Reading Group 2014

Measuring Reproducibility in
Computer Systems Research
Emir Muñoz
National University of Ireland Galway
Christian Collberg, Todd Proebsting, Gina Moraila,
Akash Shankaran, Zuoming Shi, Alex M Warren
http://reproducibility.cs.arizona.edu/

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Reproducibility is the ability of an entire experiment
or study to be reproduced, either by the researcher or
by someone else working independently.
DEFINITION
One of the main principles of the scientific method.

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“Unwillingness or inability to share ones work with
fellow researchers hampers the progress of science and
leads to needless replication of work
and the publication of potentially flawed results.”

• Cliché phrases?
• 613 papers with practical orientation from:
– 8 ACM Conferences:
• ASPLOS’12, CCS’12, OOPSLA’12, OSDI’12, PLDI’12,
SIGMOD’12, SOSP’11, VLDB’12
– 5 Journals
• TACO’9, TISSEC’15, TOCS’30, TODS’37, TOPLAS’34
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EXPERIMENT
“Our approach can be applied on ...”
“Our implementation can be found at ...”
“... we implemented out approach”
“code and data can be downloaded from our website”

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Can a CS student build the software within 30 minutes,
including finding and installing any dependent software
and libraries, and without bothering the authors?
Image source: http://jazzadvice.com/

• [Vandewalle et at. 2009] distinguish six degrees of reproducibility:
– 5: The results can be easily reproduced by an independent
researcher with at most 15 min of user effort, requiring only
standard, freely available tools (C compiler, etc.).
– 4: The results can be easily reproduced by an independent
researcher with at most 15 min of user effort, requiring some
proprietary source packages (MATLAB, etc.).
– 3: The results can be reproduced by an independent researcher,
requiring considerable effort.
– 2: The results could be reproduced by an independent researcher,
requiring extreme effort.
– 1: The results cannot seem to be reproduced by an
independent researcher.
– 0: The results cannot be reproduced by an independent
researcher.
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PREVIOUS EXERCISES

• [Stodden 2010] reports about 638 registrants at the NIPS
machine learning conf.
– Why we don’t share the code?
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PREVIOUS EXERCISES
“The time it takes to clean up and document for release”
“Dealing with questions from users about the code”
“The possibility that your code may be used without citation”
“The possibility of patents, or other IP constraints”
“Competitors may get an advantage”

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METHODOLOGY
No attempt to check the
consistency of the claims
made in the original paper.

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METHODOLOGY
Excluded
Non-reproducible
No contact

9.8%
17.4%
26.0%
34.4%
44.4%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%
Reproducibility
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RESULTS

• The National Science Foundation’s (NFS) Gran
Policy Manual states that:
– Investigators are expected to share with other
researchers...
– Investigators and grantee are encouraged to share
software and inventions...
– ... Responsibility that investigators and organizations
have as members of the scientific and engineering
community, to make results, data and collections
available to other researchers.
• Industry
– Papers with only authors from industry have a low
rate or reproducibility
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RESULTS

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Image source: www.funnyjunk.com
• Versioning Problems
• Code Will be Available Soon
• Programmer Left
• Bad Backup Practices
• Commercial Code
• Proprietary Academic Code
• Unavailable Subsystems
• Multiple Reasons
• Intellectual Property
• Research vs. Sharing
• Security and Privacy
• Poor Design
• Too Busy to Help
So, What Were Their Excuses?

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RESULTS
Attached is the (system) source code of our algorithm. I’m not very sure whether it is
the final version of the code used in our paper, but it should be at least 99% close.
Thank you for your interest in our work. Unfortunately the current system is not mature
enough at the moment, so it’s not yet publicly available...
I am afraid that the source code was never released. The code was never intended to be
released so is not in any shape for general use.
(STUDENT) was a graduate student in our program but he left a while back so I am
responding instead...
Thanks ... Unfortunately, the server in which my implementation was stored had a disk
crash in April and three disks crashed simultaneously...
The code is owned by (COMPANY), ...is not open-source...You best bet is to reimplement
:( Sorry
...sources are not meant to be opensource..I do not have the liberty of making available
The source code at my current institution (UNIVERSITY)...

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Most importantly, I do not have the bandwidth to help anyone
come up to speed on this stuff.
RESULTS

• Conferences to require the code along with
every paper submitted
• Build special tools that can run reliably and
with reproducible results
• Build web sites that allow authors to make
their code available to colleagues
• Do not follow the bad habits like “publish
and forget” style of scientific research
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RECOMMENDATIONS

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RECOMMENDATIONS
Grammar for sharing specifications

1. Unless you have compelling reasons not to, plan to
release the code.
2. Students will leave, plan for it.
3. Create permanent email addresses.
4. Create project websites.
5. Use a source code control system.
6. Backup your code.
7. Resolve licensing issues.
8. Keep your promises.
9. Plan for longevity.
10. Avoid cool but unusual design.
11. Plan for Reproducible Releases.
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LESSONS LEARNED

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Bash code!!
Run button

Output
Visualization

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Reproducible Research in Computational Science
Roger D. Peng
http://www.sciencemag.org/content/334/6060/1226.full

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Rule 1:
For Every Result,
Keep Track of
How It Was Produced
Ten Simple Rules for Reproducible Computational Research
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003285

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Rule 2:
Avoid Manual Data
Manipulation Steps

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Rule 3:
Archive the Exact
Versions of All External
Programs Used

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Rule 4:
Version Control
All Custom Scripts

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Rule 5:
Record All Intermediate
Results, When Possible
In Standardized Formats

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Rule 6:
For Analyses That
Include Randomness,
Note Underlying
Random Seeds

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Rule 7:
Always Store Raw
Data behind Plots

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Rule 8:
Generate Hierarchical
Analysis Output,
Allowing Layers
of Increasing Detail
to Be Inspected

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Rule 9:
Connect Textual
Statements to
Underlying Results

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Rule 10:
Provide Public Access
to Scripts, Runs,
and Results

• As a discipline, we are a long way from
reproducing research that is always, and
completely, reproducible.
• To share may increase the probabilities of
citation.
• The sharing specifications will have a positive
effect on researchers’ willingness to share.
• Sharing specifications can be used as a
contract between authors and readers.
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CONCLUSION

• Data Quality and Trustworthiness
– How close is this data to the real-world?
– Can I trust in this data?
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HOW THIS IS RELATED TO MY PHD
Data is The New (Black) Gold

• Data Replication & Reproducibility
– http://www.sciencemag.org/site/special/data-rep/
• Getting Results from Testing by Laura Dillon
(ACM Distinguished Speakers Program)
– http://dsp.acm.org/view_lecture.cfm?lecture_id=108
• Why You Should Share Your Musical Knowledge
– http://jazzadvice.com/why-you-should-share-your-
musical-knowledge/
• Reproducible Research in Signal Processing
– http://rr.epfl.ch/17/1/VandewalleKV09.pdf
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FURTHER LITERATURE

• RunMyCode enables scientists to openly
share the code and data that underlie their
research publications
– http://www.runmycode.org/
• Executable Papers
– http://executablepapers.com/
• CDE: Automatically create portable Linux
applications (i.e., package, deliver, run).
– http://www.pgbovine.net/cde.html
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FURTHER LITERATURE

• VLDB Guidelines
– http://www.vldb.org/2013/experimental_reprodu
cibility.html
• Data Package Management
– http://dat-data.com/
– https://github.com/maxogden/dat
• Data Dryad
– http://datadryad.org/
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FURTHER LITERATURE

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