Andreas Rausch

Dominique Briechle, Raj Chanchad, Tobias Geger et al.

Explicit software architecture models are essential artifacts for communicating, analyzing, and evolving complex software-intensive systems. In ROS~2-based robotic systems, however, structural (de-)composition and integration semantics are often only implicitly encoded across distributed artifacts such as source code and launch files, making recovery of hierarchical architecture particularly difficult. Existing approaches mainly focus on node-level entities and communication wiring, while providing limited support for recovering hierarchical structural (de-)composition across multiple abstraction levels. In this paper, we extend our previously proposed blueprint-guided LLM-assisted architecture recovery pipeline for ROS~2 systems through two major enhancements: (1) refined prompting to improve the consistency and controllability of architecture synthesis, and (2) a staged recovery strategy based on multi-level intermediate architectural representations that incorporate the atomic ROS node list and launch file dependencies, thereby enabling structurally constrained reconstruction across multiple abstraction levels. The approach is evaluated on a real-world automated product disassembly system based on cooperative robotic arms and heterogeneous ROS~2 artifacts. Compared to our previous work, the considered case study exhibits substantially higher integration complexity and richer functionality. The results demonstrate improved structural consistency, scalability, and robustness of architecture recovery, while also revealing remaining challenges related to dynamic integration semantics in large-scale ROS~2 systems.

Ruidi He, Vaibhav Tiwari, Mohanad Al-Ghobari et al.

High-definition (HD) maps are core artifacts for automated driving systems, but their generation commonly relies on sensor-intensive mobile mapping campaigns, while quality assessment often depends on high-precision reference data. These dependencies make HD map engineering costly and difficult to apply in settings where specialised measurement data or independently measured reference maps are unavailable. This paper presents an engineering-oriented geo-data-driven workflow for HD map generation with integrated representation-level verification. The workflow uses openly available geo-engineering datasets as the primary input source and transforms them into lane-level HD map representations of existing road environments through explicit intermediate representations and processing stages. To assess the generated representations without external reference maps, the workflow integrates executable constraint-based verification into the engineering process. Selected constraints are derived from specifications relevant to automated driving and road-design guidelines. They are evaluated directly on the generated lanelet-based representation to detect geometric, topological, and elevation-related inconsistencies. The workflow is evaluated using real-world shapefile-based road-network data from four cities in Lower Saxony, Germany, and controlled defect-injection scenarios. The real-world evaluation shows that the generated map representations satisfy the selected constraints in the evaluated scenarios, while the defect-injection study demonstrates complete detection of the considered defect types without observed false positives. The results indicate that geo-data-driven HD map generation with integrated executable verification can provide a modular and inspectable complement to sensor-intensive mapping workflows under reduced sensing and reference-data availability.

Fidae El Morer, Stefan Wittek, Andreas Rausch

The degradation of sewer pipes poses significant economical, environmental and health concerns. The maintenance of such assets requires structured plans to perform inspections, which are more efficient when structural and environmental features are considered along with the results of previous inspection reports. The development of such plans requires degradation models that can be based on statistical and machine learning methods. This work proposes a methodology to assess their suitability to plan inspections considering three dimensions: accuracy metrics, ability to produce long-term degradation curves and explainability. Results suggest that although ensemble models yield the highest accuracy, they are unable to infer the long-term degradation of the pipes, whereas the Logistic Regression offers a slightly less accurate model that is able to produce consistent degradation curves with a high explainability. A use case is presented to demonstrate this methodology and the efficiency of model-based planning compared to the current inspection plan.

Nour Habib, Yunsu Cho, Abhishek Buragohain et al.

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.

Andreas Rausch, Stefan Wittek, Tobias Geger et al.

