A large-scale study on research code quality and execution

TL;DR

This large-scale study evaluates the quality and reproducibility of research code in scientific datasets, revealing high crash rates and proposing best practices for improving code reuse and transparency.

Contribution

It provides an extensive analysis of research code quality, execution success rates, and the impact of journal policies, along with recommendations for better code dissemination.

Findings

74% of R files crashed initially

56% crashed after code cleaning

Journal policy strictness affects re-execution rates

Abstract

This article presents a study on the quality and execution of research code from publicly-available replication datasets at the Harvard Dataverse repository. Research code is typically created by a group of scientists and published together with academic papers to facilitate research transparency and reproducibility. For this study, we define ten questions to address aspects impacting research reproducibility and reuse. First, we retrieve and analyze more than 2000 replication datasets with over 9000 unique R files published from 2010 to 2020. Second, we execute the code in a clean runtime environment to assess its ease of reuse. Common coding errors were identified, and some of them were solved with automatic code cleaning to aid code execution. We find that 74\% of R files crashed in the initial execution, while 56\% crashed when code cleaning was applied, showing that many errors can be prevented with good coding practices. We also analyze the replication datasets from journals' collections and discuss the impact of the journal policy strictness on the code re-execution rate. Finally, based on our results, we propose a set of recommendations for code dissemination aimed at researchers, journals, and repositories.