches from expertise research. The study utilizes a sample of 34 subjects whose eye movements are recorded by a Tobii Pro Spectrum 600 Hz. The results show that the holistic models of image perception are suitable for application to source code. In addition, it can be observed that the code reviews are conducted in phases, which are characterized by certain strategies (e.g. scan, error detection,...). Furthermore, experience-related differences can be detected between experts and novices, which emphasize that experts use elaborate strategies and have a comparatively better ability to collect and process information from source code.

In the course Eye Tracking in Engineering Science, students develop a variety of skills. These competencies include both, theoretical knowledge and practical experience: in empirical research (i.e., the design of experiments and the writing of research papers), in eye tracking technology, and in applying computer science and statistics for data analysis. This competence building is done with a teaching concept based on pair-teaching and research-based learning - described in detail in the present article.

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. 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. 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. 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. 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.

Abstract 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.

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).

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.

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.

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.

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.

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.

Experts predict that more IT-Security specialists will be needed in the coming years, but in current higher education in engineering disciplines, this topic is hardly addressed. Newer learning methods such as game-based learning (GBL) are enjoying an increasing popularity as their improvement in the education of subject specific topics can be proven by a variety of studies. We chose Educational Escape Rooms (EduER) as a GBL-tool to impart IT-Security in higher education of engineers. In our Escape Room (ER), the students try to solve puzzles and riddles with learned knowledge on the emphasis of cryptography. This paper first deals with a brief introduction to GBL and EduERs, followed by the design of our ER concept, containing different tasks with focus on the topic of cryptography. The tasks cover different cryptographic methods and hash algorithms, e.g. AES, RSA, SHA3. Afterwards the experimental study is presented. The study of the EduER was carried out with students from the bachelor’s program in Electrical Engineering and Information Technology at the OTH Regensburg. The participants were divided into three groups of 5 to 8 persons each. They received a briefing with important information, followed by the ER execution, a debriefing afterwards and an exam-like evaluation sheet to test their learned knowledge. Finally, first basic results are presented.

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 services, 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 manifestations to satisfy the semantic and syntactic requirements, specific to different application development environments or frameworks. This paper describes 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.

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.

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 (LaS3) 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.

This paper deals with the identification of learning obstacles using the questionnaire method. Therefore, two iterations were proceeded: The first one was part of a survey that was carried out at four locations at universities of applied sciences. We asked students about obstructive facts in general providing items for five learning obstacle dimensions that were set up before; emotional/motivational, epistemological/cognitive, didactical, resource-related and meta-cognitive learning obstacle dimensions. After the general part, we asked them to answer the same question, but in relation to the - in their opinion - most difficult learning content. With this question, we aim to get indications regarding to epistemological obstacles. In a second step, we used the "Motivated Strategies for Learning Questionnaire", which was developed by Pintrich [1] as a basis to develop a questionnaire that extracts learning obstacles. In its original version, the "Motivated Strategies for Learning Questionnaire" was intended to measure students' learning strategies, but, as the obstacle dimensions were partly derived from learning strategy classification, we chose this already validated questionnaire [2]. Within this iteration, we could confirm a five-factor structure of the questionnaire that could be mapped to the five before set learning obstacle dimensions.

In the sixth semester of the bachelor study course “Electrical Engineering and Information Technology”, a lecture “Transmission Systems” is given. It explains basic principles, circuits and systems of electrical and optical transmission systems. Short intellectual impulses, which address professional, intercultural and ethical issues of engineers' working life are integrated into this regular lecture. In each lecture unit, an impulse with one specific topic is given approximately in the middle of the lecture hour. An evaluation was done at the end of the semester, which shows that most of the students remember the discussed topics and state high interest due to high importance for their professional life. Due to these encouraging results, this concept will be developed further.

One of the biggest problems of educational games is the adequate integration of learning content into the game environment. Thereby the main challenge is to create a balance between gameplay and learning objectives and the correct transfer of learning principles into the game design. A useful approach is to identify game components and their specific learning constraints and the relationship between each other. By mapping learning elements to Game Design Pattern, we elaborate fundamentals for the concrete game design, focusing on card games. A shared description and an understanding of how the game design aligns with learning content facilitates the development of high quality educational games. A short illustration of our further developed implementation summarizes our experiences and shows an exemplary realization.

