Replication Package for Understanding and Improving Artifact Sharing in Software Engineering Research

Replication Package for Understanding and Improving Artifact Sharing in Software Engineering Research
This archive provides the associated publication analysis dataset and survey instrument for the research paper, Understanding and Improving Artifact Sharing in Software Engineering Research, published in Empirical Software Engineering. (http://dx.doi.org/10.1007/s10664-021-09973-5). A preprint for the paper is available on arXiv: https://arxiv.org/abs/2008.01046.

This artifact is archived on Zenodo (https://doi.org/10.5281/zenodo.4737346) and hosted on GitHub (https://github.com/ChrisTimperley/se-artifact-sharing).

Abstract

In recent years, many software engineering researchers have begun to include artifacts
alongside their research papers. Ideally, artifacts, including tools, benchmarks, and data, support the dissemination of ideas, provide evidence for research claims, and serve as a starting point for future research. However, in practice, artifacts suffer from a variety of issues that prevent the realization of their full potential.

To help the software engineering community realize the full potential of artifacts, we seek to understand the challenges involved in the creation, sharing, and use of artifacts. To that end, we perform a mixed-methods study including a survey of artifacts in software engineering publications, and an online survey of 153 software engineering researchers. By analyzing the perspectives of artifact creators, users, and reviewers, we identify several high-level challenges that affect the quality of artifacts including mismatched expectations between these groups, and a lack of sufficient reward for both creators and reviewers. Using Diffusion of Innovations as an analytical framework, we examine how these challenges relate to one another, and build an understanding of the factors that affect the sharing and success of artifacts. Finally, we make recommendations to improve the quality of artifacts based on our results and existing best practices.