Artifact Evaluations for Stronger Research Results

Over the past decades, reports of reproducibility crises have surfaced in various scientific communities. Independent confirmations of published research results failed, casting doubt on the validity of these results. Even before the magnitude of the problem has become apparent in many domains, the software-engineering community introduced artifact evaluations, for the first time at ESEC/FSE 2011, in which research artifacts that support published results were voluntarily submitted for peer review. Since then, artifact evaluations have become immensely popular and are today being offered to authors at most software-engineering venues, where large artifact-evaluation committees handle large numbers of artifact submissions. At some venues, papers are accepted for publication only if their artifacts pass the artifact evaluation.

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Published
2025-06-23
DOI
https://doi.org/10.1145/3696630.3728623
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Artifact Evaluations for Stronger Research Results

Dirk Beyer, Stefan Winter
Machine Learning in Materials Science
article

Artifact Evaluations for Stronger Research Results

Dirk Beyer, Stefan Winter
article en

Abstract

Over the past decades, reports of reproducibility crises have surfaced in various scientific communities. Independent confirmations of published research results failed, casting doubt on the validity of these results. Even before the magnitude of the problem has become apparent in many domains, the software-engineering community introduced artifact evaluations, for the first time at ESEC/FSE 2011, in which research artifacts that support published results were voluntarily submitted for peer review. Since then, artifact evaluations have become immensely popular and are today being offered to authors at most software-engineering venues, where large artifact-evaluation committees handle large numbers of artifact submissions. At some venues, papers are accepted for publication only if their artifacts pass the artifact evaluation.

LMU Klinikum (DE), Ludwig-Maximilians-Universität München (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 87%
Machine Learning in Materials Science
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