arXiv subjects

Thomas Vogel

Publications and source records attributed to Thomas Vogel.

At least 19 recordsLinked to original sources

arXiv2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

cond-mat.mtrl-sci↗

arXiv2026

Accelerating Multicanonical Sampling with Irreversibility

Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding ground states or studying phase transitions in complex systems in statistical physics. However, their efficiency can be limited by the time to attain the desired flat distribution, which is generally unknown prior to the simulations. In particular, they might suffer from slowing down towards the end of a simulation due to the diffusive nature of random walks. In this work we apply irreversibility to the multicanonical Monte Carlo method via the lifting approach to alleviate this behavior. We achieve a 2-4 times speedup in ground-state search for a two-dimensional (2D) Ising model, and up to an order of magnitude of speedup for finding the ground-state energy in an Edwards-Anderson spin glass, compared to traditional multicanonical sampling. The round-trip times between ground states show a narrower distribution and are significantly shorter compared to the reversible counterpart, suggesting that a lower convergence time with a smaller time variance is feasible.

cond-mat.stat-mech↗

arXiv2025

Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer

We use a semi-supervised, neural-network based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole-dipole and Lennard-Jones interactions. As input for the neural network we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang--Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant, conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations.

cond-mat.soft↗

arXiv2024

Formal Synthesis of Uncertainty Reduction Controllers

In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce the uncertainty affecting SAS (other than through the blanket monitoring of their components and environment) remain underexplored. Our paper proposes a more nuanced, adaptive approach to SAS uncertainty reduction. To that end, we introduce a SAS architecture comprising an uncertainty reduction controller that drives the adaptive acquisition of new information within the SAS adaptation loop, and a tool-supported method that uses probabilistic model checking to synthesise such controllers. The controllers generated by our method deliver optimal trade-offs between SAS uncertainty reduction benefits and new information acquisition costs. We illustrate the use and evaluate the effectiveness of our approach for mobile robot navigation and server infrastructure management SAS.

cs.SE↗

arXiv2023

A User Study for Evaluation of Formal Verification Results and their Explanation at Bosch

Context: Ensuring safety for any sophisticated system is getting more complex due to the rising number of features and functionalities. This calls for formal methods to entrust confidence in such systems. Nevertheless, using formal methods in industry is demanding because of their lack of usability and the difficulty of understanding verification results. Objective: We evaluate the acceptance of formal methods by Bosch automotive engineers, particularly whether the difficulty of understanding verification results can be reduced. Method: We perform two different exploratory studies. First, we conduct a user survey to explore challenges in identifying inconsistent specifications and using formal methods by Bosch automotive engineers. Second, we perform a one-group pretest-posttest experiment to collect impressions from Bosch engineers familiar with formal methods to evaluate whether understanding verification results is simplified by our counterexample explanation approach. Results: The results from the user survey indicate that identifying refinement inconsistencies, understanding formal notations, and interpreting verification results are challenging. Nevertheless, engineers are still interested in using formal methods in real-world development processes because it could reduce the manual effort for verification. Additionally, they also believe formal methods could make the system safer. Furthermore, the one-group pretest-posttest experiment results indicate that engineers are more comfortable understanding the counterexample explanation than the raw model checker output. Limitations: The main limitation of this study is the generalizability beyond the target group of Bosch automotive engineers.

cs.SE↗

arXiv2023

Runtime Verification of Self-Adaptive Systems with Changing Requirements

To accurately make adaptation decisions, a self-adaptive system needs precise means to analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to check that the managed system satisfies its requirements formalized as temporal-logic properties. These requirements, however, may change due to system evolution or uncertainty in the environment, managed system, and requirements themselves. Thus, the properties under investigation by the runtime verification have to be dynamically adapted to represent the changing requirements while preserving the knowledge about requirements satisfaction gathered thus far, all with minimal latency. To address this need, we present a runtime verification approach for self-adaptive systems with changing requirements. Our approach uses property specification patterns to automatically obtain automata with precise semantics that are the basis for runtime verification. The automata can be safely adapted during runtime verification while preserving intermediate verification results to seamlessly reflect requirement changes and enable continuous verification. We evaluate our approach on an Arduino prototype of the Body Sensor Network and the Timescales benchmark. Results show that our approach is over five times faster than the typical approach of redeploying and restarting runtime monitors to reflect requirements changes, while improving the system's trustworthiness by avoiding interruptions of verification.