Generative AI and agentic tools are reshaping agile software development, yet many engineering curricula still teach agile methods and AI competencies separately and largely lecture-based. This paper presents a project-based AI Engineering curriculum designed to prepare students for AI-driven agile development by integrating agile practices and AI-enabled engineering throughout the program. We contribute (1) the curriculum concept and guiding principles, (2) a case study of interdisciplinary, AI-enabled agile student projects, and (3) early evidence from a mixed-methods evaluation. In our case study, second-semester bachelor students work in teams over seven two-week sprints on a realistic software product. AI tools are embedded into everyday agile engineering tasks - requirements clarification, backlog refinement, architectural reasoning, coding support, testing, and documentation - paired with reflection on human responsibility and quality. Initial results indicate that the integrated approach supports hands-on competence development in AI-assisted engineering. Key observations highlight the need for agile teaching adaptations due to rapid tool evolution, the critical role of oral verification to ensure foundational learning. We close with lessons learned and recommendations for educators designing agile project-based curricula in the age of AI.

Ahmad Hatahet, Christoph Knieke, Andreas Rausch

Software Architecture Descriptions (SADs) are essential for managing the inherent complexity of modern software systems. They enable high-level architectural reasoning, guide design decisions, and facilitate effective communication among diverse stakeholders. However, in practice, SADs are often missing, outdated, or poorly aligned with the system's actual implementation. Consequently, developers are compelled to derive architectural insights directly from source code-a time-intensive process that increases cognitive load, slows new developer onboarding, and contributes to the gradual degradation of clarity over the system's lifetime. To address these issues, we propose a semi-automated generation of SADs from source code by integrating reverse engineering (RE) techniques with a Large Language Model (LLM). Our approach recovers both static and behavioral architectural views by extracting a comprehensive component diagram, filtering architecturally significant elements (core components) via prompt engineering, and generating state machine diagrams to model component behavior based on underlying code logic with few-shots prompting. This resulting views representation offer a scalable and maintainable alternative to traditional manual architectural documentation. This methodology, demonstrated using C++ examples, highlights the potent capability of LLMs to: 1) abstract the component diagram, thereby reducing the reliance on human expert involvement, and 2) accurately represent complex software behaviors, especially when enriched with domain-specific knowledge through few-shot prompting. These findings suggest a viable path toward significantly reducing manual effort while enhancing system understanding and long-term maintainability.

Mohammad Abboush, Ahmad Hatahet, Andreas Rausch

A well-known testing method for the safety evaluation and real-time validation of automotive software systems (ASSs) is Fault Injection (FI). In accordance with the ISO 26262 standard, the faults are introduced artificially for the purpose of analyzing the safety properties and verifying the safety mechanisms during the development phase. However, the current FI method and tools have a significant limitation in that they require manual identification of FI attributes, including fault type, location and time. The more complex the system, the more expensive, time-consuming and labour-intensive the process. To address the aforementioned challenge, a novel Large Language Models (LLMs)-assisted fault test cases (TCs) generation approach for utilization during real-time FI tests is proposed in this paper. To this end, considering the representativeness and coverage criteria, the applicability of various LLMs to create fault TCs from the functional safety requirements (FSRs) has been investigated. Through the validation results of LLMs, the superiority of the proposed approach utilizing gpt-4o in comparison to other state-of-the-art models has been demonstrated. Specifically, the proposed approach exhibits high performance in terms of FSRs classification and fault TCs generation with F1-score of 88% and 97.5%, respectively. To illustrate the proposed approach, the generated fault TCs were executed in real time on a hardware-in-the-loop system, where a high-fidelity automotive system model served as a case study. This novel approach offers a means of optimizing the real-time testing process, thereby reducing costs while simultaneously enhancing the safety properties of complex safety-critical ASSs.

Tobias Geger, Andreas Rausch, Ina Schiering et al.

Agile software development evolves so rapidly that research struggles to remain timely and transferable - an issue heightened by the swift adoption of generative AI and agentic tools. Earlier discussions highlight theory and time gaps, leading to results that often lack clear reuse conditions or arrive too late for practical decisions. This paper introduces a project-based, AI-integrated agile education platform as a collaborative research environment, positioned between controlled studies and real-world industry. The platform enables rapid inquiry through sprint rhythms, quality gates, and genuine stakeholder involvement. We present a framework specifying iteration structures, recurring events, and quality gates for AI-assisted engineering artifacts. Early results from several semesters - covering project pipeline, cohort growth, and stakeholder participation - show the platform's potential to generate practice-relevant evidence efficiently and with reusable context. Finally, we outline future steps to enhance governance and evidence capture.

