Institution profile

fortiss GmbH

Academic institutioneurope · de
Official website
Research library22linked papers
Opportunities0open roles
Selected work

Representative Papers

Replications, Revisions, and Reanalyses: Managing Empirical Evidence in Software Engineering

Sep 05, 2026

One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study. However, research synthesis in SE is rare and if done mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations. We aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution. We apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence. The framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research. The framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.

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White paper: A perspective on civilian-to-defence research transfer to SDD

Aug 10, 2026

This study addresses a fundamental lifecycle mismatch between decade-long defense platform acquisition cycles and the rapid evolution of AI and software systems, which often require updates within hours or days. To resolve this tension, the paper proposes a “Software-Defined Defense” (SDD) framework that systematically adapts mature commercial practices—including DevOps, model-based systems engineering, and edge computing—to defense contexts. The SDD framework establishes a continuous, integrated loop spanning systems engineering, AI engineering, and connected infrastructure, enabling tactical, low-power edge execution, continuous compliance, variability management, and assured AI trustworthiness and sovereignty in contested environments. The work outlines short-, medium-, and long-term validation pathways and fosters collaboration among research, industry, policy, and defense organizations, leveraging existing capabilities from automotive, manufacturing, and aerospace sectors to advance SDD certification and deployment under operational conditions.

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Building a Low-cost Network Digital Twin for the IoT-Edge-Cloud Continuum Using Open-Source Tooling

Jun 23, 2026

This work addresses the challenge of validating network configurations and testing faults in IoT-edge-cloud environments without disrupting operational networks. The authors propose a low-cost, fully open-source network digital twin system that integrates Containerlab, Open vSwitch, ONOS, and Prometheus+Grafana to create a high-fidelity, low-overhead, end-to-end deployable artifact. This system enables real-time telemetry and SDN-based traffic scheduling tailored for industrial IoT (IIoT) edge scenarios. Evaluated on a physical Raspberry Pi-based edge WLAN testbed, the digital twin accurately replicates real network behavior, exhibiting a median RTT deviation of only 0.4 ms and a UDP throughput error of merely 0.03 Mbps. Furthermore, it successfully identifies virtualization-induced discrepancies in TCP throughput and packet loss.

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Recent publications

Latest Papers

Replications, Revisions, and Reanalyses: Managing Empirical Evidence in Software Engineering

Sep 05, 2026

One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study. However, research synthesis in SE is rare and if done mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations. We aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution. We apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence. The framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research. The framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.

0 citationsRead paper

White paper: A perspective on civilian-to-defence research transfer to SDD

Aug 10, 2026

This study addresses a fundamental lifecycle mismatch between decade-long defense platform acquisition cycles and the rapid evolution of AI and software systems, which often require updates within hours or days. To resolve this tension, the paper proposes a “Software-Defined Defense” (SDD) framework that systematically adapts mature commercial practices—including DevOps, model-based systems engineering, and edge computing—to defense contexts. The SDD framework establishes a continuous, integrated loop spanning systems engineering, AI engineering, and connected infrastructure, enabling tactical, low-power edge execution, continuous compliance, variability management, and assured AI trustworthiness and sovereignty in contested environments. The work outlines short-, medium-, and long-term validation pathways and fosters collaboration among research, industry, policy, and defense organizations, leveraging existing capabilities from automotive, manufacturing, and aerospace sectors to advance SDD certification and deployment under operational conditions.

0 citationsRead paper

Building a Low-cost Network Digital Twin for the IoT-Edge-Cloud Continuum Using Open-Source Tooling

Jun 23, 2026

This work addresses the challenge of validating network configurations and testing faults in IoT-edge-cloud environments without disrupting operational networks. The authors propose a low-cost, fully open-source network digital twin system that integrates Containerlab, Open vSwitch, ONOS, and Prometheus+Grafana to create a high-fidelity, low-overhead, end-to-end deployable artifact. This system enables real-time telemetry and SDN-based traffic scheduling tailored for industrial IoT (IIoT) edge scenarios. Evaluated on a physical Raspberry Pi-based edge WLAN testbed, the digital twin accurately replicates real network behavior, exhibiting a median RTT deviation of only 0.4 ms and a UDP throughput error of merely 0.03 Mbps. Furthermore, it successfully identifies virtualization-induced discrepancies in TCP throughput and packet loss.

0 citationsRead paper