Prioritizing Configuration Relevance via Compiler-Based Refined Feature Ranking

TL;DR

This paper introduces a compiler-based method for prioritizing configurations in Rust programs, enabling efficient exploration of large configuration spaces through feature ranking and graph analysis.

Contribution

It presents the first compiler-based approach for ranking and refining feature relevance in Rust, integrating graph centrality and code impact for configuration prioritization.

Findings

Efficient generation of relevant configurations within bounded resources

Soundness guaranteed by SAT solver integration

Effective exploration of large configuration spaces

Abstract

Modern programming languages, most notably Rust, offer advanced linguistic constructs for building highly configurable software systems as aggregation of features -- identified by a configuration. However, they pose substantial challenges for program analysis, optimization, and testing, as the combinatorial explosion of configurations often makes exhaustive exploration infeasible. In this manuscript, we present the first compiler-based method for prioritizing configurations. Our approach consists of four main steps: 1. extracting a tailored intermediate representation from the Rust compiler, 2. constructing two complementary graph-based data structures, 3. using centrality measures to rank features, and 4. refining the ranking by considering the extent of code they impact. A fixed number of most relevant configurations are generated based on the achieved feature ranking. The validity of the generated configurations is guaranteed by using a SAT solver that takes a representation of this graph in conjunctive normal form. We formalized this approach and implemented it in a prototype, RustyEx, by instrumenting the Rust compiler. An empirical evaluation on higher-ranked open source Rust projects shows that RustyEx efficiently generates user-specified sets of configurations within bounded resources, while ensuring soundness by construction. The results demonstrate that centrality-guided configuration prioritization enables effective and practical exploration of large configuration spaces, paving the way for future research in configuration-aware analysis and optimization.