Andreas Rausch, Stefan Wittek

Different domains foster different architectural styles -- and thus different documentation practices (e.g., state-based models for behavioral control vs. ER-style models for information structures). Agentic AI systems exhibit another characteristic style: specialized agents collaborate by exchanging artifacts, invoking external tools, and coordinating via recurring interaction patterns and quality gates. As these systems evolve into long-lived industrial solutions, documentation must capture these style-defining concerns rather than relying on ad-hoc code sketches or pipeline drawings. This paper reports industrial experience from joint projects and derives a documentation systematics tailored to this style. Concretely, we provide (i) a style-oriented modeling vocabulary and a small set of views for agents, artifacts, tools, and their coordination patterns, (ii) a hierarchical description technique aligned with C4 to structure these views across abstraction levels, and (iii) industrial examples with lessons learned that demonstrate how the approach yields transparent, maintainable architecture documentation supporting sustained evolution.

Ruidi He, Yu Zhang, Meng Zhang et al.

High-definition map transformations are essential in autonomous driving systems, enabling interoperability across tools. Ensuring their semantic correctness is challenging, since existing rule-based frameworks rely on manually written formulas and domain-specific functions, limiting scalability. In this paper, We present an LLM-assisted pipeline that jointly generates logical formulas and corresponding executable predicates within a computational FOL framework, extending the map verifier in CommonRoad scenario designer with elevation support. The pipeline leverages prompt-based LLM generation to produce grammar-compliant rules and predicates that integrate directly into the existing system. We implemented a prototype and evaluated it on synthetic bridge and slope scenarios. The results indicate reduced manual engineering effort while preserving correctness, demonstrating the feasibility of a scalable, semi-automated human-in-the-loop approach to map-transformation verification.

Iqra Aslam, Nour Habib, Abhishek Buragohain et al.

The advancement of automated vehicles introduces complex safety challenges, particularly in dynamic and unpredictable environments where AI-enabled perception systems must operate reliably. Ensuring compliance with safety standards such as ISO 26262 and ISO/PAS 21448 (SOTIF) is essential for addressing system malfunctions and mitigating unsafe behavior in unknown scenarios. However, as automation levels increase, vehicles must go beyond conventional functional safety by incorporating fail-operational capabilities that enable continued safe operation during system or component failures and the handling of unfamiliar or degraded operational conditions. To address these safety concerns, we propose the Connected Dependability Cage, an architectural framework designed to enable hierarchical fail-operational behavior in AI-enabled perception systems. This framework integrates two complementary monitoring mechanisms: a Function Monitor that oversees multiple heterogeneous AI-based perception pipelines and detects inconsistencies through a voting mechanism, and an Anomaly Monitor that evaluates the reliability of AI perception by detecting unknown or novel objects in scenes that may be excluded from the training dataset. In the presence of critical discrepancies, the system supports graceful degradation, ultimately enabling a transition to a minimal-risk maneuver strategy. Furthermore, whenever either monitor raises a safety flag, an automated data recording process is initiated to facilitate iterative system development and continuous improvement. Both monitors have been implemented and validated through extensive vehicle testing, demonstrating their practical effectiveness in real-world applications.

