一句话：提出RecGFI，利用多维度特征和XGBoost为开源项目推荐适合新手的good first issue。
开源项目需要吸引和留住新人，但新人难以找到合适任务，现有Good First Issue（GFI）标签往往不足且不准确。本文提出RecGFI，从内容、背景和动态三个维度刻画issue，并用XGBoost分类器预测其为GFI的概率，向新人推荐合适的入门任务。作者从100个GitHub项目收集53,510个已解决issue并恢复其历史状态构建真实标注数据集。评估显示RecGFI最高达到0.853 AUC，优于对比模型；模型可解释性分析揭示GFI特征。作者还向维护者报告新issue，其中16个被确认真实GFI，5个由新人解决。
Attracting and retaining newcomers is vital for the sustainability of an open-source software project. However, it is difficult for newcomers to locate suitable development tasks, while existing "Good First Issues" (GFI) in GitHub are often insufficient and inappropriate. In this paper, we propose RecGFI, an effective practical approach for the recommendation of good first issues to newcomers, which can be used to relieve maintainers' burden and help newcomers onboard. RecGFI models an issue with features from multiple dimensions (content, background, and dynamics) and uses an XGBoost classifier to generate its probability of being a GFI. To evaluate RecGFI, we collect 53,510 resolved issues among 100 GitHub projects and carefully restore their historical states to build ground truth datasets. Our evaluation shows that RecGFI can achieve up to 0.853 AUC in the ground truth dataset and outperforms alternative models. Our interpretable analysis of the trained model further reveals interesting observations about GFI characteristics. Finally, we report latest issues (without GFI-signaling labels but recommended as GFI by our approach) to project maintainers among which 16 are confirmed as real GFIs and five have been resolved by a newcomer.