# ConfigCrusher: Towards White-Box Performance Analysis for Configurable   Systems

**Authors:** Miguel Velez, Pooyan Jamshidi, Florian Sattler, Norbert Siegmund, Sven, Apel, Christian Kastner

arXiv: 1905.02066 · 2020-07-15

## TL;DR

ConfigCrusher introduces a white-box approach using static and dynamic analysis to more efficiently and accurately determine how configuration options affect system performance, surpassing black-box methods.

## Contribution

It presents a novel white-box performance analysis method that leverages static data-flow analysis and code instrumentation to identify performance influences in configurable systems.

## Key findings

- White-box approach achieves comparable or better accuracy than black-box methods.
- Method reduces the number of configurations needed for analysis.
- Provides detailed insights into performance-influencing code regions.

## Abstract

Stakeholders of configurable systems are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and optimization processes of these systems. Several black-box approaches can be used to obtain this information, but they either sample a large number of configurations to make accurate predictions or miss important performance-influencing interactions when sampling few configurations. Furthermore, black-box approaches cannot pinpoint the parts of a system that are responsible for performance differences among configurations. This article proposes ConfigCrusher, a white-box performance analysis that inspects the implementation of a system to guide the performance analysis, exploiting several insights of configurable systems in the process. ConfigCrusher employs a static data-flow analysis to identify how configuration options may influence control-flow statements and instruments code regions, corresponding to these statements, to dynamically analyze the influence of configuration options on the regions' performance. Our evaluation on 10 configurable systems shows the feasibility of our white-box approach to more efficiently build performance-influence models that are similar to or more accurate than current state of the art approaches. Overall, we showcase the benefits of white-box performance analyses and their potential to outperform black-box approaches and provide additional information for analyzing configurable systems.

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/1905.02066/full.md

## Figures

27 figures with captions in the complete paper: https://tomesphere.com/paper/1905.02066/full.md

## References

87 references — full list in the complete paper: https://tomesphere.com/paper/1905.02066/full.md

---
Source: https://tomesphere.com/paper/1905.02066