# How to "DODGE" Complex Software Analytics?

## TL;DR

This paper introduces DODGE, a hyperparameter optimization tool for software analytics that significantly speeds up the tuning process by avoiding redundant configurations, leading to more accurate predictive models.

## Contribution

DODGE is a novel hyperparameter tuning method that efficiently skips redundant configurations, improving speed and accuracy over existing approaches.

## Key findings

- DODGE runs orders of magnitude faster than traditional methods.
- DODGE produces more accurate predictive models.
- Ignoring redundant tunings enhances hyperparameter optimization efficiency.

## Abstract

Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner's control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time exploring "redundant tunings"', i.e., pairs of tunings which lead to indistinguishable results. By ignoring redundant tunings, DODGE, a tuning tool, runs orders of magnitude faster, while also generating learners with more accurate predictions than seen in prior state-of-the-art approaches.