预测开源项目的健康指标（使用超参数优化）
Predicting Health Indicators for Open Source Projects (using Hyperparameter Optimization)
摘要：
在公共平台上开发的软件是可用于对这些项目进行预测的数据来源。虽然单个开发活动可能是随机的，很难预测，但当大型开发人员在软件项目上一起工作时，项目级别上的开发行为可以很好地预测。为了证明这一点，我们使用了来自1,159个GitHub项目的64,181个月的数据，对这些项目的最新状态（截至2020年4月）进行了各种预测。我们发现，传统的估计算法有很多错误。像$k$ -最近邻（KNN）、支持向量回归（SVR）、随机森林（RFT）、线性回归（LNR）和回归树（CART）这样的算法具有高出错率。但是使用超参数优化可以大大降低出错率。据我们所知，这是迄今为止进行的最大规模的研究，使用最新数据预测开源项目的多个健康指标。
英文摘要：
Software developed on public platform is a source of data that can be used to make predictions about those projects. While the individual developing activity may be random and hard to predict, the developing behavior on project level can be predicted with good accuracy when large groups of developers work together on software projects. To demonstrate this, we use 64,181 months of data from 1,159 GitHub projects to make various predictions about the recent status of those projects (as of April 2020). We find that traditional estimation algorithms make many mistakes. Algorithms like $k$-nearest neighbors (KNN), support vector regression (SVR), random forest (RFT), linear regression (LNR), and regression trees (CART) have high error rates. But that error rate can be greatly reduced using hyperparameter optimization. To the best of our knowledge, this is the largest study yet conducted, using recent data for predicting multiple health indicators of open-source projects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
