Ichthyostega
92bc044e9e
Using random or pseudo-random numbers as input for tests can be a very effective tool to spot unintended behaviour in corner cases, and also helps writing more principled test verifications. However, investigating failures in randomised tests can be challenging. A well-proven solution is to exploit the **determinism** of pseudo-random-numbers by documenting a randomly generated seed, that can be re-injected for investigation. Up to now, most tests rely on the old library function `rand()`, while at some places already the C++ standard framework for random number generation is used, packaged into a custom wrapper. Adding adequate support for documented seed values seems to be easy to achieve, after switching existing usages of `rand()` to a suitable drop-in replacement. After some consideration, I decided ''against'' wiring random generator instances explicitly, while allowing to do so on occasion, when necessary. Thus the planned seeding mechanism will rather re-seed a ''implicit default'' generator, which could then be used to construct explicit generator instances when required (e.g. for multithreaded tests) As a starting point, this changeset replaces the `randomise()` API call by a direct access to the ''reseeding functionality'' exposed by the C++ framework and all default generators. Since we already provide a dedicated static instance of the plattform entropy source, re-randomisation can be achieved by seeding from there. NOTE: there was extended debate in the net, questioning the viability of the `std::random_seq` -- these arguments, while valid from a theoretical point of view, seem rather moot when placed into a practical context, where even 2^32 different generation-paths(cycles) are more than enough to provide sufficient diffusion of results (unless the goal is really to engage into Monte-Carlo simulations for scientific research or large model simulations). Notable most of the more catchy reprovals raised by Melissa O'Neill have been refuted by experts of the field, even while being still propagated at various places in the net, often combined with promoting PCG-Random.
91 lines
2.2 KiB
C++
91 lines
2.2 KiB
C++
/*
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random.cpp - storage and implementation for random number framework
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Copyright (C) Lumiera.org
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2024, Hermann Vosseler <Ichthyostega@web.de>
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This program is free software; you can redistribute it and/or
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modify it under the terms of the GNU General Public License as
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published by the Free Software Foundation; either version 2 of
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the License, or (at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program; if not, write to the Free Software
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Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
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* *****************************************************/
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/** @file random.cpp
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** Implementation details and common storage for random number generation.
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** @todo 3/2024 work out how some executions or performances are configured reproducibly.
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*/
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#include "lib/random.hpp"
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#include <string>
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using std::string;
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namespace lib {
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namespace {
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const string ENTROPY_SOURCE_SPEC{"/dev/random"};
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class EntropyNucleus
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: public SeedNucleus
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{
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std::random_device entropySource_;
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uint64_t
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getSeed() override
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{
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return entropySource_();
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}
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public:
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EntropyNucleus(string const& SPEC)
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: entropySource_{SPEC}
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{ }
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};
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class EternalNucleus
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: public SeedNucleus
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{
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uint64_t
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getSeed() override
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{
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return LIFE_AND_UNIVERSE_4EVER;
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}
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};
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/** static global entropy source instance */
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EntropyNucleus entropyNucleus{ENTROPY_SOURCE_SPEC};
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/// @todo this one should somehow be configurable
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EternalNucleus eternalNucleus;
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}
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SeedNucleus::~SeedNucleus() { }
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Random defaultGen{eternalNucleus};
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Random entropyGen{entropyNucleus};
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void
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randomiseRandomness()
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{
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entropyGen.reseed(entropyNucleus);
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defaultGen.reseed(entropyNucleus);
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}
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} // namespace lib
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