In this paper we describe a proposed framework for Cooperative Adaptive Personalized Education for Life-long Learning and Activation (CAPELLA). CAPELLA comprises four models: A Knowledge Organization Model, a Learning Process Model, a Community Collaboration Model and a Gaming Model. The underlying pedagogical approach is based on the four pillars of learning: Learning to know, learning to do, learning to collaborate and learning to be. Based on these models and principles, CAPELLA provides a theoretical framework that may assist IT developers to develop state-of-the-art educational platforms and advanced learning applications. Using an example of a UML Diagramming Workshop we demonstrate how CAPELLA incorporates the principles of Adaptive Learning.

A cultural change at the university eco-system is possible with diverse learning approaches in faculties. Diverse learning offers will cope with the diversity of students regarding their value systems. Currently teaching at universities is dominated by "teacher-centered teaching", also there are approaches to use different methods to accelerate and intensify the teaching and learning process. Nevertheless these approaches do often not show the desired impact with all students. This paper is offering insights how that comes using the Graves value systems model and is proposing a set of methods which fits to different value systems of students.

Program comprehension and the ability to find program errors are key skills of software engineering. The aim of this pilot study was to examine the visual processes of novice and advanced programmers in authentic tasks. Fifteen novices and eight advanced programmers were given eight short pieces of code. Their task was to either identify an error or give the output of the code. Eye movements and keyboard activity were recorded. On average, the novices spent more time reading the code than composing the response, whereas the more advanced programmers started composing the response sooner and spent more time on it. In general, the advanced programmers had shorter fixations and saccades. The results suggest that the advanced programmers are quicker to grasp the essence of the code and able to see more details in it. The advanced programmers had shorter fixations and saccade lengths during the second phase which might indicate the process of chunking.

Teaching software testing is a challenging task. Especially if you want to impart more in-depth and practical knowledge to the students. Therefore, most lectures still teach in a classic lecture format despite the fact that this way of instruction is in any case the optimal way of instruction for today's requirements anymore. In this paper we present our implementation of an active learning method to deepen the knowledge in academic software test education. We describe a card game for advanced learning that promotes students' collaboration and knowledge exchange in a playful and competitive manner. The design of the game is based on constructive and cooperative theories. A subsequent evaluation shows that the use of this card game for teaching software testing is a suitable method.

In this research, we investigate the possibility of applying ranking task activity in teaching and learning software engineering courses. We introduce three types of ranking tasks, conceptual-, contextual- and sequential ranking questions, which cover most core topics such as requirement analysis, architecture design and quality validation in the course. We have also done experiments on a group of students to see if ranking tasks could increase their conceptual knowledge in specific areas. Assessments were given in order to evaluate the effectiveness of this activity, showing an obvious increase in complex conceptual understanding.

With the availability of the AUTOSAR standard, model-driven methodologies are becoming established in theautomotive domain. However, the process of creating models ofexisting system components is often difficult and time consuming, especially when legacy code has to be re-used or informationabout the exact timing behavior is needed. In order to tackle thisreverse engineering problem, we present CoreTAna, a novel toolthat derives an AUTOSAR compliant model of a real-time systemfrom a dynamic analysis of its trace recordings. This paper givesan overview of CoreTAna's current features and discusses itsbenefits for reverse engineering.

Considering the claim of furthering self-directed learning in higher education in general and in Software Engineering education in particular, this paper deals with a new approach on understanding and facilitating self-directed learning. This approach involves the concept of subjective theories, which are expected to influence students' self-directed learning. Therefore this paper presents the intended qualitative research design for reconstructing these subjective theories and for developing ways of integrating them in didactical situations in higher education and especially in Software Engineering education.

Within this thesis, we present our suggestions why playful learning in software engineering education is useful to mediate generic competences in academic teaching. Therefor we identified competences which are addressed by playful learning and mapped them to demanded generic competences in software engineering. Due to the well compliance, we analyzed current implementations of playful learning and their design regarding the mediation of required soft skills. Based on the lack of effective implementation, we close our paper with an exemplary design for playful learning.

Measuring competencies may serve as a feedback mechanism as well as a judgment device for a lecturer. As measuring every competency from a catalogue of competencies is not very viable, the to-be-measured competencies are grouped in competency profiles. Further, assessment practices are shown and applied to a course in a study program. A discussion of useful practices concludes this contribution.

Teaching design patterns in computer science introductory sequence is more difficult than learning it. We present a Just-in-Time-Teaching (JiTT) practice in Software Design Patterns course targeted to entry level students. We proposed a model that combines JiTT concepts with didactic considerations, preparation materials, in-class sessions and post-lecture assignments. We have effectively used this approach to teach two undergraduate classes. The paper also presents the results of a survey conducted to identify the effectiveness of the JiTT activities through warm-up questions, class-wide discussions, experiments and exercises.

