arXiv subjects

Andreas Rausch

Publications and source records attributed to Andreas Rausch.

At least 37 records · Page 2Linked to original sources

arXiv2025

Towards Benchmarking Design Pattern Detection Under Obfuscation: Reproducing and Evaluating Attention-Based Detection Method

This paper investigates the semantic robustness of attention-based classifiers for design pattern detection, particularly focusing on their reliance on structural and behavioral semantics. We reproduce the DPDAtt, an attention-based design pattern detection approach using learning-based classifiers, and evaluate its performance under obfuscation. To this end, we curate an obfuscated version of the DPDAtt Corpus, where the name identifiers in code such as class names, method names, etc., and string literals like print statements and comment blocks are replaced while preserving control flow, inheritance, and logic. Our findings reveal that these trained classifiers in DPDAtt depend significantly on superficial syntactic features, leading to substantial misclassification when such cues are removed through obfuscation. This work highlights the need for more robust detection tools capable of capturing deeper semantic meanings in source code. We propose our curated Obfuscated corpus (containing 34 Java source files) as a reusable proof-of-concept benchmark for evaluating state-of-the-art design pattern detectors on their true semantic generalization capabilities.

cs.SE↗

arXiv2025

LLMs-Powered Real-Time Fault Injection: An Approach Toward Intelligent Fault Test Cases Generation

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.

cs.SE↗

arXiv2025

The Lifecycle Workbench -- A Configurable Framework for Digitized Product Maintenance Services

The global production of electric goods is at an all-time high, causing negative environmental and health impacts as well as a continuing depletion of natural resources. Considering the worsening global climate change, a transition of current industrial processes is necessary to tackle the above-mentioned factors. To address this urgent issue, socio-economic systems like the Circular Economy (CE) provide options to reallocate the use of resources and products on a global scale. Especially in terms of product lifecycle-prolonging, this system provides suitable approaches to alter the current modes of product handling by society and industry alike, based on the condition of the products. Although the importance and benefits of sustainable services enabling these options are widely known, users tend to shy away from using them. One of the reasons is the missing reliability in terms of the knowledge of the costs associated with a particular service. This uncertainty in expected pricing can, therefore, lower the willingness of potential clients. However, not only clients struggle with the boundary conditions of such services. On the part of the potential providers of services, the monetary risk is often caused by the incapability to detect the condition of a product in advance. This can result on the provider side in a severe economic loss if this possibility is not covered by the service price or through the mass of items, which could allow equalization of serval service operations. To address these weak points in current service execution, the authors propose the \textit{Lifecycle Workbench (LCW)}-ecosystem, which features digital representations to enhance the reliability of service pricing as well as the assessment of the condition of items, assemblies, and parts in the Circular Economy domain.

cs.SE↗

arXiv2025

Generating Software Architecture Description from Source Code using Reverse Engineering and Large Language Model

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.

cs.SE↗

arXiv2025

VeriODD: From YAML to SMT-LIB -- Automating Verification of Operational Design Domains

Operational Design Domains (ODDs) define the conditions under which an Automated Driving System (ADS) is allowed to operate, while Current Operational Domains (CODs) capture the actual runtime situation. Ensuring that a COD instance lies within the ODD is a crucial step in safety assurance. Today, ODD and COD specifications are frequently expressed in YAML to remain accessible for stakeholders, but such descriptions are not directly suitable for solver-based verification. Manual translation into formal languages such as SMT-LIB is slow and error-prone. We present VeriODD, a tool that automates this translation. VeriODD uses ANTLR-based compiler technology to transform YAML-based ODD/COD specifications into both human-readable propositional logic, for lightweight review on a simple basis, and solver-ready SMT-LIB. The tool integrates with SMT solvers such as Z3 to provide automated consistency checks of ODD specifications and verification of COD conformance. A graphical user interface supports editing specifications, inspecting generated formulas, and performing verification with a single click. VeriODD thereby closes the gap between stakeholder-friendly ODD/COD notations and formal verification, enabling scalable and automated assurance of operational boundaries in autonomous driving. Video demonstration: https://youtu.be/odRacNoL_Pk Tool available at: https://github.com/BasselRafie/VeriODD

cs.SE↗

arXiv2025

LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations

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.

