# Leveraging Language Models to Discover Evidence-Based Actions for OSS Sustainability

arXiv: https://arxiv.org/abs/2602.11746 · HTML: https://mlsanity.com/paper/2602.11746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [MammoWise: Multi-Model Local RAG Pipeline for Mammography Report Generation](https://mlsanity.com/paper/2602.22462/) (2026)

## Abstract (arXiv)

When successful, Open Source Software (OSS) projects create enormous value, but most never reach a sustainable state. Recent work has produced accurate models that forecast OSS sustainability, yet these models rarely tell maintainers what to do: their features are often high-level socio-technical signals that are not directly actionable. Decades of empirical software engineering research have accumulated a large but underused body of evidence on concrete practices that improve project health. We close this gap by using LLMs as evidence miners over the SE literature. We design a RAG-pipeline and a two-layer prompting strategy that extract researched actionables (ReACTs): concise, evidence-linked recommendations mapping to specific OSS practices. In the first layer, we systematically explore open LLMs and prompting techniques, selecting the best-performing combination to derive candidate ReACTs from 829 ICSE and FSE papers. In the second layer, we apply follow-up prompting to filter hallucinations, extract impact and evidence, and assess soundness and precision. Our pipeline yields 1,922 ReACTs, of which 1,312 pass strict quality criteria and are organized into practice-oriented categories connectable to project signals from tools like APEX. The result is a reproducible, scalable approach turning scattered research findings into structured, evidence-based actions guiding OSS projects toward sustainability.