软件分析的更简单的超参数优化：为什么，如何，何时？
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?
摘要：
如何让软件分析更简单、更快速？一种方法是将分析的复杂性与正在探索的数据的内在复杂性相匹配。例如，超参数优化器为数据挖掘器找到控制设置，以改进通过软件分析生成的预测。有时候，非常快速的超参数优化可以通过避开以前尝试过的东西来实现。但是什么时候使用DODGE是明智的，什么时候我们必须使用更复杂（和更慢）的优化器？为了回答这个问题，我们对120个SE数据集应用了超参数优化，这些数据集探索了异味检测、预测Github ssue关闭时间、bug报告分析、缺陷预测和其他数十个非SE问题。我们发现，DODGE最适合低“内在维度”（D = 3）的数据集，而对于高维数据（D超过8）则非常差。这里看到的几乎所有SE数据本质上都是低维的，这表明DODGE适用于许多SE分析任务。
英文摘要：
How to make software analytics simpler and faster? One method is to match the complexity of analysis to the intrinsic complexity of the data being explored. For example, hyperparameter optimizers find the control settings for data miners that improve for improving the predictions generated via software analytics. Sometimes, very fast hyperparameter optimization can be achieved by just DODGE-ing away from things tried before. But when is it wise to use DODGE and when must we use more complex (and much slower) optimizers? To answer this, we applied hyperparameter optimization to 120 SE data sets that explored bad smell detection, predicting Github ssue close time, bug report analysis, defect prediction, and dozens of other non-SE problems. We find that DODGE works best for data sets with low "intrinsic dimensionality" (D = 3) and very poorly for higher-dimensional data (D over 8). Nearly all the SE data seen here was intrinsically low-dimensional, indicating that DODGE is applicable for many SE analytics tasks.
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。