cs.SE↗

arXiv2025

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials

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).

physics.comp-ph↗

arXiv2025

A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting

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.

eess.IV↗

arXiv2025

Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural Software Architecture Inference

Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoning over these architectures, enabling neurosymbolic architectural reasoning. We discuss how this approach works in general and outline a research agenda based on six general research question that need to be addressed, to achieve this vision.

cs.SE↗

arXiv2025

Analysis of Patterns in Recorded Signals of Software Systems With a Variance Based Segmentation Algorithm

Due to the increasing complexity and interconnectedness of different components in modern automotive software systems there is a great number of interactions between these system components and their environment. These interactions result in unique temporal behaviors we call underlying scenarios. The signal data from all system components, which is recorded during runtime, can be processed and analyzed by observing changes in their runtime. Different system behaviors can be characterized by dividing the whole data spectrum into appropriate segments with consistent behavior, classifying these segments, and mapping them to different scenarios. These disjunctive scenarios can be analyzed for their specific behavior which may divert from the expected average system behavior. We state the emerging problem of data segmentation as follows: divide a multivariate data set into a suitable amount of segments with consistent internal behavior. The problem can be divided into 2 subproblems: "How many segments are present in the data set?", and "What are good segmentation indices for the underlying segments?". The complexity of the problem still needs to be assessed, however, at this point we expect it to be NP-hard, as both the number of segments and the segmentation points are unknown. We are in search of appropriate metrics to quantify the quality of a given segmentation of a whole data set. In this paper, we discuss the segmentation of multivariate data, but not the classification of segments into scenario classes. In the following, we investigate segmentation algorithms for solving the subproblem of finding suitable segmentation indices by constant amount of segments. The algorithms are investigated towards effectivity and efficiency by applying them to a data set taken out of a real system trace provided by our automotive partners.

stat.AP↗

arXiv2025

LLM-Based Design Pattern Detection

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.

cs.SE↗

arXiv2025

Software Process as a Service: Towards A Software Process Ecosystem

In large-scale projects operated in regulated environments, standard development processes are employed to meet strict compliance demands. Since such processes are usually complex, providing process users with access to their required process, which should be tailored to a project's needs is a challenging task that requires proper tool support. In this paper, we present a process ecosystem in which software processes are provided as web-based services. We outline the general idea, describe the modeling approach, and we illustrate the concept's realization using a proof-of-concept case based on a large software process line that is mandatory to use for IT projects in the German public sector. The suitability is evaluated with three experts that valued the improved accessibly and usability of the process and the end-user support tool.

cs.SE↗

arXiv2024

A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System

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.

cs.RO↗

arXiv2024

Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving

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.

cs.RO↗

arXiv2024

DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis

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.

cs.LG↗

arXiv2024

Enhancing Multiscale Simulations with Constitutive Relations-Aware Deep Operator Networks

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.

cs.LG↗

arXiv2024

Nonlinear model reduction for operator learning

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.

cs.LG↗

arXiv2024

Emergent Software Service Platform and its Application in a Smart Mobility Setting

The development dynamics of digital innovations for industry, business, and society are producing complex system conglomerates that can no longer be designed centrally and hierarchically in classic development processes. Instead, systems are evolving in DevOps processes in which heterogeneous actors act together on an open platform. Influencing and controlling such dynamically and autonomously changing system landscapes is currently a major challenge and a fundamental interest of service users and providers, as well as operators of the platform infrastructures. In this paper, we propose an architecture for such an emergent software service platform. A software platform that implements this architecture with the underlying engineering methodology is demonstrated by a smart parking lot scenario.

cs.SE↗