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Computing academics destined to remain software engineering virgins
If you want to have a sensible conversation about software engineering with an academic, the best departments to search are Engineering and Physics (these days perhaps also Biology). Here you are much more likely to find people who have had to write large’ish programs to solve some research problem, than in the Computing department; they will understand what you are talking about because they have been there.
A lot of academics in Computing departments hold some seriously strange views about how non-trivial software engineering is done (but not the few who have actually written large programs). I recently had a moment of insight, these academics are treating the task of creating a large program as if it were just like coding up an algorithm, but bigger. I don’t know why I did not think of this before.
Does this insight have any practical use? Should I stop telling academics that algorithms are often not that important in solving a problem (I now understand why this comment baffles so many of them)?
Why don’t many computer science academics get involved in writing large programs? Its not an efficient use of their time (as more than one has explained to me); academics are rated by the number of papers they publish (plus the quality of the publishing journals and citations, etc) and the publishable paper/time ratio for large software development projects is not attractive for risk averse academics (which most of them are; any intrepid seekers of knowledge are soon hammered down by the bureaucracy, or leave for industry).
A power law artifact
Over the last few years software engineering academics have jumped aboard the power-law band-wagon (examples here and here). With few exceptions (one here) these researchers have done little more that plot their data on a log-log graph and shown that a straight line is a good fit for many of the points. What a sorry state of affairs.
Cognitive psychologists have also encountered straight lines in log-log graphs, but they have been in the analysis of data business much longer and are aware that there might be other distributions that are just as straight in the same places.
A very interesting paper, Toward an explanation of the power law artifact: Insights from response surface analysis, shows how averaging data obtained from a variety of sources (example given is the performance of different subjects in a psychology experiment) can produce a power law where none originally existed. The underlying fault could be that data from a non-linear system is being averaged using the arithmetic mean (I suspect that I have done this in the past), which it turns out should only be used to average data from a linear system. The authors list the appropriate averaging formula that should be used for various non-linear systems.
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