Repeatability Evaluation

Repeatability Evaluation

Submission guidelines

The Repeatability Evaluation Package (REP) consists of three components:

  • A copy (in pdf format) of the final camera-ready paper. This copy will be used by the repeatability evaluation committee to evaluate how well the elements of the REP match the paper.
  • A document (either a web-page, a pdf, or a plain text file) explaining at a minimum:
    • What elements of the paper are included in the REP (eg: specific figures, tables, etc.).
    • The system requirements for running the REP (eg: OS, compilers, environments, etc.).
    • Instructions for installing and running the software and extracting the corresponding results.
  • The software and any accompanying data. We will accept at least the following formats:
    • A link to a public on-line repository, such as,, or
    • An archive in a standard file format (eg: zip, gz, tgz) containing all the necessary components.
    • A link to a virtual machine image (using either VirtualBox or VMware) which can be downloaded.

If you would like to submit software and/or data in another format, please contact the RE committee chair in advance to discuss options. The REP should be submitted through the Easychair website.

Please, note that this is a different site than that used for paper submissions.

When preparing your REP, keep in mind that other conferences have reported that the most common reason for reproducibility failure is installation problems. We recommend that you have an independent member of your lab test your installation instructions and REP on a clean machine before final submission.

Submitting a REP is not mandatory for regular papers, and strongly suggested for Tool and Case Study papers. Papers whose REPs pass the repeatability evaluation criteria will be listed on-line and in the final proceedings. On the other hand, papers whose REPs do not pass the repeatability evaluation criteria will be treated the same as papers which do not submit REPs.

The repeatability evaluation process uses anonymous reviews so as to solicit honest feedback. Authors of REPs should make a genuine effort to avoid learning the identity of the reviewers. This effort may require turning off analytics or only using systems with high enough traffic that REC accesses will not be apparent. In all cases where tracing is unavoidable the authors should provide warnings in the documentation so that reviewers can take necessary precautions to maintain anonymity.

REPs are considered confidential material in the same sense as initial paper submissions: committee members agree not to share REP contents and to delete them after evaluation. REPs remain the property of the authors, and there is no requirement to post them publicly (although we encourage you to do so).

Background and Goals

HSCC has a rich history of publishing strong papers emphasizing computational contributions; however, subsequent re-creation of these computational elements is often challenging because details of the implementation are unavoidably absent in the paper. Some authors post their code and data to their websites, but there is little formal incentive to do so and no easy way to determine whether others can actually use the result. As a consequence, computational results often become non reproducible — even by the research group which originally produced them — after just a few years.

The goal of the HSCC repeatability evaluation process is to improve the reproducibility of computational results in the papers selected for the conference.

Benefits for Authors

We hope that this process will provide the following benefits to authors:

  • Raise the profile of papers containing repeatable computational results by highlighting them at the conference and on-line.
  • Raise the profile of HSCC as a whole, by making it easier to build upon the published results.
  • Provide authors with an incentive to adopt best-practices for code and data management that are known to improve the quality and extendibility of computational results.
  • Provide authors an opportunity to receive feedback from independent reviewers about whether their computational results can be repeated.
  • Obtain a special mention in the conference proceedings, and take part in the competition for the best RE award.

While creating a repeatability package will require some work from the authors, we believe the cost of that extra work is outweighed by a direct benefit to members of the authors’ research lab: if an independent reviewer can replicate the results with a minimum of effort, it is much more likely that future members of the lab will also be able to do so, even if the primary author has departed.

The repeatability evaluation process for HSCC draws upon several similar efforts at other conferences (SIGMOD, SAS, CAV, ECOOP, OOPSLA), and a first experimental run was held at HSCC14.

Repeatability Evaluation Criteria

Each member of the repeatability evaluation committee assigned to review a Repeatability Package (REP) will judge it based on three criteria — coverage, instructions, and quality — where each criteria is assessed on the following scale:

  • Significantly exceeds expectations (5)
  • Exceeds expectations (4)
  • Meets expectations (3)
  • Falls below expectations (2)
  • Missing or significantly falls below expectations (1)

In order to be judged “repeatable” an REP must “meet expectations” (average score of 3), and must not have any missing elements (no scores of 1). Each REP is evaluated independently according to the objective criteria. The higher scores (“exceeds” or “significantly exceeds expectations”) in the criteria should be considered aspirational goals, not requirements for acceptance.


What fraction of the appropriate figures and tables are reproduced by the REP? Note that some figures and tables should not be included in this calculation; for example, figures generated in a drawing program, or tables listing only parameter values. The focus is on those figures or tables in the paper containing computationally generated or processed experimental evidence to support the claims of the paper.

Note that satisfying this criterion does not require that the corresponding figures or tables be recreated in exactly the same format as appears in the paper, merely that the data underlying those figures or tables be generated in a recognizable format.

