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Sampling is now an issue in software engineering research

Data analysis in software engineering often has to make do with measurements extracted from the handful of measurable/measured instances at hand, but every now and again the abundance of stuff to measure is such that a subset has to be selected. How should the subset be selected?

Population sampling is a well established part of statistics and a variety of terms have sprung up to label the various strategies used. I think ‘Accidental sampling’ accurately describes the provenance of many software engineering datasets seen in research papers and some of my work. It is quite common to see academic papers using exactly the same sample as previously published papers, perhaps a new term is needed to describe using samples that are identical to those used in previously papers: lazy sampling, coat-tail sampling…

Program source code, which was once so hard to obtain in any significant quantity, is now available by the terabyte load, a population that all but the fastest analysis and most general of questions warrant processing as a whole.

The question being asked can itself intrinsically lead to a reduction in the size of the population, e.g., properties of programs written in X, or programs with more than 10 active developers.

What should be the unit of sampling? The package making up a standalone system/library is a common choice (e.g., all the files in the tar or zip archive from which binaries are built); this can result in unexpected source files being included in the measurement process, such as test programs. A less common choice is to use individual source files as the sampling unit (it is so much easier to randomly select a list of packages, download, extract and measure them one by one).

Are the source file characteristics of the contents of 1,000 packages statistically very similar to 25,000 files obtained by randomly selecting 1 file from 25,000 packages? I don’t know.

A recent paper by Nagappan, Zimmermann and Bird proposes a sampling algorithm which looks like quota or coverage sampling in that candidate similarity to the current sample is used to decide whether to add that candidate (too much similarity results in exclusion). The authors misleadingly associate the term ‘representativeness’ with this algorithm, where common statistical usage of representative requires that if 40% of a population have attribute Z then 40% of a sample’s members will have this attribute (within sampling tolerances).

If software engineering research is to be useful to commercial software engineering any discoveries need to be applicable to samples outside of those used in the original analysis. At the moment researchers are having a hard enough time finding any useful patterns in their data, this is not a reason to continue with the practice of coat-tail sampling and we all need to start addressing sampling issues.

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