This paper introduces SEMODS, a dataset of 3,427 software engineering (SE) models extracted and validated from Hugging Face. It aims to bridge the gap in identifying SE-specific models for AI integration by linking them to SE tasks, standardizing evaluation results, and supporting various applications like model discovery and benchmarking.

This vision paper explores how artificial intelligence (AI) agents can support Software Engineering (SE) researchers in formulating research problems, aiming to bridge the gap between academic contributions and industrial needs. It proposes integrating AI agents into the Lean Research Inception (LRI) framework to enhance problem definition and assessment.

This paper empirically studies 328 configuration files from public Claude Code projects to understand how developers configure AI coding agents. The findings highlight the importance of defining various software engineering concerns, particularly architectural specifications, within these configuration files to guide agent behavior and improve effectiveness.