Sharing Software-Evolution Datasets: Practices, Challenges, and Recommendations

Sharing research artifacts (e.g., software, data, protocols) is an immensely important topic for improving transparency, replicability, and reusability in research, and has recently gained more and more traction in software engineering. For instance, recent studies have focused on artifact reviewing, the impact of open science, and specific legal or ethical issues of sharing artifacts.Most of such studies are concerned with artifacts created by the researchers themselves (e.g., scripts, algorithms, tools) and processes for quality assuring these artifacts (e.g., through artifact-evaluation committees). In contrast, the practices and challenges of sharing software-evolution datasets (i.e., republished version-control data with person-related information) have only been scratched in such works. To tackle this gap, we conducted a meta study of software-evolution datasets published at the International Conference on Mining Software Repositories from 2017 until 2021 and snowballed a set of papers that build upon these datasets. Investigating 200 papers, we elicited what types of software-evolution datasets have been shared following what practices and what challenges researchers experienced with sharing or using the datasets. We discussed our findings with an authority on research-data management and ethics reviews through a semi-structured interview to put the practices and challenges into context. Through our meta study, we provide an overview of the sharing practices for software-evolution datasets and the corresponding challenges. The expert interview enriched this analysis by discussing how to solve the challenges and by defining recommendations for sharing software-evolution datasets in the future. Our results extend and complement current research, and we are confident that they can help researchers share software-evolution datasets (as well as datasets involving the same types of data) in a reliable, ethical, and trustworthy way.