A Framework for Generating Informative Benchmark Instances
This work addresses the need for more effective benchmarking tools in constraint programming, though it is incremental as it builds on existing parameterized models and competition data.
The paper tackles the problem of limited utility in benchmarking due to insufficient quality and quantity of problem instances by introducing a framework that generates graded and discriminative benchmark instances, demonstrated on five problems from the MiniZinc competition to rank solvers and analyze their behavior across the instance space.
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.