Tobias Geger, Dominique Briechle, Andreas Rausch

To prepare students for upcoming trends and challenges, it is important to teach them about the helpful and important aspects of modern technologies, such as robotics. However, classic study programs often fail to prepare students for working in the industry because of the lack of practical experience, caused by solely theoretical lecturing. The challenge is to teach both practical and theoretical skills interactively to improve the students' learning. In the scope of the paper, a project-based learning approach is proposed, where students are taught in an agile, semester-spanning project how to work with robots. This project is part of the applied computer science degree study program Digital Technologies. The paper presents the framework as well as an exemplary project featuring the development of a disassembly software ecosystem for hardware robots. In the project, the students are taught the programming of robots with the help of the Robot Operating System (ROS). To ensure the base qualifications, the students are taught in so-called schools, an interactive mix of lectures and exercises. At the beginning of the course, the basics of the technologies are covered, while the students work more and more in their team with the robot on a specific use case. The use case here is to automate the disassembly of build block assemblies.

Hamidreza Eivazi, Stefan Wittek, Andreas Rausch

Operator learning provides methods to approximate mappings between infinite-dimensional function spaces. Deep operator networks (DeepONets) are a notable architecture in this field. Recently, an extension of DeepONet based on model reduction and neural networks, proper orthogonal decomposition (POD)-DeepONet, has been able to outperform other architectures in terms of accuracy for several benchmark tests. We extend this idea towards nonlinear model order reduction by proposing an efficient framework that combines neural networks with kernel principal component analysis (KPCA) for operator learning. Our results demonstrate the superior performance of KPCA-DeepONet over POD-DeepONet.

Hamidreza Eivazi, Jendrik-Alexander Tröger, Stefan Wittek et al.

Multiscale problems are ubiquitous in physics. Numerical simulations of such problems by solving partial differential equations (PDEs) at high resolution are computationally too expensive for many-query scenarios, e.g., uncertainty quantification, remeshing applications, topology optimization, and so forth. This limitation has motivated the application of data-driven surrogate models, where the microscale computations are $\textit{substituted}$ with a surrogate, usually acting as a black-box mapping between macroscale quantities. These models offer significant speedups but struggle with incorporating microscale physical constraints, such as the balance of linear momentum and constitutive models. In this contribution, we propose Equilibrium Neural Operator (EquiNO) as a $\textit{complementary}$ physics-informed PDE surrogate for predicting microscale physics and compare it with variational physics-informed neural and operator networks. Our framework, applicable to the so-called multiscale FE$^{\,2}\,$ computations, introduces the FE-OL approach by integrating the finite element (FE) method with operator learning (OL). We apply the proposed FE-OL approach to quasi-static problems of solid mechanics. The results demonstrate that FE-OL can yield accurate solutions even when confronted with a restricted dataset during model development. Our results show that EquiNO achieves speedup factors exceeding 8000-fold compared to traditional methods and offers an optimal balance between data-driven and physics-based strategies.

Iqra Aslam, Abhishek Buragohain, Daniel Bamal et al.

Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.

Sakshi Dhankhar, Stefan Wittek, Hamidreza Eivazi et al.

Study Region: Goslar and Göttingen, Lower Saxony, Germany. Study Focus: In July 2017, the cities of Goslar and Göttingen experienced severe flood events characterized by short warning time of only 20 minutes, resulting in extensive regional flooding and significant damage. This highlights the critical need for a more reliable and timely flood forecasting system. This paper presents a comprehensive study on the impact of radar-based precipitation data on forecasting river water levels in Goslar. Additionally, the study examines how precipitation influences water level forecasts in Göttingen. The analysis integrates radar-derived spatiotemporal precipitation patterns with hydrological sensor data obtained from ground stations to evaluate the effectiveness of this approach in improving flood prediction capabilities. New Hydrological Insights for the Region: A key innovation in this paper is the use of residual-based modeling to address the non-linearity between precipitation images and water levels, leading to a Spatiotemporal Radar-based Precipitation Model with residuals (STRPMr). Unlike traditional hydrological models, our approach does not rely on upstream data, making it independent of additional hydrological inputs. This independence enhances its adaptability and allows for broader applicability in other regions with RADOLAN precipitation. The deep learning architecture integrates (2+1)D convolutional neural networks for spatial and temporal feature extraction with LSTM for timeseries forecasting. The results demonstrate the potential of the STRPMr for capturing extreme events and more accurate flood forecasting.