cs.SE↗

arXiv2023

On the Need for Artifacts to Support Research on Self-Adaptation Mature for Industrial Adoption

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community.

cs.SE↗

arXiv2022

A Property Specification Pattern Catalog for Real-Time System Verification with UPPAAL

Context: The goal of specification pattern catalogs for real-time requirements is to mask the complexity of specifying such requirements in a timed temporal logic for verification. For this purpose, they provide frontends to express and translate pattern-based natural language requirements to formulae in a suitable logic. However, the widely used real-time model checking tool UPPAAL only supports a restricted subset of those formulae that focus only on basic and non-nested reachability, safety, and liveness properties. This restriction renders many specification patterns inapplicable. As a workaround, timed observer automata need to be constructed manually to express sophisticated requirements envisioned by these patterns. Objective: In this work, we fill these gaps by providing a comprehensive specification pattern catalog for UPPAAL. The catalog supports qualitative and real-time requirements and covers all corresponding patterns of existing catalogs. Method: The catalog we propose is integrated with UPPAAL. It supports the specification of qualitative and real-time requirements using patterns and provides an automated generator that translates these requirements to observer automata and TCTL formulae. The resulting artifacts are used for verifying systems in UPPAAL. Thus, our catalog enables an automated end-to-end verification process for UPPAAL based on property specification patterns and observer automata. Results: We evaluate our catalog on three UPPAAL system models reported in the literature and mostly applied in an industrial setting. As a result, not only the reproducibility of the related UPPAAL models was possible, but also the validation of an automated, seamless, and accurate pattern- and observer-based verification process. Conclusion: The proposed property specification pattern catalog for UPPAAL enables practitioners to specify qualitative and real-time requirements...

cs.SE↗

arXiv2022

Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation

Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introduced to support research in the field of self-adaptive systems. While these artifacts have shown their value, it is not clear to what extent these artifacts support research on problems in self-adaptation that are relevant to industry. This paper provides a set of guidelines for artifacts that aim at supporting industry-relevant research on self-adaptation. The guidelines that are grounded on data obtained from a survey with practitioners were derived during working sessions at the 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. Artifact providers can use the guidelines for aligning future artifacts with industry needs; they can also be used to evaluate the industrial relevance of existing artifacts. We also propose an artifact template.

cs.SE↗

arXiv2022

Supervised and Unsupervised Machine Learning of Structural Phases of Polymers Adsorbed to Nanowires

We identify configurational phases and structural transitions in a polymer nanotube composite by means of machine learning. We employ various unsupervised dimensionality reduction methods, conventional neural networks, as well as the confusion method, an unsupervised neural-network-based approach. We find neural networks are able to reliably recognize all configurational phases that have been found previously in experiment and simulation. Furthermore, we locate the boundaries between configurational phases in a way that removes human intuition or bias. This could be done before only by relying on preconceived, ad-hoc order parameters.

