Artifact evaluation


Empirical evidence is important to produce long-lasting impact research. We feel that the tools and experiments that are used to produce or validate research results are not given as much attention as they should. To counteract this tendency, some communities have started a process called Artifact Evaluation (AE), that rewards well written tools allowing researchers to replicate the experiments presented in papers. The purpose of the AE process is mainly to improve the reproducibility of computational results.

ECRTS has been the first real-time systems conference to introduce artifact evaluation in 2016, then has continued since then. In 2022, the process will be repeated.

Authors of accepted papers with a computational component will be invited to submit their code and/or their data to an optional AE process. We seek to achieve the benefits of the AE process without disturbing the current process through which ECRTS has generated high-quality programs in the past. In particular, the decision to submit or not an artifact has no impact on whether a paper is accepted at ECRTS. Moreover, there will be no disclosure of the title and authors of papers which would not pass the repeatability evaluation.

The authors of the papers corresponding to the artifacts which pass the evaluation can decide to use a seal that indicates that the artifact has passed the repeatability test.

We recognize that not all the results are repeatable. For instance, the execution time of the experiments may be too long or a complete infrastructure to execute the tests may be required, but not available to the evaluators. We encourage submissions but we can only guarantee to repeat experiments that are reasonably repeatable with regular computing resources. Our focus is on (1) replicating the tests that are repeatable, (2) improving the repeatability infrastructure so that more tests become repeatable in the future.

Formatting instructions

Artifacts should include two components:

  • a document explaining how to use the artifact and which of the experiments presented in the paper are repeatable (with reference to specific digits, figures and tables in the paper), the system requirements and instructions for installing and using the artifact;
  • the software and any accompanying data.

A good how-to to prepare an artifact evaluation package is available online at

The evaluation process is single-blind. The evaluation process is non-competitive and we hope that all the artifacts submitted can pass the evaluation criteria.

Special Artifacts

If you are not in a position to prepare the artifact as above, or if your artifact requires special libraries, commercial tools (e.g., MATLAB or specific toolboxes), or particular hardware, please contact the AE chair as soon as possible.

Recommendation: Use Virtual Machines

Based on previous experience, the biggest hurdle to successful reproducibility is the setup and installation of the necessary libraries and dependencies. Authors are therefore encouraged to prepare a virtual machine (VM) image including their artifact (if possible) and to make it available via HTTP throughout the evaluation process (and, ideally, afterwards). As the basis of the VM image, please choose commonly-used OS versions that have been tested with the virtual machine software and that evaluators are likely to be accustomed to. We encourage authors to use VirtualBox ( and save the VM image as an Open Virtual Appliance (OVA) file. To facilitate the preparation of the VM, we suggest using the VM images available


  • Artifact submission deadline: April 20, 2022
  • Response to authors: Mai 13, 2022

Submission Process

Given that your paper has been accepted, we highly encourage you to submit to the ECRTS’22 Artifact Evaluation (AE). The artifact submission deadline is 20 April 2022.

Submission Site:


Artifact Evaluation co-chairs:

  • Angeliki Kritikakou, IRISA, Rennes, France
  • Matthias Becker, KTH, Sweden

Evaluation committee:

  • Xiaotian Dai, University of York
  • Bryan Donyanavard, San Diego State University
  • Filip Markovic, Mälardalen University
  • Marcello Traiola, Inria Rennes / IRISA
  • Jung-Eun Kim, Syracuse University
  • Leonidas Kosmidis, Barcelona Supercomputing Center (BSC)
  • Lea Schönberger, TU Dortmund University
  • Romain Jacob, ETH Zurich
  • Benjamin Rouxel, University of Amsterdam
  • Cláudio Maia, CISTER Research Centre
  • Cédric Courtaud, Max Planck Institute for Software Systems
  • Catherine Nemitz, Davidson College

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