arXiv subjects

Franz Wotawa

Publications and source records attributed to Franz Wotawa.

17 recordsLinked to original sources

arXiv2026

DJPlus: Generating minimal test suites for strong coverage criteria in graph models

Automated test generation from graph models is essential to model-based testing. In this type of testing, graph coverage ensures test suite strength but also results in long test cases that take time to execute on the system under test. We propose a novel optimization-driven method, DJPlus, which generates reduced test suites while satisfying given graph-based test requirements. We implement DJPlus and show the feasibility of edge-pair criterion, a stronger coverage criterion than vertex or edge criteria, on four realistic systems, while prime path criterion poses scalability issues. Our evaluation reveals that the alternative methods generate 2 to 26 times more redundant test steps than DJPlus and DJPlus decreases test execution times by reducing the number of test steps. These results show that DJPlus is a positive step towards tackling the challenges of model-based testing at an industrial scale.

cs.SE↗

arXiv2025

Reproducibility of Machine Learning-Based Fault Detection and Diagnosis for HVAC Systems in Buildings: An Empirical Study

Reproducibility is a cornerstone of scientific research, enabling independent verification and validation of empirical findings. The topic gained prominence in fields such as psychology and medicine, where concerns about non - replicable results sparked ongoing discussions about research practices. In recent years, the fast-growing field of Machine Learning (ML) has become part of this discourse, as it faces similar concerns about transparency and reliability. Some reproducibility issues in ML research are shared with other fields, such as limited access to data and missing methodological details. In addition, ML introduces specific challenges, including inherent nondeterminism and computational constraints. While reproducibility issues are increasingly recognized by the ML community and its major conferences, less is known about how these challenges manifest in applied disciplines. This paper contributes to closing this gap by analyzing the transparency and reproducibility standards of ML applications in building energy systems. The results indicate that nearly all articles are not reproducible due to insufficient disclosure across key dimensions of reproducibility. 72% of the articles do not specify whether the dataset used is public, proprietary, or commercially available. Only two papers share a link to their code - one of which was broken. Two-thirds of the publications were authored exclusively by academic researchers, yet no significant differences in reproducibility were observed compared to publications with industry-affiliated authors. These findings highlight the need for targeted interventions, including reproducibility guidelines, training for researchers, and policies by journals and conferences that promote transparency and reproducibility.

cs.LG↗

arXiv2025

Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis

This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround organization of retinal ganglion cell receptive fields, we propose a frequency-domain feature extraction algorithm that enhances the detection of fault signatures in vibration signals. SFRFs are designed as antagonistic spectral filters centered on characteristic fault frequencies, with inhibitory surrounds that enable robust characterization of incipient faults under variable operating conditions. A multi-objective evolutionary optimization strategy based on NSGA-II algorithm is employed to tune the receptive field parameters by simultaneously minimizing RUL prediction error, maximizing feature monotonicity, and promoting smooth degradation trajectories. The method is demonstrated on the XJTU-SY bearing run-to-failure dataset, confirming its suitability for constructing condition indicators in health monitoring applications. Key contributions include: (i) the introduction of SFRFs, inspired by the biology of vision in the primate retina; (ii) an evolutionary optimization framework guided by condition monitoring and prognosis criteria; and (iii) experimental evidence supporting the detection of early-stage faults and their precursors. Furthermore, we confirm that our diagnosis-informed spectral representation achieves accurate RUL prediction using a bagging regressor. The results highlight the interpretability and principled design of SFRFs, bridging signal processing, biological sensing principles, and data-driven prognostics in rotating machinery.

cs.NE↗

arXiv2024

Generalizable Temperature Nowcasting with Physics-Constrained RNNs for Predictive Maintenance of Wind Turbine Components

Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating physics-based knowledge in neural networks to enforce their physical plausibilty is a promising method to improve current approaches, but incomplete system information often impedes their application in real world scenarios. We describe a simple and efficient way for physics-constrained deep learning-based predictive maintenance for wind turbine gearbox bearings with partial system knowledge. The approach is based on temperature nowcasting constrained by physics, where unknown system coefficients are treated as learnable neural network parameters. Results show improved generalization performance to unseen environments compared to a baseline neural network, which is especially important in low data scenarios often encountered in real-world applications.

