On the Relation of External and Internal Feature Interactions: A Case Study

TL;DR

This paper investigates the relationship between static control-flow feature interactions and dynamic performance feature interactions in configurable systems, aiming to reduce measurement costs in performance prediction.

Contribution

It presents a qualitative case study showing a potential link between static code analysis and dynamic performance interactions, enabling more efficient performance prediction.

Findings

A relation exists between static control-flow and dynamic performance interactions.

Static analysis can potentially predict performance interactions.

The approach reduces the need for extensive performance measurements.

Abstract

Detecting feature interactions is imperative for accurately predicting performance of highly-configurable systems. State-of-the-art performance prediction techniques rely on supervised machine learning for detecting feature interactions, which, in turn, relies on time consuming performance measurements to obtain training data. By providing information about potentially interacting features, we can reduce the number of required performance measurements and make the overall performance prediction process more time efficient. We expect that the information about potentially interacting features can be obtained by statically analyzing the source code of a highly-configurable system, which is computationally cheaper than performing multiple performance measurements. To this end, we conducted a qualitative case study in which we explored the relation between control-flow feature interactions (detected through static program analysis) and performance feature interactions (detected by performance prediction techniques using performance measurements). We found that a relation exists, which can potentially be exploited to predict performance interactions.