Collaborative Recommendation of Search Heuristics For Constraint Solvers

Feature models (FM) support the management of variability properties of software, products, and services. To enable feature model configuration, these models have to be translated into a corresponding formal representation (e.g., a satisfiability or constraint satisfaction representation). Specifically in interactive configuration, efficient response times are crucial. In this paper, we show how to improve the performance of constraint solvers (supporting FM configuration) on the basis of exploiting the concepts of collaborative filtering for recommending solver search heuristics (variable orderings and value orderings). As a basis for our recommendation approach, we used data (configurations) synthesized from real-world feature models using different state-of-the-art synthesis approaches. A performance analysis shows that, with heuristics recommendation, significant improvements of solver runtime performance compared to standard solver heuristics can be achieved.