3 citations · 3 across the 2 of their papers we have counts for
5 papers
Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software
Daniel Russo
Autonomous coding agents now open and merge pull requests in shared repositories at scale, and the field evaluates them the way it has always evaluated components, one agent at a t…
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…
More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems
Daniel Russo
Software engineering faces a fundamental challenge: multi-agent AI systems fail in ways that defy explanation by traditional theories. While individual agents perform correctly, th…
Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering Purposes
Stefano Lambiase, Gemma Catolino, Fabio Palomba +2
Context: The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering (SE) processes. Research has explored their r…
From Challenge to Change: Design Principles for AI Transformations
Theocharis Tavantzis, Stefano Lambiase, Daniel Russo +1
The rapid rise of Artificial Intelligence (AI) is reshaping Software Engineering (SE), creating new opportunities while introducing human-centered challenges. Although prior work n…