Christian Schindler, Andreas Rausch

Detecting design pattern instances in unfamiliar codebases remains a challenging yet essential task for improving software quality and maintainability. Traditional static analysis tools often struggle with the complexity, variability, and lack of explicit annotations that characterize real-world pattern implementations. In this paper, we present a novel approach leveraging Large Language Models to automatically identify design pattern instances across diverse codebases. Our method focuses on recognizing the roles classes play within the pattern instances. By providing clearer insights into software structure and intent, this research aims to support developers, improve comprehension, and streamline tasks such as refactoring, maintenance, and adherence to best practices.

Iqra Aslam, Igor Anpilogov, Andreas Rausch

Autonomous driving technology is progressing rapidly, largely due to complex End To End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, raising safety concerns. This paper presents a new solution a Behavior Selector that uses multiple smaller artificial neural networks (ANNs) to manage different driving tasks, such as lane following and turning. Rather than relying on a single large network, which can be burdensome, require extensive training data, and is hard to understand, the developed approach allows the system to dynamically select the appropriate neural network for each specific behavior (e.g., turns) in real time. We focus on ensuring smooth transitions between behaviors while considering the vehicles current speed and orientation to improve stability and safety. The proposed system has been tested using the AirSim simulation environment, demonstrating its effectiveness.

Hamidreza Eivazi, André Hebenbrock, Raphael Ginster et al.

Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery capacity loss accurately remains a formidable task due to its complex nature, influenced by both aging and cycling behaviors. To address this challenge, we introduce a novel general-purpose model for battery degradation prediction and synthesis, DiffBatt. Leveraging an innovative combination of conditional and unconditional diffusion models with classifier-free guidance and transformer architecture, DiffBatt achieves high expressivity and scalability. DiffBatt operates as a probabilistic model to capture uncertainty in aging behaviors and a generative model to simulate battery degradation. The performance of the model excels in prediction tasks while also enabling the generation of synthetic degradation curves, facilitating enhanced model training by data augmentation. In the remaining useful life prediction task, DiffBatt provides accurate results with a mean RMSE of 196 cycles across all datasets, outperforming all other models and demonstrating superior generalizability. This work represents an important step towards developing foundational models for battery degradation.

Rabia Iftikhar, Andreas Rausch

Large Language Models (LLMs) are increasingly applied to automate software engineering tasks, including the generation of UML class diagrams from natural language descriptions. While prior work demonstrates that LLMs can produce syntactically valid diagrams, syntactic correctness alone does not guarantee meaningful design. This study investigates whether LLMs can move beyond diagram translation to perform design synthesis, and how reliably they maintain design-oriented reasoning under variation. We introduce a preference-based few-shot prompting approach that biases LLM outputs toward designs satisfying object-oriented principles and pattern-consistent structures. Two design-intent benchmarks, each with three domain-only, paraphrased prompts and 10 repeated runs, are used to evaluate three LLMs (ChatGPT 4o-mini, Claude 3.5 Sonnet, Gemini 2.5 Flash) across three modeling strategies: standard prompting, rule-injection prompting, and preference-based prompting, totaling 540 experiments (i.e. 2x3x10x3x3). Results indicate that while preference-based alignment improves adherence to design intent it does not eliminate non-determinism, and model-level behavior strongly influences design reliability. These findings highlight that achieving dependable LLM-assisted software design requires not only effective prompting but also careful consideration of model behavior and robustness.

Moustapha El Outmani, Manthan Venkataramana Shenoy, Ahmad Hatahet et al.

Agile organizations increasingly rely on automated regression testing to sustain rapid, high-quality software delivery. However, as systems grow and requirements evolve, a persistent bottleneck arises: test specifications are produced faster than they can be transformed into executable scripts, leading to mounting manual effort and delayed releases. In partnership with Hacon (a Siemens company), we present an agentic AI approach that generates system-level test scripts directly from validated specifications, aiming to accelerate automation without sacrificing human oversight. Our solution features a retrieval-augmented, multi-agent architecture integrated into Hacon's agile workflows. We evaluate this system through a mixed-method analysis of industrial artifacts and practitioner feedback. Results show that the AI teammate significantly increases test script throughput and reduces manual authoring effort, while underscoring the ongoing need for clear specifications and human review to ensure quality and maintainability. We conclude with practical lessons for scaling regression automation and fostering effective Human-AI collaboration in agile environments.