In this paper we present simulation and model based approaches for evaluating and validating the temporal and safety relevant properties of software intensive safety-critical real-time embedded systems. A high level reliability model of a safe task execution is described by a continuous-time Markov process, enhanced by the modeling of execution times. It is shown that the behavior - regarding real-time and safety metrics - of this theoretical model can be transferred into an abstract system timing model, which then can be analyzed by a discrete event simulation approach. The verification of the discrete event simulation by Markov models offers the possibility of a holistic approach for reliability analysis combined with schedulability analysis of complex safety-critical multicore real-time systems by the discrete event simulation.

During a student project a stratosphere balloon was developed and launched. This project included the definition of the balloon parameters and the scientific instruments for performing atmospheric measurements, the development of all mechanical and electronic parts, the administration of the project as well as the management tasks related to the balloon launch. The main challenge for the students was the high complexity of the project due to tasks involving many different knowledge domains and the long project duration from the initial definition to the launch.

The increase in complexity of software and its creation over the last decades requires an increase in the quality of the education of software engineering professionals. The number of Universities which answer this call by replacing or extending lectures with simulated software projects is constantly rising. However, the question how to best transfer new technological knowledge needed to be successful within this projects to the students remains open. Therefore this paper will present a comparison between the classical frontal lecture and a modern workshop learning arrangement used within these projects. By the use of workshops not only the knowledge transfer but also the acquisition of personal and social competences will be facilitated. Furthermore positive experiences with workshops at university level will support life-long learning.

Professional software development is a complex task with many inputs and a complex output. In order to handle complex topics as software or complex engineering projects, structured processes as the V-model or iterative development processes exist. Similarly, the development of a software engineering lecture is a task with many inputs, and a complex output. A structured and methodological approach to the development of a lecture is presented, which applies the same principles as used in the development of software.

Learning centered teaching becomes an important factor in a global perspective of learning software engineering. The Just-in-Time Teaching approach is used in a Chinese-German empirical case study. In a one year terminated project we will analyze the performance of our students in an active learning scenario with Just-in-Time Teaching and Peer Instruction. We will contribute an inter-cultural comparison of achieved competencies by student's self-assessment and teacher's observation.

We present in this paper a new lock-based resource sharing protocol PWLP (Preemptable Waiting Locking Protocol) for embedded multi-core processors. It is based on the busy-wait model and works with non-preemptive critical sections while task may be preempted by tasks with a higher priority when waiting for resources. Our protocol can be applied in partitioned as well as global scheduling scenarios, in which task-fix, job-fix or dynamically assigned priorities may be used. Furthermore, the PWLP permits nested requests to shared resources. Finally, we present a case study based on event-based simulations in which the FMLP (Flexible Multiprocessor Locking Protocol) and the proposed PWLP are compared.

The data flow is a crucial part of software execution in recent applications. It depends on the concrete implementation of the realized algorithm and it influences the correctness of a result in case of hardware faults during the calculation. In logical circuits, like arithmetic operations in a processor system, arbitrary faults become a more tremendous aspect in future. With modern manufacturing processes, the probability of such faults will increase and the result of a software's data flow will be more vulnerable. This paper shows a principle evaluation method for the reliability of a software's data flow with arbitrary soft errors also with the concept of fault compensation. This evaluation is discussed by means of a simple example based on an addition.

The professional requirements in Software Engineering have become highly volatile due to the complexities of project development and rapid and innovative changes occurring in the field. Therefore, the development of inter-personal and social competences has gained central importance in the training of software developers. This paper will present a concept allowing to acquire competences by using Case Method and Role Play as instruments in several lectures of Software Engineering. By approaching the issue of competence acquisition on a technical as well as on an educational and social level, life-long learning is facilitated and supported.

This paper presents the reliability evaluation of task execution during safe software processing. The standard method of duplication in a safety-critical application can also be applied for tasks in a software system. But in addition to this, there is also the possibility for coded task processing to increase the reliability and availability of software. The presented analysis covers the reliability analysis of a single, a duplicated and a coded task by the technique of continuous time Markov processes. Markov processes are often used for the reliability evaluation of safety-critical systems. We introduce a method to describe the execution time of tasks by means of enhanced Markov models and their solution by numerical methods.

Software process models are fairly abstract tools for organizing large, complex software development projects. Since particularly undergraduate students commonly do not have any experience in being part of such a project, their understanding of the benefits and shortcomings of particular process models is very limited. Even more, frequently they are not aware of the need to follow a particular process model since their previous one-person software assignments were too small for requiring any such model at all.