cond-mat.soft↗

arXiv2022

VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python

Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniques should achieve a high accuracy, point developers directly to the vulnerable code fragments, scale to real-world software, generalize across the boundaries of a specific software project, and require no or only moderate setup or configuration effort. Method: In this article, we present VUDENC (Vulnerability Detection with Deep Learning on a Natural Codebase), a deep learning-based vulnerability detection tool that automatically learns features of vulnerable code from a large and real-world Python codebase. VUDENC applies a word2vec model to identify semantically similar code tokens and to provide a vector representation. A network of long-short-term memory cells (LSTM) is then used to classify vulnerable code token sequences at a fine-grained level, highlight the specific areas in the source code that are likely to contain vulnerabilities, and provide confidence levels for its predictions. Results: To evaluate VUDENC, we used 1,009 vulnerability-fixing commits from different GitHub repositories that contain seven different types of vulnerabilities (SQL injection, XSS, Command injection, XSRF, Remote code execution, Path disclosure, Open redirect) for training. In the experimental evaluation, VUDENC achieves a recall of 78%-87%, a precision of 82%-96%, and an F1 score of 80%-90%. VUDENC's code, the datasets for the vulnerabilities, and the Python corpus for the word2vec model are available for reproduction. Conclusions: Our experimental results suggest...

cs.CR↗

arXiv2022

A systematic literature review on counterexample explanation

Context: Safety is of paramount importance for cyber-physical systems in domains such as automotive, robotics, and avionics. Formal methods such as model checking are one way to ensure the safety of cyber-physical systems. However, adoption of formal methods in industry is hindered by usability issues, particularly the difficulty of understanding model checking results. Objective: We want to provide an overview of the state of the art for counterexample explanation by investigating the contexts, techniques, and evaluation of research approaches in this field. This overview shall provide an understanding of current and guide future research. Method: To provide this overview, we conducted a systematic literature review. The survey comprises 116 publications that address counterexample explanations for model checking. Results: Most primary studies provide counterexample explanations graphically or as traces, minimize counterexamples to reduce complexity, localize errors in the models expressed in the input formats of model checkers, support linear temporal logic or computation tree logic specifications, and use model checkers of the Symbolic Model Verifier family. Several studies evaluate their approaches in safety-critical domains with industrial applications. Conclusion: We notably see a lack of research on counterexample explanation that targets probabilistic and real-time systems, leverages the explanations to domain-specific models, and evaluates approaches in user studies. We conclude by discussing the adequacy of different types of explanations for users with varying domain and formal methods expertise, showing the need to support laypersons in understanding model checking results to increase adoption of formal methods in industry.

cs.SE↗

arXiv2021

A User-Study Protocol for Evaluation of Formal Verification Results and their Explanation

Context: The complexity of modern safety-critical systems in industries keep on increasing due to the rising number of features and functionalities. This calls for formal methods in order to entrust confidence in such systems. Nevertheless, using formal methods in industry is demanding because of usability issues, e.g., the difficulty of understanding model checking results. Thus the hypothesis is, presenting the result of model checker results in a user-friendly manner could promote formal methods usage in industries. Objective: We aim to evaluate the acceptance of formal methods by engineers if the complexity of understanding verification results is made easy. Method: We perform two different exploratory studies. First, we conduct an online survey to explore challenges in identifying inconsistent specifications and using formal methods from engineers. Second, we perform a one group pretest and posttest experiment to collect impressions from engineers using formal methods if understanding verification results is eased. Limitations: The main limitation of this study is the generalization because the survey focuses on a particular target group and it uses a pre-experimental design.

cs.SE↗

arXiv2021

How do we Evaluate Self-adaptive Software Systems?

With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated. Such insights will support researchers to better compare new findings when developing new knowledge for the community. However, so far there is no clear overview of how evaluations are performed in self-adaptive systems. To address this gap, we conduct a mapping study. The study focuses on experimental evaluations published in the last decade at the prime venue of research in software engineering for self-adaptive systems -- the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Results point out that specifics of self-adaptive systems require special attention in the experimental process, including the distinction of the managing system (i.e., the target of evaluation) and the managed system, the presence of uncertainties that affect the system behavior and hence need to be taken into account in data analysis, and the potential of managed systems to be reused across experiments, beyond replications. To conclude, we offer a set of suggestions derived from our study that can be used as input to enhance future experiments in self-adaptive systems.

