We are trying to make artifact submission as simple as possible. You just need to pack your artifact (code and data) using any publicly avialable tool you prefer. In some exceptional cases when rare hardware or proprietary software is used, you can arrange a remote access to machine with the pre-installed software.
Then, you need to prepare a small and informal Artifact Evaluation appendix using our AE LaTeX template (common for PPoPP, CGO, PACT and SC conferences - see below) to explain evaluators what are your artifacts and how to use them (you will be allowed to add up to 2 pages of this Appendix to your final camera-ready paper). Please see this PPoPP'16 paper for the example of such AE appendix.
At least two reviewers will follow your guide to replicate your results (for example, exact output match) or reproduce them (for example, varying performance numbers or scalability on a different machine), and will then send you a report with the following overall assessment of your artifact based on our reviewing guidelines:
Since our eventual goal is to promote artifact validation and sharing (rather than naming and shaming problematic artifacts), you will be able to address raised issues during the rebuttal. Furthermore, we allow a small amount of communication between reviewers and authors whenever there are installation/usage problems. In such cases, AE chairs will serve as a proxy to avoid revealing reviewers' identity (the review is blind, i.e. your identity is known to reviewers since your paper is already accepted, but not vice versa).
From our past Artifact Evaluation experience, we've noticed that the most challenging part is to automate and customize experimental workflows. It is even worse, if you need to validate experiments using latest software environment and hardware (rather than quickly outdated VM and Docker images). Most of the time, some ad-hoc scripts are used to implement these workflows. They are very difficult to change and customize, particularly when an evaluator would like to try other compilers, libraries and data sets.
These problems motivated us to develop Collective Knowledge Framework (CK) -
a small and open-source infrastructure and repository to help researchers share their artifacts as reusable Python components
with a unified JSON API. This approach should help researchers quickly prototype experimental workflows
(such as multi-objective autotuning) from such components while automatically detecting and resolving
all required software or hardware dependencies. CK is also intended to reduce evaluators' burden
by unifying statistical analysis and predictive analytics (via scikit-learn, R, DNN),
and enabling interactive reports. Please see examples of a CK-based live repository,
and PLDI'15 CLSmith artifact shared in CK format.
Feel free to use it when submitting artifacts for the evaluation.
PPoPP'15 article together with this LaTeX example. You can change \hspace and \raisebox parameters to better fit stamp to your paper.
We strongly encourage you to submit your AE appendix (up to 2 pages) as an auxiliary material for the Digital Library (while removing all unnecessary or confidential information) along with the final variant of your paper. This will help readers better understand what was evaluated.
Though you are not obliged to publicly release your artifacts (in fact, it is sometimes impossible due to various limitations), we also strongly encourage you to share them with the community (even if they are not open-source). You can release them as an auxiliary material in Digital Libraries together with your AE appendix or use your institutional repository and various public services for code and data sharing.
Even accepted artifacts may have some unforeseen behavior and limitations discovered during evaluation. Now you have a chance to add related notes to your paper as a future work (if you wish)..
Note, that we are developing open-source Collective Knowledge framework to help researchers share their artifacts as unified, reusable and customizable components together with experimental workflows and interactive articles. If you are interested to know more, please check some online examples and a recent DATE'16 paper.