Abstract

Abstract. Propagation is at the very core of Constraint Programming (CP): it can provide significant performance boosts as long as the search space reduction is not outweighed by the cost for running the propaga-tors. A lot of research effort in the CP community is directed toward improving this trade-off, which for a given type of filtering amounts to reducing the computation cost. This is done chiefly by 1) devising more efficient algorithms or by 2) using on-line control policies to limit the propagator activations. In both cases, obtaining improvements is a long and demanding process with uncertain outcome. We propose a method to assess the potential gain of both approaches before actually starting the endeavor, providing the community with a tool to best direct the research efforts. Our approach is based on instrumenting the constraint solver to collect statistics, and we rely on replaying search trees to ob-tain more realistic assessments. The overall approach is easy to setup and is showcased on the Energetic Reasoning (ER) and the Revisited Cardinality Reasoning for BinPacking (RCRB) propagators.