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Software research is 200 years behind biology research
Evidence-based software research requires access to data, and Github has become the primary source of raw material for many (most?) researchers.
Parallels are starting to emerge between today’s researchers exploring Github and biologists exploring nature centuries ago.
Centuries ago scientific expeditions undertook difficult and hazardous journeys to various parts of the world, collecting and returning with many specimens which were housed and displayed in museums and botanical gardens. Researchers could then visit the museums and botanical gardens to study these specimens, without leaving the comforts of their home country. What is missing from these studies of collected specimens is information on the habitat in which they lived.
Github is a living museum of specimens that today’s researchers can study without leaving the comforts of their research environment. What is missing from these studies of collected specimens is information on the habitat in which the software was created.
Github researchers are starting the process of identifying and classifying specimens into species types, based on their defining characteristics, much like the botanist Carl_Linnaeus identified stamens as one of the defining characteristics of flowering plants. Some of the published work reads like the authors did some measurements, spotted some differences, and then invented a plausible story around what they had found. As a sometime inhabitant of this glasshouse I will refrain from throwing stones.
Zoologists study the animal kingdom, and entomologists specialize in the insect world, e.g., studying Butterflys. What name might be given to researchers who study software source code, and will there be specialists, e.g., those who study cryptocurrency projects?
The ecological definition of a biome, as the community of plants and animals that have common characteristics for the environment they exist in, maps to the end-user use of software systems. There does not appear to be a generic name for people who study the growth of plants and animals (or at least I cannot think of one).
There is only so much useful information that can be learned from studying specimens in museums, no matter how up to date the specimens are.
Studying the development and maintenance of software systems in the wild (i.e., dealing with the people who do it), requires researchers to forsake their creature comforts and undertake difficult and hazardous journeys into industry. While they are unlikely to experience any physical harm, there is a real risk that their egos will be seriously bruised.
I want to do what I can to prevent evidence-based software engineering from just being about mining Github. So I have a new policy for dealing with PhD/MSc student email requests for data (previously I did my best to point them at the data they sought). From now on, I will tell students that they need to behave like real researchers (e.g., Charles Darwin) who study software development in the wild. Charles Darwin is a great role model who should appeal to their sense of adventure (alternative suggestions welcome).
Describing software engineering in terms of a traditional science
If you were asked to describe the ‘building stuff’ side of software engineering, by comparing it with one of the traditional sciences, which science would you choose?
I think a lot of people would want to compare it with Physics. Yes, physics envy is not restricted to the softer sciences of humanities and liberal arts. Unlike physics, software engineering is not governed by a handful of simple ‘laws’, it’s a messy collection of stuff.
I used to think that biology had all the necessary important characteristics needed to explain software engineering: evolution (of code and products), species (e.g., of editors), lifespan, and creatures are built from a small set of components (i.e., DNA or language constructs).
Now I’m beginning to think that chemistry has aspects that are a better fit for some important characteristics of software engineering. Chemists can combine atoms of their choosing to create whatever molecule takes their fancy (subject to bonding constraints, a kind of syntax and semantics for chemistry), and the continuing existence of a molecule does not depend on anything outside of itself; biological creatures need to be able to extract some form of nutrient from the environment in which they live (which is also a requirement of commercial software products, but not non-commercial ones). Individuals can create molecules, but creating new creatures (apart from human babies) is still a ways off.
In chemistry and software engineering, it’s all about emergent behaviors (in biology, behavior is just too complicated to reliably say much about). In theory the properties of a molecule can be calculated from the known behavior of its constituent components (e.g., the electrons, protons and neutrons), but the equations are so complicated it’s impractical to do so (apart from the most simple of molecules; new properties of water, two atoms of hydrogen and one of oxygen, are still being discovered); the properties of programs could be deduced from the behavior its statements, but in practice it’s impractical.
What about the creative aspects of software engineering you ask? Again, chemistry is a much better fit than biology.
What about the craft aspect of software engineering? Again chemistry, or rather, alchemy.
Is there any characteristic that physics shares with software engineering? One that stands out is the ego of some of those involved. Describing, or creating, the universe nourishes large egos.
Success: Software engineering data is starting to become very dull
A few years ago it was unusual for the author(s) of a paper in software engineering to make their data public (and on top of that it was rare to encounter a paper that actually made use of empirical data). The situation now is that I am having trouble keeping up with all the papers that include a link to downloadable data. Part of the problem is that I will pay a lot more attention to papers that come with data, having lived through a long famine I have not yet adjusted to the greater abundance. I’m sure that journal editors and referees are in the same boat and are being lured by accompanying data to accept paper for publication that they would otherwise have rejected.
This growing quantity of empirical software engineering data means we can now start thinking about what data is useful to have and what data is not so useful. Data is useful if it highlights a pattern of behavior that can be used to help reduce the resources needed to create/maintain software.
To get a handle on estimating data usefulness we need a model of research in software engineering. While many have used Physics as the model for software engineering research (i.e., a few simple universal laws that apply everywhere), I think Biology is a much better fit.
Software is written in different habitats environments (e.g., small teams, large teams) and targets different habitats environments (e.g., embedded, desktop, mobile, supercomputer) using different techniques and driven by different predators/prey market forces (e.g., release first/quickly, be reliable). Yes there are common drivers, just as the living things studied by biologists share a common need to eat, sleep and reproduce.
Like biology, the bulk of software engineering research is about the study of niche topics, with some small percentage of researchers trying to build theories that tie everything together at one level or another to create bigger pictures.
This model of software engineering research means estimating the usefulness of data probably requires some knowledge of the niche to which it applies. It also means that a particular data set might not be useful yet because it needs to be combined with other data, that does not yet exist (perhaps it was collected first because it was easier to do).
So in a space of a few years most software engineering data has gone from being very interesting (because it is rare) to being very dull (because it is harder to stand out in a crowd).
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