Falk Howar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Falk Howar has a total of 137 co-authors. Here are the top co-authors by number of shared publications:
Bernhard Steffen
TU Dortmund University
31 shared publicationsMaik Merten
16 shared publications- Malte Mues
TU Dortmund University
13 shared publications - Malte Isberner
TU Dortmund University
12 shared publications Bengt Jonsson
Uppsala University
10 shared publications- Bernhard Steffen
TU Dortmund University
9 shared publications
Latest Publications
- 7 October 2026
- Published by Springer Nature
- p. 472-479
Automatic Clutch: An Industrial Experience with Predictive Autoscaling as a Diagnostic Instrument
- 7 October 2026
- Published by Springer Nature
- p. 472-479
Automatic Clutch: An Industrial Experience with Predictive Autoscaling as a Diagnostic Instrument
- 7 October 2026
- Published by Springer Nature
- p. 11-27
ARRC: Evidence-Linked, Workflow-Integrated Recommender for Resource Optimization Across the Edge–Cloud Continuum
- 7 October 2026
- Published by Springer Nature
- p. 11-27
ARRC: Evidence-Linked, Workflow-Integrated Recommender for Resource Optimization Across the Edge–Cloud Continuum
- Open Access
- 29 August 2026
- Published by Association for Computing Machinery (ACM)
- p. 643-647
Exploring LLM-generated hints for students’ Error Recovery in programming
- 29 August 2026
- Published by Association for Computing Machinery (ACM)
- p. 643-647
Open AccessExploring LLM-generated hints for students’ Error Recovery in programming
- Open Access
- 3 August 2026
- Published by Springer Nature in Journal of Automated Reasoning
- Vol. 70 (2) , 14
${SL}^{\lambda }$: A Scalable Algorithm for Register Automata LearningExisting active automata learning (AAL) algorithms have demonstrated their potential in capturing the behavior of complex systems (e.g., in analyzing network protocol implementations). The most widely used AAL algorithms generate finite state machine models, such as Mealy machines or deterministic finite automata. For many analysis tasks, however, it is crucial to generate richer classes of models that also show how relations between data parameters affect system behavior. Such models have shown potential to uncover critical bugs, but their learning algorithms do not scale beyond small and well curated experiments. In this article, we present $${SL}^{\lambda }$$ , an effective and scalable register automata (RA) learning algorithm that significantly reduces the number of membership queries required for inferring models. It achieves this by combining a tree-based cost-efficient data structure with mechanisms for computing short and restricted tests. We prove that $${SL}^{\lambda }$$ is guaranteed to learn an acceptor, in the form of a register automaton with n locations and t transitions, for a given data language of finite index, and that it can do so with at most $$O(t^2 \, (2n)^n + m t^2 \, m^m)$$ membership queries and O ( t ) equivalence queries, where m is the length of the longest counterexample received during learning. We have implemented $${SL}^{\lambda }$$ as a new algorithm in RALib. We evaluate its performance by comparing it against $${SL}^{*}$$ , the current state-of-the-art RA learning algorithm. Experiments on a series of benchmarks show that it reduces the number of membership queries by up to an order of magnitude, and also shows substantial asymptotic improvements in bigger systems.- 3 August 2026
- Published by Springer Nature in Journal of Automated Reasoning
- Vol. 70 (2) , 14
Open Access${SL}^{\lambda }$: A Scalable Algorithm for Register Automata LearningExisting active automata learning (AAL) algorithms have demonstrated their potential in capturing the behavior of complex systems (e.g., in analyzing network protocol implementations). The most widely used AAL algorithms generate finite state machine models, such as Mealy machines or deterministic finite automata. For many analysis tasks, however, it is crucial to generate richer classes of models that also show how relations between data parameters affect system behavior. Such models have shown potential to uncover critical bugs, but their learning algorithms do not scale beyond small and well curated experiments. In this article, we present $${SL}^{\lambda }$$ , an effective and scalable register automata (RA) learning algorithm that significantly reduces the number of membership queries required for inferring models. It achieves this by combining a tree-based cost-efficient data structure with mechanisms for computing short and restricted tests. We prove that $${SL}^{\lambda }$$ is guaranteed to learn an acceptor, in the form of a register automaton with n locations and t transitions, for a given data language of finite index, and that it can do so with at most $$O(t^2 \, (2n)^n + m t^2 \, m^m)$$ membership queries and O ( t ) equivalence queries, where m is the length of the longest counterexample received during learning. We have implemented $${SL}^{\lambda }$$ as a new algorithm in RALib. We evaluate its performance by comparing it against $${SL}^{*}$$ , the current state-of-the-art RA learning algorithm. Experiments on a series of benchmarks show that it reduces the number of membership queries by up to an order of magnitude, and also shows substantial asymptotic improvements in bigger systems. - 12 June 2026
