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20242026
most citedReflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.SE2026

RefVerifier: Semi-Automated Reference Claim Verification for Scientific Manuscripts

Stefania Mocan, Florian Angermeir, Mark Kreitz

As software engineering research submission counts surge, peer reviewers face severe time constraints, making systematic verification of citation-supported claims prohibitively exp…

cs.SE2026

From Backlog Items to Security Guidance: Towards Continuous Security Compliance

Ignacio García Núñez, Florian Angermeir, Fabiola Moyón Constante

Continuous software engineering in regulated domains requires engineering teams to address security throughout the development lifecycle. Yet making security requirements explicit…

cs.SE2026

An Agentic Approach Towards Replication Package Quality Evaluation

Maximilian Alexander Amougou Mbida, Florian Angermeir

Reproducibility in empirical software engineering relies on complete, accessible, and reusable research artifacts, yet artifact evaluation remains largely manual and difficult to s…

cs.SE2025

Aligning Security Compliance and DevOps: A Longitudinal Study

Fabiola Moyón, Florian Angermeir, Daniel Mendez +3

Companies adopt agile methodologies and DevOps to facilitate efficient development and deployment of software-intensive products. This, in turn, introduces challenges in relation t…

cs.SE20251 cited

Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies

Florian Angermeir, Maximilian Amougou, Mark Kreitz +6

Large Language Models have gained remarkable interest in industry and academia. The increasing interest in LLMs in academia is also reflected in the number of publications on this…

cs.SE2025

Guidelines for Empirical Studies in Software Engineering involving Large Language Models

Sebastian Baltes, Florian Angermeir, Chetan Arora +19

Large Language Models (LLMs) are widely used in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threat…