Ecosystems chapter of “evidence-based software engineering” reworked

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The Ecosystems chapter of my evidence-based software engineering book has been reworked (I have given up on the idea that this second pass is also where the polishing happens; polishing still needs to happen, and there might be more material migration between chapters); download here.

I have been reading books on biological ecosystems, and a few on social ecosystems. These contain lots of interesting ideas, but the problem is, software ecosystems are just very different, e.g., replication costs are effectively zero, source code does not replicate itself (and is not self-evolving; evolution happens because people change it), and resources are exchanged rather than flowing (e.g., people make deals, they don’t get eaten for lunch). Lots of caution is needed when applying ecosystem related theories from biology, the underlying assumptions probably don’t hold.

There is a surprising amount of discussion on the computing world as it was many decades ago. This is because ecosystem evolution is path dependent; understanding where we are today requires knowing something about what things were like in the past. Computer memory capacity used to be a big thing (because it was often measured in kilobytes); memory does not get much publicity because the major cpu vendor (Intel) spends a small fortune on telling people that the processor is the most important component inside a computer.

There are a huge variety of software ecosystems, but you would not know this after reading the ecosystems chapter. This is because the work of most researchers has been focused on what used to be called the desktop market, which over the last few years the focus has been shifting to mobile. There is not much software engineering research focusing on embedded systems (a vast market), or supercomputers (a small market, with lots of money), or mainframes (yes, this market is still going strong). As the author of an evidence-based book, I have to go where the data takes me; no data, then I don’t have anything to say.

Empirical research (as it’s known in academia) needs data, and the ‘easy’ to get data is what most researchers use. For instance, researchers analyzing invention and innovation invariably use data on patents granted, because this data is readily available (plus everybody else uses it). For empirical research on software ecosystems, the readily available data are package repositories and the Google/Apple Apps stores (which is what everybody uses).

The major software ecosystems barely mentioned by researchers are the customer ecosystem (the people who pay for everything), the vendors (the companies in the software business) and the developer ecosystem (the people who do the work).

Next, the Projects chapter.

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