We're Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration

TL;DR

This paper introduces consistency-preserving configuration operators (CPCOs) for product line configuration, enabling valid solutions throughout the search process and significantly improving efficiency and quality in large feature models.

Contribution

The paper presents CPCOs, a novel genetic operator that maintains configuration validity during search, enhancing the performance of multi-objective optimization in product line configuration.

Findings

CPCOs outperform state-of-the-art tools in speed and quality.

Significant improvements in large feature models with thousands of features.

CPCOs reduce the need for repair actions during search.

Abstract

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce consistency-preserving configuration operators (CPCOs)--genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.