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This is a Experimental Study study (also classified as: Quantitative Study).

September 28, 20250 citationsOpen Access

A Framework for Supporting the Reproducibility of Computational Experiments in Multiple Scientific Domains

LCLázaro Costa SBSusana Barbosa JCJácome Cunha

Key Points

SciRep successfully executed 89% of the computational experiments, demonstrating its effectiveness in enhancing reproducibility.
The proposed framework allowed all executed experiments to produce results consistent with original publications, highlighting robust replicability.
SciRep addresses challenges in configuring and executing computational environments, ensuring reliable experimental setups across various fields.
By comparing with three state-of-the-art tools, SciRep proves superior in achieving higher reproducibility rates in multiple scientific domains.

Abstract

In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run.

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