A repeatable element is one for which the computation can be rerun by following the instructions in the REP in a suitably equipped environment. An extensible element is one for which variations of the original computation can be run by modifying elements of the code and/or data. Consequently, necessary conditions for extensibility include that the modifiable elements be identified in the instructions or documentation, and that all source code must be available and/or involve calls to commonly available and trusted software (eg: Windows, Linux, C or Python standard libraries, Matlab, etc.).

The categories for this criterion are:

  • None (missing / 1): There are no repeatable elements. This case automatically applies to papers which do not submit a REP or papers which contain no computational elements.
  • Some (falls below expectations / 2): There is at least one repeatable element.
  • Most (meets expectations / 3): The majority (at least half) of the elements are repeatable.
  • All repeatable or most extensible (exceeds expectations / 4): All elements are repeatable or most are repeatable and easily modified. Note that if there is only one computational element and it is repeatable, then this score should be awarded.
  • All extensible (significantly exceeds expectations / 5): All elements are repeatable and easily modified.

This criterion is focused on the instructions which will allow another user to recreate the computational results from the paper.

  • None (missing / 1): No instructions were included in the REP.
  • Rudimentary (falls below expectations / 2): The instructions specify a script or command to run, but little else.
  • Complete (meets expectations / 3): For every computational element that is repeatable, there is a specific instruction which explains how to repeat it. The environment under which the software was originally run is described.
  • Comprehensive (exceeds expectations / 4): For every computational element that is repeatable there is a single command which recreates that element almost exactly as it appears in the published paper (eg: file format, fonts, line styles, etc. might not be the same, but the content of the element is the same). In addition to identifying the specific environment under which the software was originally run, a broader class of environments is identified under which it could run.
  • Outstanding (significantly exceeds expectations / 5): In addition to the criteria for a comprehensive set of instructions, explanations are provided of:
    • All the major components / modules in the software
    • Important design decisions made during implementation
    • How to modify / extend the software
    • What environments / modifications would break the software

This criterion explores the documentation and trustworthiness of the software and its results. While a set of scripts which exactly recreate, for example, the figures from the paper certainly aid in repeatability, without well-documented code it is hard to understand how the data in that figure were processed, without well-documented data it is hard to determine whether the input is correct, and without testing it is hard to determine whether the results can be trusted.

If there are tests in the REP which are not included in the paper, they should at least be mentioned in the instructions document. Documentation of test details can be put into the instructions document or into a separate document in the REP.

The categories for this criterion are:

  • None (missing / 1): There is no evidence of documentation or testing.
  • Rudimentary documentation (falls below expectations / 2): The purpose of almost all files is documented (preferably within the file, but otherwise in the instructions or a separate readme file).
  • Comprehensive documentation (meets expectations / 3): The puREPose of almost all files is documented. Within source code files, almost all classes, methods, attributes and variables are given lengthy clear names and/or documentation of their puREPose. Within data files, the format and structure of the data is documented; for example, in comma separated value (csv) files there is a header row and/or comments explaining the contents of each column.
  • Comprehensive documentation and rudimentary testing (exceeds expectations / 4): In addition to the criteria for comprehensive documentation, there are identified test cases with known solutions which can be run to validate at least some components of the code.
  • Comprehensive documentation and testing (significantly exceeds expectations / 5): In addition to the criteria for comprehensive documentation, there are clearly identified unit tests (preferably run with a unit test framework) which exercise a significant fraction of the smaller components of the code (individual functions and classes) and system level tests which exercise a significant fraction of the full package. Unit tests are typically self-documenting, but the system level tests will require documentation of at least the source of the known solution.

Note that tests are a form of documentation, so it is not really possible to have testing without documentation.

Example of Repeatability Evaluation Package and Repeatability Evaluation

  • Citation: Ian M. Mitchell, “Scalable calculation of reach sets and tubes for nonlinear systems with terminal integrators: a mixed implicit explicit formulation” in Hybrid Systems Computation and Control, pp. 103-112 (2011).
  • Official version:
  • Author postprint:
  • Repeatability evaluation package:
  • Repeatability evaluation: meets or exceeds expectations (average 3&#8531).
    • Coverage: All repeatable (exceeds expectations / 4). Code to recreate figures 1-5 and 7-8 is provided. Figure 6 is a hand-drawn coordinate system. There are no tables.
    • Instructions: Complete (meets expectations / 3). The included readme.txt file lists which m-files are used to recreate which figures. The environment is described (Matlab R2010b or later, link to the Toolbox of Level Set Methods). However, some effort is required to extract certain figures (eg: figures 7 & 8).
    • Quality: Comprehensive documentation (meets expectations / 3). All source files include Matlab help entries for every function as well as numerous comments. There are no data files. However, there is no sign of testing.

[Thanks to Ian M. Mitchell for the content of this page]