Mohammad Abboush, Ehab Ghannoum, Andreas Rausch

Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process. Duringsystemverificationandvalidation,theintegrationofanintelligent fault detection anddiagnosis (FDD) model with test recordings analysis process serves as a powerful tool for efficiency ensuring functional safety. However, the lack of interpretability of the black-box FDD models developed not only hinders understanding of the cause underlying the prediction, but also prevents the model from being adapted based on the prediction result. This, in turn, increases the computational cost required for developingacomplexFDDmodelandlimitsconfidenceinreal-timesafety-criticalapplications.To address this challenge, a novel explainable method for fault detection, identification, and localization is proposed in this article with the aim of providing a clear understanding of the logic behind the prediction outcome. To this end, a hybrid 1dCNN-GRU-based intelligent model was developed to analyze the recordings from the real-time validation process of ASSs. The employment of explainable AI techniques, i.e., IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, was instrumental in enabling model adaptation and facilitating the root cause analysis (RCA). The proposed approach is applied to the real time dataset collected during a virtual test drive performed by the user on hardware in the loop system.

Dhananjeyan Jeyaraj, Hamidreza Eivazi, Jendrik-Alexander Tröger et al.

The behavior of materials is influenced by a wide range of phenomena occurring across various time and length scales. To better understand the impact of microstructure on macroscopic response, multiscale modeling strategies are essential. Numerical methods, such as the $\text{FE}^2$ approach, account for micro-macro interactions to predict the global response in a concurrent manner. However, these methods are computationally intensive due to the repeated evaluations of the microscale. This challenge has led to the integration of deep learning techniques into computational homogenization frameworks to accelerate multiscale simulations. In this work, we employ neural operators to predict the microscale physics, resulting in a hybrid model that combines data-driven and physics-based approaches. This allows for physics-guided learning and provides flexibility for different materials and spatial discretizations. We apply this method to time-dependent solid mechanics problems involving viscoelastic material behavior, where the state is represented by internal variables only at the microscale. The constitutive relations of the microscale are incorporated into the model architecture and the internal variables are computed based on established physical principles. The results for homogenized stresses ($<6\%$ error) show that the approach is computationally efficient ($\sim 100 \times$ faster).

Hamidreza Eivazi, Mahyar Alikhani, Jendrik-Alexander Tröger et al.

Multiscale problems are widely observed across diverse domains in physics and engineering. Translating these problems into numerical simulations and solving them using numerical schemes, e.g. the finite element method, is costly due to the demand of solving initial boundary-value problems at multiple scales. On the other hand, multiscale finite element computations are commended for their ability to integrate micro-structural properties into macroscopic computational analyses using homogenization techniques. Recently, neural operator-based surrogate models have shown trustworthy performance for solving a wide range of partial differential equations. In this work, we propose a hybrid method in which we utilize deep operator networks for surrogate modeling of the microscale physics. This allows us to embed the constitutive relations of the microscale into the model architecture and to predict microscale strains and stresses based on the prescribed macroscale strain inputs. Furthermore, numerical homogenization is carried out to obtain the macroscale quantities of interest. We apply the proposed approach to quasi-static problems of solid mechanics. The results demonstrate that our constitutive relations-aware DeepONet can yield accurate solutions even when being confronted with a restricted dataset during model development.

Andreas Rausch, Azarmidokht Motamedi Sedeh, Meng Zhang

Many autonomous systems, such as driverless taxis, perform safety critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for the environment perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection - identifying data that differ in some respect from the data used for training - becomes a safety measure for system development and operation. In this paper, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from literature by minimizing false negatives.

