Advancing Research Software Engineering with AI: A Research Framework

Abstract The rapid adoption of Artificial Intelligence (AI) and Generative AI (GenAI) tools is transforming the creation, maintenance, and dissemination of research software. Despite their growing prevalence, the implications of these technologies for Research Software Engineering (RSE) practices remain underexplored. This work introduces AI4RSE, an emerging research domain focused on the integration of AI into the development lifecycle of research software. To investigate current trends in AI-augmented RSE, we conducted an empirical study of more than 1,500 open-source research software repositories hosted on Zenodo. Each repository was assessed using a quadrant-based typology defined by two key dimensions: software engineering maturity and the level of AI integration. Our analysis combined static and semantic code inspection, evaluation of alignment with the FAIR Principles for Research Software (FAIR4RS), and heuristic classification of generative AI usage and MLOps adoption. Repositories are categorized into four development modes: Exploratory Coding, Vibe Coding, RSE, and AI4RSE, which reflect different levels of process rigor and AI tool integration. While many projects exhibit informal development patterns, a growing subset demonstrates mature, AI-assisted workflows. This landscape reveals key challenges, such as reproducibility risks and licensing ambiguity, while also highlighting emerging opportunities, including AI-assisted testing and intelligent documentation generation. The findings support a research agenda for AI4RSE, outlining benchmarks, guidelines, and community standards to promote responsible, reproducible, and scalable adoption of AI in scientific software development.