most citedGuidelines for Empirical Studies in Software Engineering involving Large Language Models

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE20263 cited

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…

cs.SE2026

Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape

Bianca Trinkenreich, Fabio Calefato, Kelly Blincoe +8

Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there…

cs.SE2026

Self-Admitted GenAI Usage in Open-Source Software

Tao Xiao, Youmei Fan, Fabio Calefato +4

Strategized LaTeX removal and whitespace normalization approachThe widespread adoption of generative AI (GenAI) tools such as GitHub Copilot and ChatGPT is transforming software de…

cs.SE2025

Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research

Bianca Trinkenreich, Fabio Calefato, Geir Hanssen +5

The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in th…

cs.SE2025

Who "Controls" Where Work Shall be Done? State-of-Practice in Post-Pandemic Remote Work Regulation

Darja Smite, Nils Brede Moe, Maria Teresa Baldassarre +8

The COVID-19 pandemic has permanently altered workplace structures, making remote work a widespread practice. While many employees advocate for flexibility, many employers reconsid…