- Published by Springer Nature
- p. 170-191
Learning Probabilistic Automata from Single Continuous-Valued System Logs
- 12 June 2026
- Published by Springer Nature
- p. 170-191
Learning Probabilistic Automata from Single Continuous-Valued System Logs
- Open Access
- 18 May 2026
- Published by Springer Nature
- p. 130-150
Test Coverage of Automated Robotic Systems in Open World Environments
Automated robotic systems operating in real-world environments require thorough test campaigns before deployment, in which engineers assess whether the system is actually exposed to critical scenarios. A well-established way to judge the efficacy of test campaigns in controlled environments is scenario coverage: It quantifies the quality of recorded data from test campaigns w.r.t. a set of (critical) scenario classes of interest. Challenges arise when transferring coverage from controlled to open environments, as it may no longer be exactly determined to which scenario class the recorded test data belongs to: sensors offer limited observability, e.g., by occlusions or hardware failures, and specifications might be inherently vague, e.g., via imprecise traffic regulations. We leverage the Open World Assumption to formally extend scenario coverage to such ambiguous test data and present algorithms for computing guaranteed lower and upper bounds on coverage. Whereas deciding the lower bound problem is $$D^p$$ -complete, the upper bound can be computed in polynomial time. We extend an existing coverage software tool with two approaches for incorporating the Open World Assumption, grounded in LTL f . Our evaluation shows that meaningful statements on scenario coverage are feasible, even under intricacies of the real world.- 18 May 2026
- Published by Springer Nature
- p. 130-150
Open AccessTest Coverage of Automated Robotic Systems in Open World Environments
Automated robotic systems operating in real-world environments require thorough test campaigns before deployment, in which engineers assess whether the system is actually exposed to critical scenarios. A well-established way to judge the efficacy of test campaigns in controlled environments is scenario coverage: It quantifies the quality of recorded data from test campaigns w.r.t. a set of (critical) scenario classes of interest. Challenges arise when transferring coverage from controlled to open environments, as it may no longer be exactly determined to which scenario class the recorded test data belongs to: sensors offer limited observability, e.g., by occlusions or hardware failures, and specifications might be inherently vague, e.g., via imprecise traffic regulations. We leverage the Open World Assumption to formally extend scenario coverage to such ambiguous test data and present algorithms for computing guaranteed lower and upper bounds on coverage. Whereas deciding the lower bound problem is $$D^p$$ -complete, the upper bound can be computed in polynomial time. We extend an existing coverage software tool with two approaches for incorporating the Open World Assumption, grounded in LTL f . Our evaluation shows that meaningful statements on scenario coverage are feasible, even under intricacies of the real world. - Open Access
- 20 April 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 14, 64191-64201
GMB-ECC: Guided Measuring and Benchmarking of the Edge Cloud Continuum
Modern edge–cloud deployments integrate devices, edge servers, private networks, and cloud platforms into heterogeneous, multi-layer systems. Each layer exposes distinct metrics, measurement granularities, and time scales, which complicates system-wide reasoning about energy use. Coordinated cross-layer optimization can reduce energy consumption, but assessing efficiency is difficult because metrics are incompatible and measurements are noisy. Practitioners must navigate efficiency trade-offs when selecting among alternatives, yet often rely on isolated measurements and system-specific heuristics. This paper introduces GMB-ECC (Guided Measuring and Benchmarking of the Edge–Cloud Continuum). GMB-ECC is a framework for precision-aware energy-efficiency benchmarking in heterogeneous edge–cloud systems. It models deployments as directed acyclic graphs that enforce conservation when aggregating energy and work metrics. The framework derives normalized efficiency measures with uncertainty bounds and ranks optimization opportunities by gap confidence, energy shares, and remediation costs. We evaluate GMB-ECC in a synthetic intra-logistics scenario and a live industrial deployment. The industrial deployment includes autonomous mobile robots, an edge server, and a private 5G network. Our framework-guided changes reduced total service energy consumption by 12% relative to an energy-unaware baseline while meeting latency and safety constraints. These results indicate that precision-aware benchmarking can support cross-layer energy optimization in heterogeneous edge–cloud environments.