cs.LG↗

arXiv2024

Investigating Reproducibility in Deep Learning-Based Software Fault Prediction

Over the past few years, deep learning methods have been applied for a wide range of Software Engineering (SE) tasks, including in particular for the important task of automatically predicting and localizing faults in software. With the rapid adoption of increasingly complex machine learning models, it however becomes more and more difficult for scholars to reproduce the results that are reported in the literature. This is in particular the case when the applied deep learning models and the evaluation methodology are not properly documented and when code and data are not shared. Given some recent -- and very worrying -- findings regarding reproducibility and progress in other areas of applied machine learning, the goal of this work is to analyze to what extent the field of software engineering, in particular in the area of software fault prediction, is plagued by similar problems. We have therefore conducted a systematic review of the current literature and examined the level of reproducibility of 56 research articles that were published between 2019 and 2022 in top-tier software engineering conferences. Our analysis revealed that scholars are apparently largely aware of the reproducibility problem, and about two thirds of the papers provide code for their proposed deep learning models. However, it turned out that in the vast majority of cases, crucial elements for reproducibility are missing, such as the code of the compared baselines, code for data pre-processing or code for hyperparameter tuning. In these cases, it therefore remains challenging to exactly reproduce the results in the current research literature. Overall, our meta-analysis therefore calls for improved research practices to ensure the reproducibility of machine-learning based research.

cs.SE↗

arXiv2024

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -- and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.

cs.CY↗

arXiv2021

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

cs.SE↗

arXiv2021

A framework for the automation of testing computer vision systems

Vision systems, i.e., systems that allow to detect and track objects in images, have gained substantial importance over the past decades. They are used in quality assurance applications, e.g., for finding surface defects in products during manufacturing, surveillance, but also automated driving, requiring reliable behavior. Interestingly, there is only little work on quality assurance and especially testing of vision systems in general. In this paper, we contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition. The framework makes use of existing libraries allowing to modify original images and to obtain similarities between the original and modified images. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces and present preliminary results from the image classification domain.

cs.SE↗

arXiv2021

Automatic Generation of Challenging Road Networks for ALKS Testing based on Bezier Curves and Search

In this paper, we outline an approach for automatic generation of challenging road networks for virtual testing of an automated lane keep system. Based on a set of control points, we construct a parametric curve that represents a road network, which defines the dynamic driving task an automated lane keep system equipped vehicle has to perform. Changing control points has global influence on the resulting road geometry. Our approach uses search to find a set of control points that results in a challenging road geometry, eventually forcing the vehicle to leave the intended path. We evaluated our approach in three different search-configurations regarding test efficiency and practical applicability for automatic virtual testing of an automated lane keep system.

cs.SE↗

arXiv2019

CIMAX: Collective Information Maximization in Robotic Swarms Using Local Communication

Robotic swarms and mobile sensor networks are used for environmental monitoring in various domains and areas of operation. Especially in otherwise inaccessible environments decentralized robotic swarms can be advantageous due to their high spatial resolution of measurements and resilience to failure of individuals in the swarm. However, such robotic swarms might need to be able to compensate misplacement during deployment or adapt to dynamical changes in the environment. Reaching a collective decision in a swarm with limited communication abilities without a central entity serving as decision-maker can be a challenging task. Here we present the CIMAX algorithm for collective decision making for maximizing the information gathered by the swarm as a whole. Agents negotiate based on their individual sensor readings and ultimately make a decision for collectively moving in a particular direction so that the swarm as a whole increases the amount of relevant measurements and thus accessible information. We use both simulation and real robotic experiments for presenting, testing and validating our algorithm. CIMAX is designed to be used in underwater swarm robots for troubleshooting an oxygen depletion phenomenon known as "anoxia".

