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A Grain Of Salt
Somehow, I stumbled upon an academic paper that compares programming language performance in the context of computing the results for a well-known, computationally dense, macro-economics problem: “the stochastic neoclassical growth model“. Since the results hoist C++ on top of the other languages, I felt the need to publish the researchers’ findings in this blog post :). As with all benchmarks, take it with a grain of salt because… context is everything.
Qualitative Findings
Quantitative Findings
The irony of this post is that I’m a big fan of Nassim Taleb, whose lofty goal is to destroy the economics profession as we know it. He thinks all the fancy, schmancy mathematical models and metrics used by economists (including famous Nobel laureates) to predict the future are predicated on voodoo science. They cause more harm than good by grossly misrepresenting and underestimating the role of risk in their assumptions and derived equations.
Staying Sane
Standardization is long periods of mind-numbing boredom interrupted by moments of sheer terror – Bjarne Stroustrup
In his ACCU2013 talk, “C++14 Early Thoughts“, Bjarne Stroustrup presented this slide:
By using “we” in each bullet point, Bjarne was referring to the ISO WG-1 C++ committee and the daunting challenges it faces to successfully move the language forward.
Not only do the committee leaders have to manage the external onslaught of demands from a huge, dedicated user base, they have to homogenize the internal communications amongst many smart and assertive members. To illustrate the internal management problem, Bjarne said something akin to: “There is no topic the committee isn’t willing to discuss at length for two years“.
In order to prevent being overwhelmed with work, the committee uses this set of grass roots principles to filter out the incoming chaff from the wheat:
I have no idea how internal conflicts are handled, nor how infinite loops of technical debate are exited, but since the all-volunteer committee is still functioning and doing a great job (IMO) of modernizing the language, there’s got to be some magic at work here:
Defect Density
ln “Software’s Hidden Clockwork: A General Theory of Software Defects“, Les Hatton presents these two interesting charts:
The thing I find hard to believe is that Les has concluded that there is no obvious significant relationship between defect density and the choice of programming language. But notice that he doesn’t seem to have any data points on his first chart for the relatively newer, less “tricky“, and easier-to-program languages like Java, C#, Ruby, Python, et al.
So, do you think Les might have jumped the gun here by prematurely asserting the virtual independence of defect density on programming language?
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WTF?
Meh!
D'oh!
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