# Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?

arXiv: https://arxiv.org/abs/2008.07334 · HTML: https://mlsanity.com/paper/2008.07334/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health](https://mlsanity.com/paper/2301.06577/) (2023)
- [Old but Gold: Reconsidering the value of feedforward learners for software analytics](https://mlsanity.com/paper/2101.06319/) (2021)
- [How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs](https://mlsanity.com/paper/2205.10504/) (2022)
- [Is Hyper-Parameter Optimization Different for Software Analytics?](https://mlsanity.com/paper/2401.09622/) (2024)
- [Assessing the Early Bird Heuristic (for Predicting Project Quality)](https://mlsanity.com/paper/2105.11082/) (2021)

## Abstract (arXiv)

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.