- 20 April 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 14, 64191-64201
Open AccessGMB-ECC: Guided Measuring and Benchmarking of the Edge Cloud Continuum
Modern edge–cloud deployments integrate devices, edge servers, private networks, and cloud platforms into heterogeneous, multi-layer systems. Each layer exposes distinct metrics, measurement granularities, and time scales, which complicates system-wide reasoning about energy use. Coordinated cross-layer optimization can reduce energy consumption, but assessing efficiency is difficult because metrics are incompatible and measurements are noisy. Practitioners must navigate efficiency trade-offs when selecting among alternatives, yet often rely on isolated measurements and system-specific heuristics. This paper introduces GMB-ECC (Guided Measuring and Benchmarking of the Edge–Cloud Continuum). GMB-ECC is a framework for precision-aware energy-efficiency benchmarking in heterogeneous edge–cloud systems. It models deployments as directed acyclic graphs that enforce conservation when aggregating energy and work metrics. The framework derives normalized efficiency measures with uncertainty bounds and ranks optimization opportunities by gap confidence, energy shares, and remediation costs. We evaluate GMB-ECC in a synthetic intra-logistics scenario and a live industrial deployment. The industrial deployment includes autonomous mobile robots, an edge server, and a private 5G network. Our framework-guided changes reduced total service energy consumption by 12% relative to an energy-unaware baseline while meeting latency and safety constraints. These results indicate that precision-aware benchmarking can support cross-layer energy optimization in heterogeneous edge–cloud environments.
- Open Access
- 28 October 2025
- Published by Springer Nature
- p. 229-235
Let’s Talk AI with Computer Science Expert Falk Howar
We must ensure reasonable risk before deploying AI systems at a societal scale. My personal AI mission: I work on methods for ensuring that AI systems, autonomous vehicles in particular, are safe.
- 28 October 2025
- Published by Springer Nature
- p. 229-235
Open AccessLet’s Talk AI with Computer Science Expert Falk Howar
We must ensure reasonable risk before deploying AI systems at a societal scale. My personal AI mission: I work on methods for ensuring that AI systems, autonomous vehicles in particular, are safe.
- 30 September 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-8
A Qualitative Analysis of the German Road Traffic Regulations (StVO) for Automated Driving
An Automated Driving Systems (ADS) must comply with traffic regulations that are still primarily written for human drivers, such as the German Road Traffic Regulations (StVO). Automatically assessing legal compliance remains a key challenge, as existing formalization approaches typically cover only limited rule subsets. In this paper, we present a comprehensive qualitative analysis of the StVO, focusing on regulations relevant to passenger vehicles. Using inductive coding, we categorize legal concepts, quantify the relevant share of the regulation that apply to ADSs, and identify recurring sentence structures. The analysis highlights structural limitations such as vague formulations, implicit assumptions, and missing quantification that hinder formalization and verification of ADSs. Based on our results, we motivate recommendations for future legislation that is better suited for automated interpretation and monitoring.
- 30 September 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-8
A Qualitative Analysis of the German Road Traffic Regulations (StVO) for Automated Driving
An Automated Driving Systems (ADS) must comply with traffic regulations that are still primarily written for human drivers, such as the German Road Traffic Regulations (StVO). Automatically assessing legal compliance remains a key challenge, as existing formalization approaches typically cover only limited rule subsets. In this paper, we present a comprehensive qualitative analysis of the StVO, focusing on regulations relevant to passenger vehicles. Using inductive coding, we categorize legal concepts, quantify the relevant share of the regulation that apply to ADSs, and identify recurring sentence structures. The analysis highlights structural limitations such as vague formulations, implicit assumptions, and missing quantification that hinder formalization and verification of ADSs. Based on our results, we motivate recommendations for future legislation that is better suited for automated interpretation and monitoring.
- 29 August 2025
- Published by Springer Nature
- p. 135-153
Unsupervised Automata Learning via Discrete Optimization
- 29 August 2025
- Published by Springer Nature
- p. 135-153
Unsupervised Automata Learning via Discrete Optimization