一句话：提出社会网络分析估计器 SNet，更准更快地估计开源项目 Truck Factor 并识别关键贡献者。
项目安全依赖持续及时的维护，但这需要深入了解代码库；Truck Factor 度量项目因关键贡献者流失而失去维护知识时的脆弱程度。现有估计器随项目规模扩大准确率下降，且常忽略大型项目中常见的管理型团队成员。本文提出 SNet，一种基于社会网络分析的社会感知 Truck Factor 估计器。SNet 利用网络中心性度量以及 GitHub Issue 交互等社会信号来估计 Truck Factor 并识别 Truck Factor 贡献者。作者在包含 26 个开源项目的既有 ground truth 上评估 SNet。结果显示，该社会网络分析方法在贡献者分类上取得更优性能（F1 中位数为 0.8），同时相较最先进估计器将计算时间减少 2 倍以上。
Continued timely maintenance is a key aspect of project security, but typically requires in-depth knowledge of a project’s code base. Truck Factor is a metric that aims to represent how vulnerable a project is to losing this knowledge through the attrition of key contributors. However, the accuracy of existing Truck Factor estimators scales poorly with project size since they tend to ignore influential team members in managerial roles, which are more common in large projects.This work proposes SNet, a novel socially aware Truck Factor estimator based on social network analysis. SNet uses network centrality measures and social signals such as GitHub Issue interactions to estimate Truck Factor and identify Truck Factor contributors. We evaluate SNet against an existing ground truth comprised of twenty-six open source projects. Our social network analysis approach achieves superior contributor classification performance (Median F1 score = 0.8) while reducing computation time by over 2x compared to state-of-the-art estimators.
DOI 原文 ·
@inproceedings{paperbot9353,
title = {Measuring Software Resilience Using Socially Aware Truck Factor Estimation},
author = {Alexis Butler and Dan O’Keeffe and Santanu Kumar Dash},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00318}
}