一句话：提出半自动化文献综述工具 ProfOlaf，结合迭代雪球与 LLM 辅助筛选、主题抽取和问答，提升效率与可复现性。
系统性综述与 mapping 研究对梳理研究现状、识别空白至关重要，但过程耗时耗力；现有工具只覆盖部分步骤，大量环节仍依赖人工且易错。本文提出 ProfOlaf，一个保持方法学严谨性的半自动化工具。它支持带 human-in-the-loop 过滤的迭代 snowballing 文献收集，并利用大语言模型辅助文章筛选、关键主题抽取以及针对文章内容的问答。通过自动化与受引导人工投入相结合，ProfOlaf 旨在提升跨领域系统性综述的效率、质量与可复现性。工具同时提供 CLI 与 Web 应用形式，并附有演示视频；摘要未报告定量评估结果。
Systematic reviews and mapping studies are critical to synthesize research, identify gaps, and guide future work, but are often labor-intensive and time-consuming. Existing tools provide partial support for specific steps, leaving much of the process manual and error-prone. We present ProfOlaf, a semi-automated tool designed to streamline systematic reviews while maintaining methodological rigor. ProfOlaf supports iterative snowballing for article collection with human-in-the-loop filtering and uses large language models to help select articles, extract key topics, and answer queries about the content of articles. By combining automation with guided manual effort, ProfOlaf enhances the efficiency, quality, and reproducibility of systematic reviews across research fields. ProfOlaf can be used both as a CLI tool and in web application format. A video demonstrating ProfOlaf is available at: https://youtu.be/R-gY4dJlN3s
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2510-26750,
title = {ProfOlaf: Semi-Automated Tool for Systematic Literature Reviews},
author = {Martim Afonso and Nuno Saavedra and Bruno Lourenço and Alexandra Mendes and João F. Ferreira},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806432}
}