A Framework for Generating Informative Benchmark Instances

TL;DR

This paper presents a framework that automatically generates informative benchmark instances for constraint programming, enhancing the evaluation and understanding of solver performance across diverse problem sets.

Contribution

It introduces a novel framework that combines instance grading and discrimination to produce large, effective benchmark datasets for solver comparison.

Findings

Generated benchmark instances effectively differentiate solver performance.

Framework reveals solver behavior variations across instance subsets.

Demonstrated on MiniZinc competition problems.

Abstract

Benchmarking is an important tool for assessing the relative performance of alternative solving approaches. However, the utility of benchmarking is limited by the quantity and quality of the available problem instances. Modern constraint programming languages typically allow the specification of a class-level model that is parameterised over instance data. This separation presents an opportunity for automated approaches to generate instance data that define instances that are graded (solvable at a certain difficulty level for a solver) or can discriminate between two solving approaches. In this paper, we introduce a framework that combines these two properties to generate a large number of benchmark instances, purposely generated for effective and informative benchmarking. We use five problems that were used in the MiniZinc competition to demonstrate the usage of our framework. In addition to producing a ranking among solvers, our framework gives a broader understanding of the behaviour of each solver for the whole instance space; for example by finding subsets of instances where the solver performance significantly varies from its average performance.