This link has been bookmarked by 12 people . It was first bookmarked on 07 Sep 2006, by someone privately.
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29 Jun 09
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07 Dec 07
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David WileyPowerful concepts like encapsulation were supposed to save people from themselves while developing software, but encapsulation fails for global properties or when software evolution and wholesale changes are needed. Open Source handles this better. It’s
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06 Dec 07
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20 Oct 07
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statistical behavior
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adaptation
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self-organization
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massive and complex systems
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The object-oriented approach does not adequately address the computing requirements of the future.
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University and industrial research communities retreated from innovating in programming languages in order to harvest the easy pickings from the OO tree.
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Beyond static types, precise interfaces, and mathematical reasoning, we need self-healing and self-organizing mechanisms, checking for and responding to failures, and managing systems whose overall complexity is beyond the ken of any single person.
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But with C++ and Java, the dynamic thinking fostered by object-oriented languages was nearly fatally assaulted by the theology of static thinking inherited from our mathematical heritage
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And as a result we find that object-oriented languages have succumbed to static thinkers who worship perfect planning over runtime adaptability, early decisions over late ones, and the wisdom of compilers over the cleverness of failure detection and repair.
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Objects, as envisioned by the designers of languages like Smalltalk and Actors—long before C++ and Java came around— were for modeling and building complex, dynamic worlds. Programming environments for languages like Smalltalk were written in those languages and were extensible by developers. Because the philosophy of dynamic change was part of the post-Simula OO worldview, languages and environments of that era were highly dynamic.
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In the new world we will foreground robustness, flexibility, adaptation, distributed systems, multiple-author programs, and biological metaphors for computing.
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In the old world, we focussed on efficiency, resource limitations, performance, monolithic programs, standalone systems, single author programs, and mathematical approaches.
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07 Sep 06
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01 Feb 06
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16 Jan 04
Seb PaquetWhat can it mean for a programming paradigm to fail? A paradigm fails when the narrative it embodies fails to speak truth or when its proponents embrace it beyond reason.
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