At ICSE 2012, one of the presented papers caught my attention; the title was provocative enough and the topic was very hot: functional vs imperative programming. The paper presented a comparative study of programming a multicore application in Java and Scala. The authors employed a group of master students to write a non-toy application in both languages and then compared the results. They found no significant difference between the two languages.

I remember leaving the paper presentation with mixed feelings; my suspicions grew stronger when I actually read the paper. There were several errors in the paper with respect to the methods used and the statistical treatment of the data. Together with my colleague Panos Louridas, we wrote a paper that criticizes the methods used in the Pankratius et al. paper. Partially because only the paper abstract was published in the print version of SigSoft Software Engineering notes, our criticism went relatively unnoticed. Since today marks the first anniversary of the writing of this paper, I am summarizing our findings here. You can also read the full version.

Problems we found

  • Wrong statistical tests being used or wrong naming of the statistical tests

  • Liberal interpretation of p-values. While the authors use p < 0.05 as a threshold for significance, they later claim significance (or support) for p-values of 0.078 and even 0.094

  • Subjects were classified as experts in Scala after 4 weeks of training while other subjects were classified as novices in Java after 4 years of university studies.

  • The method used to identify imperative and functional parts of the code classified as imperative an example created by Martin Odersky to showcase the functional programming capabilities of the language.

How to fix them?

We would have encountered none of the problems outlined above if published papers included:

  • All measurement data
  • All interviews, questionnaire, research protocols, and other related data derived from subjects, anonymized if necessary
  • Full details on the statistical methods used.
  • Any other code that has been used in the publication’s research
  • Documentation for all of the above

Conferences and journals should require from authors to open up their data and their data manipulation tools under a license that enables everybody to use them. Sharing of data should happen in an organized way; for example, conference organization committees could create a shared repository where researchers can upload their data and tools along with instructions to use them. To enable full replication, researchers should provide virtual machine images with the full environment and data they used. Moreover, conferences and journals can describe a formal redress procedure; should an error is found in a paper, authors should be required to reply to the error claim.

What we propose can be a best effort approach: by default, submissions should be accompanied by datasets and tools; if these are not available due to force majeure, it should be up to the editor/conference chair to decide on the submission.

Conclusions

The purpose of this work was not to point fingers, but to raise the issue of the dangers of inadequate reproducibility. We were drawn to this particular article and use it as an example mostly because some of the findings contradict our own experience. Other articles in the same conference are equally opaque with regards to replication and verification. However, we believe that publication-time availability of experimental data, tools and experiment replication documentation should be a requirement for publication. Our proposal, if adopted, might be a first step in this direction.



Published

01 July 2013

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