cs.MA↗

arXiv2018

On the Refinement of Spreadsheet Smells by means of Structure Information

Spreadsheet users are often unaware of the risks imposed by poorly designed spreadsheets. One way to assess spreadsheet quality is to detect smells which attempt to identify parts of spreadsheets that are hard to comprehend or maintain and which are more likely to be the root source of bugs. Unfortunately, current spreadsheet smell detection techniques suffer from a number of drawbacks that lead to incorrect or redundant smell reports. For example, the same quality issue is often reported for every copy of a cell, which may overwhelm users. To deal with these issues, we propose to refine spreadsheet smells by exploiting inferred structural information for smell detection. We therefore first provide a detailed description of our static analysis approach to infer clusters and blocks of related cells. We then elaborate on how to improve existing smells by providing three example refinements of existing smells that incorporate information about cell groups and computation blocks. Furthermore, we propose three novel smell detection techniques that make use of the inferred spreadsheet structures. Empirical evaluation of the proposed techniques suggests that the refinements successfully reduce the number of incorrectly and redundantly reported smells, and novel deficits are revealed by the newly introduced smells.

cs.SE↗

arXiv2018

Controlling Swarms: A Programming Paradigm with Minimalistic Communication

Inspired by natural swarms, numerous control schemes enabling robotic swarms, mobile sensor networks and other multi-agent systems to exhibit various self-organized behaviors have been suggested. In this work, we present a Wave Oriented Swarm Programming Paradigm (WOSPP) enabling the control of swarms with minimalistic communication bandwidth in a simple manner, yet allowing the emergence of diverse complex behaviors and autonomy of the swarm. Communi cation in the proposed paradigm is based on "ping"-signals inspired by strategies for communication and self organization of slime mold (dictyostelium discoideum) and fireflies (lampyridae). Signals propagate as information-waves throughout the swarm. We show that even with 1-bit bandwidth communication between agents suffices for the design of a substantial set of behaviors in the domain of essential behaviors of a collective. Ultimately, the reader will be enabled to develop and design a control scheme for individual swarms.

cs.MA↗

arXiv2018

Combining Spreadsheet Smells for Improved Fault Prediction

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.

cs.SE↗

arXiv2018

A Formal TLS Handshake Model in LNT

Testing of network services represents one of the biggest challenges in cyber security. Because new vulnerabilities are detected on a regular basis, more research is needed. These faults have their roots in the software development cycle or because of intrinsic leaks in the system specification. Conformance testing checks whether a system behaves according to its specification. Here model-based testing provides several methods for automated detection of shortcomings. The formal specification of a system behavior represents the starting point of the testing process. In this paper, a widely used cryptographic protocol is specified and tested for conformance with a test execution framework. The first empirical results are presented and discussed.

cs.CR↗

arXiv2012

Using Constraints for Equivalent Mutant Detection

In mutation testing the question whether a mutant is equivalent to its program is important in order to compute the correct mutation score. Unfortunately, answering this question is not always possible and can hardly be obtained just by having a look at the program's structure. In this paper we introduce a method for solving the equivalent mutant problem using a constraint representation of the program and its mutant. In particularly the approach is based on distinguishing test cases, i.e., test inputs that force the program and its mutant to behave in a different way. Beside the foundations of the approach, in this paper we also present the algorithms and first empirical results.

cs.SE↗

arXiv2003

Modeling State in Software Debugging of VHDL-RTL Designs -- A Model-Based Diagnosis Approach

In this paper we outline an approach of applying model-based diagnosis to the field of automatic software debugging of hardware designs. We present our value-level model for debugging VHDL-RTL designs and show how to localize the erroneous component responsible for an observed misbehavior. Furthermore, we discuss an extension of our model that supports the debugging of sequential circuits, not only at a given point in time, but also allows for considering the temporal behavior of VHDL-RTL designs. The introduced model is capable of handling state inherently present in every sequential circuit. The principal applicability of the new model is outlined briefly and we use industrial-sized real world examples from the ISCAS'85 benchmark suite to discuss the scalability of our approach.

cs.AI↗

arXiv2000

Extended Abstract - Model-Based Debugging of Java Programs

Model-based reasoning is a central concept in current research into intelligent diagnostic systems. It is based on the assumption that sources of incorrect behavior in technical devices can be located and identified via the existence of a model describing the basic properties of components of a certain application domain. When actual data concerning the misbehavior of a system composed from such components is available, a domain-independent diagnosis engine can be used to infer which parts of the system contribute to the observed behavior. This paper describes the application of the model-based approach to the debugging of Java programs written in a subset of Java. We show how a simple dependency model can be derived from a program, demonstrate the use of the model for debugging and reducing the required user interactions, give a comparison of the functional dependency model with program slicing, and finally discuss some current research issues.

cs.SE↗