# Using Bad Learners to find Good Configurations

## TL;DR

This paper introduces a rank-based method that uses inexpensive, less accurate performance models to effectively identify optimal configurations of software systems, reducing measurement costs and time.

## Contribution

The paper proposes a novel rank-based approach that leverages cheap, inaccurate models to find optimal configurations without needing precise performance predictions.

## Key findings

- Significantly reduces measurement and modeling costs in 16 out of 21 scenarios.
- Effective even with inaccurate models for ranking configurations.
- In 5 scenarios, accurate models can be built with few samples, making the rank-based approach unnecessary.

## Abstract

Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, building an accurate performance model can be very expensive (and is often infeasible in practice). The central insight of this paper is that exact performance values (e.g. the response time of a software system) are not required to rank configurations and to identify the optimal one. As shown by our experiments, models that are cheap to learn but inaccurate (with respect to the difference between actual and predicted performance) can still be used rank configurations and hence find the optimal configuration. This novel \emph{rank-based approach} allows us to significantly reduce the cost (in terms of number of measurements of sample configuration) as well as the time required to build models. We evaluate our approach with 21 scenarios based on 9 software systems and demonstrate that our approach is beneficial in 16 scenarios; for the remaining 5 scenarios, an accurate model can be built by using very few samples anyway, without the need for a rank-based approach.