Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Multicore Tool for Constraint Solving
Authors: Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results are very promising. sunny-cp2 can even outperform the performance of the oracle solver which always selects the best solver of the portfolio for a given problem. |
| Researcher Affiliation | Academia | Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro Department of Computer Science and Engineering, University of Bologna / Lab. Focus INRIA EMAIL |
| Pseudocode | No | The paper describes the SUNNY algorithm and its parallelization using prose and mathematical notation, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of sunny-cp2 is entirely written in Python and publicly available at https://github.com/CP-Unibo/sunny-cp. |
| Open Datasets | Yes | The default dataset of sunny-cp2 is the union of a set CSP of 5527 CSPs and a set COP of 4988 COPs, retrieved from the instances of the Mini Zinc 1.6 benchmarks, the MZCs 2012 – 2014, and the International CSP Solver Competitions 2008/09. |
| Dataset Splits | Yes | Following the standard practices, we used a 10-fold cross-validation: we partitioned each dataset in 10 disjoint folds, treating in turn one fold as test set and the union of the remaining folds as the training set. |
| Hardware Specification | No | The paper mentions running experiments in a 'multicore setting' and varying the number of 'c cores', but it does not provide specific details about the hardware used, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions that sunny-cp2 is 'entirely written in Python' and lists several constituent solvers by name (e.g., Chuffed, CPX, Gecode, Choco, OR-Tools) but does not specify version numbers for Python or any of these solvers. |
| Experiment Setup | Yes | We ran sunny-cp2 on all the instances of CSP and COP within a timeout of T = 1800 seconds by varying the number c of cores in {1, 2, 4, 8}. ...the default values of Tw and Tr to 2 and 5 seconds respectively. ...The neighbourhood size is set by default to k = 70... |