Replicating results using research software

Derek Jones from The Shape of Code

The reproducibility of results, from scientific studies, has always been an important issue. Over the last few years software has become a hot topic in reproducibility circles; many researchers have an expectation that if they run the original researcher’s software, they will replicate the results. Reality has not lived up to their expectations and there is lots of flapping around looking for a solution. There is a solution, but first, why does the problem exist?

I have spent a lot of time porting software to different compilers (when I was in the compiler business, I wanted everybody to port their applications to the compiler I was working), different hardware (oh, the days when every major vendor had at least one distinct cpu; not like today where it’s x86, ARM, or embedded), different operating systems (umpteen flavors of Unix, all with slightly different header file contents and library behavior; the Unix wars were good for those in the porting business) and every now and again different languages (by translating).

The Wintel alliance wiped out variation in cpus and operating systems (they can still be found lurking in dark corners) and open source compilers created a near monoculture of compilers for the major languages.

The major software portability problems of 30 years ago have become rather minor. But software portability problems that once tended to be minor (at least for scientific software), have grown to become a major headache. Today’s major portability problems center around evolution of the libraries/packages being used, and longer term the evolution of the language(s) used.

Evolution has created development ecosystems where there are rampant dependencies on specific, or earlier than, or later than versions of libraries/packages. I have been out of the porting business for several decades, but talking to those doing it today, the story is the same; experience in porting from A to B is everything, second best is talking to somebody else who has gone in that direction and third best are the one-line forums such as stackoverflow.

Researchers are doing research on who-knows-what and probably have need-to-know knowledge of software and the libraries they are using, the researchers receiving a copy of the original software might know less. What is the probability that the originating and receiving researchers have exactly versions of libraries installed? The receiving researcher may not have any of the needed libraries installed, and promptly install the latest version (which may well be more recent than the one used by the original researcher).

A solution is available; distribute a duplicate of the researchers complete system as a container, e.g., a Docker image.

Containers solve the replication problem. But these days people want more, they actually think it should be possible to take research software and modify it to suite their own needs. Good luck with that.

Research software is written to solve a problem, often by people writing their first non-trivial programs (i.e., they are novices), with no incentive to produce something that is easy for others to use. When software is written by experienced developers, who have an incentive to build something that is easy for others to work with, multiple reimplementations are often still required to achieve something of decent quality. Creating robust software, that others can use, is very hard.

The problem with software is its invisibility; the difficulties are not visible. When the internal operations are visible, the difficulties of making changes are easier to see.

James Albert Bonsack's cigarette rolling machine

James Albert Bonsack’s cigarette rolling machine (from Wikipedia).

Replication: not always worth the effort

Derek Jones from The Shape of Code

Replication is the means by which mistakes get corrected in science. A researcher does an experiment and gets a particular result, but unknown to them one or more unmeasured factors (or just chance) had a significant impact. Another researcher does the same experiment and fails to get the same results, and eventually many experiments later people have figured out what is going on and what the actual answer is.

In practice replication has become a low status activity, journals want to publish papers containing new results, not papers backing up or refuting the results of previously published papers. The dearth of replication has led to questions being raised about large swathes of published results. Most journals only published papers that contain positive results, i.e., something was shown to some level of statistical significance; only publishing positive results produces publication bias (there have been calls for journals that publishes negative results).

Sometimes, repeating an experiment does not seem worth the effort. One such example is: An Explicit Strategy to Scaffold Novice Program Tracing. It looks like the authors ran a proper experiment and did everything they are supposed to do; but, I think the reason that got a positive result was luck.

The experiment involved 24 subjects and these were randomly assigned to one of two groups. Looking at the results (figures 4 and 5), it appears that two of the subjects had much lower ability that the other subjects (the authors did discuss the performance of these two subjects). Both of these subjects were assigned to the control group (there is a 25% chance of this happening, but nobody knew what the situation was until the experiment was run), pulling down the average of the control, making the other (strategy) group appear to show an improvement (i.e., the teaching strategy improved student performance).

Had one, or both, low performers been assigned to the other (strategy) group, no experimental effect would have shown up in the results, significantly reducing the probability that the paper would have been accepted for publication.

Why did the authors submit the paper for publication? Well, academic performance is based on papers published (quality of journal they appear in, number of citations, etc), a positive result is reason enough to submit for publication. The researchers did what they have been incentivized to do.

I hope the authors of the paper continue with their experiments. Life is full of chance effects and the only way to get a solid result is to keep on trying.