cs.SE↗

arXiv2020

A Comprehensive Empirical Evaluation of Generating Test Suites for Mobile Applications with Diversity

Context: In search-based software engineering we often use popular heuristics with default configurations, which typically lead to suboptimal results, or we perform experiments to identify configurations on a trial-and-error basis, which may lead to better results for a specific problem. We consider the problem of generating test suites for mobile applications (apps) and rely on \Sapienz, a state-of-the-art approach to this problem that uses a popular heuristic (NSGA-II) with a default configuration. Objective: We want to achieve better results in generating test suites with \Sapienz while avoiding trial-and-error experiments to identify a more suitable configuration of \Sapienz. Method: We conducted a fitness landscape analysis of \Sapienz to analytically understand the search problem, which allowed us to make informed decisions about the heuristic and configuration of \Sapienz when developing \SapienzDiv. We comprehensively evaluated \SapienzDiv in a head-to-head comparison with \Sapienz on 34 apps. Results: Analyzing the fitness landscape of \Sapienz, we observed a lack of diversity of the evolved test suites and a stagnation of the search after 25 generations. \SapienzDiv realizes mechanisms that preserve the diversity of the test suites being evolved. The evaluation showed that \SapienzDiv achieves better or at least similar test results than \Sapienz concerning coverage and the number of revealed faults. However, \SapienzDiv typically produces longer test sequences and requires more execution time than \Sapienz. Conclusions: The understanding of the search problem obtained by the fitness landscape analysis helped us to find a more suitable configuration of \Sapienz without trial-and-error experiments. By promoting diversity of test suites during the search, improved or at least similar test results in terms of faults and coverage can be achieved.

cs.SE↗

arXiv2020

Evolutionary Grammar-Based Fuzzing

A fuzzer provides randomly generated inputs to a targeted software to expose erroneous behavior. To efficiently detect defects, generated inputs should conform to the structure of the input format and thus, grammars can be used to generate syntactically correct inputs. In this context, fuzzing can be guided by probabilities attached to competing rules in the grammar, leading to the idea of probabilistic grammar-based fuzzing. However, the optimal assignment of probabilities to individual grammar rules to effectively expose erroneous behavior for individual systems under test is an open research question. In this paper, we present EvoGFuzz, an evolutionary grammar-based fuzzing approach to optimize the probabilities to generate test inputs that may be more likely to trigger exceptional behavior. The evaluation shows the effectiveness of EvoGFuzz in detecting defects compared to probabilistic grammar-based fuzzing (baseline). Applied to ten real-world applications with common input formats (JSON, JavaScript, or CSS3), the evaluation shows that EvoGFuzz achieved a significantly larger median line coverage for all subjects by up to 48% compared to the baseline. Moreover, EvoGFuzz managed to expose 11 unique defects, from which five have not been detected by the baseline.

cs.SE↗

arXiv2020

Bet and Run for Test Case Generation

Anyone working in the technology sector is probably familiar with the question: "Have you tried turning it off and on again?", as this is usually the default question asked by tech support. Similarly, it is known in search based testing that metaheuristics might get trapped in a plateau during a search. As a human, one can look at the gradient of the fitness curve and decide to restart the search, so as to hopefully improve the results of the optimization with the next run. Trying to automate such a restart, it has to be programmatically decided whether the metaheuristic has encountered a plateau yet, which is an inherently difficult problem. To mitigate this problem in the context of theoretical search problems, the Bet and Run strategy was developed, where multiple algorithm instances are started concurrently, and after some time all but the single most promising instance in terms of fitness values are killed. In this paper, we adopt and evaluate the Bet and Run strategy for the problem of test case generation. Our work indicates that use of this restart strategy does not generally lead to gains in the quality metrics, when instantiated with the best parameters found in the literature.

cs.SE↗

arXiv2020

Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches.

cs.SE↗