Christoph Knieke, Andreas Rausch, Mirco Schindler

Architecture erosion is a big challenge in modern architectures leading to a deterioration of the quality properties of these systems. Today, no comprehensive approach for regaining architecture consistency in eroded software systems exists and architecture consistency is essentially achieved by repairing the implementation level only. In this paper, we propose a novel approach enabling a joint architecture and implementation repairing for tackling software architecture erosion. By using a holistic view on violation causes and suitable repair actions in combination with learning mechanisms we build up a project specific knowledge-base improving accuracy and efficiency in consolidation of architecture and implementation over time.

Basel Alhaji, Andreas Rausch, Michael Prilla

The recent revolution of intelligent systems made it possible for robots and autonomous systems to work alongside humans, collaborating with them and supporting them in many domains. It is undeniable that this interaction can have huge benefits for humans if it is designed properly. However, collaboration with humans requires a high level of cognition and social capabilities in order to gain humans acceptance. In all-human teams, mutual trust is the engine for successful collaboration. This applies to human-robot collaboration as well. Trust in this interaction controls over- and under-reliance. It can also mitigate some risk. Therefore, an appropriate trust level is essential for this new form of teamwork. Most research in this area has looked at trust of humans in machines, neglecting the mutuality of trust among collaboration partners. In this paper, we propose a trust model that incorporates this mutuality captures trust levels of both the human and the robot in real-time, so that robot can base actions on this, allowing for smoother, more natural interactions. This increases the human autonomy since the human does not need to monitor the robot behavior all the time.

Adina Aniculaesei, Daniel Arnsberger, Falk Howar et al.

There is an increasing necessity to deploy autonomous systems in highly heterogeneous, dynamic environments, e.g. service robots in hospitals or autonomous cars on highways. Due to the uncertainty in these environments, the verification results obtained with respect to the system and environment models at design-time might not be transferable to the system behavior at run time. For autonomous systems operating in dynamic environments, safety of motion and collision avoidance are critical requirements. With regard to these requirements, Macek et al. [6] define the passive safety property, which requires that no collision can occur while the autonomous system is moving. To verify this property, we adopt a two phase process which combines static verification methods, used at design time, with dynamic ones, used at run time. In the design phase, we exploit UPPAAL to formalize the autonomous system and its environment as timed automata and the safety property as TCTL formula and to verify the correctness of these models with respect to this property. For the runtime phase, we build a monitor to check whether the assumptions made at design time are also correct at run time. If the current system observations of the environment do not correspond to the initial system assumptions, the monitor sends feedback to the system and the system enters a passive safe state.

Franz Huber, Andreas Rausch, Bernhard Rumpe

We adopt a component model based on object-oriented systems, introducing the concepts of components and their structure. A component consists of a dynamically changing set of connected objects. Only some of these objects are interface objects, and are thus accessible from the environment. During the component lifetime not only the number of objects, but also that of interface objects, and their connections change. To describe this situation, we introduce component interface diagrams (CIDs)-an adaption of UML diagrams-as a notation to characterize interfaces of components, their structure, and their navigability. We show how CIDs can be used to describe the in-house developed Open Editor Framework (OEF). Finally, we give guidelines that allow to map components described with CIDs directly to several component technologies, like ActiveX, CORBA, or Java Beans

Christian Bartelt, Manfred Broy, Christoph Herrmann et al.

Global software engineering has become a fact in many companies due to real necessity in practice. In contrast to co-located projects global projects face a number of additional software engineering challenges. Among them quality management has become much more difficult and schedule and budget overruns can be observed more often. Compared to co-located projects global software engineering is even more challenging due to the need for integration of different cultures, different languages, and different time zones - across companies, and across countries. The diversity of development locations on several levels seriously endangers an effective and goal-oriented progress of projects. In this position paper we discuss reasons for global development, sketch settings for distribution and views of orchestration of dislocated companies in a global project that can be seen as a "virtual project environment". We also present a collection of questions, which we consider relevant for global software engineering. The questions motivate further discussion to derive a research agenda in global software engineering.