2015-04-02 03:30:20 +02:00
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/*
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2016-02-26 22:57:49 +01:00
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TreeMutator(Test) - customisable intermediary to abstract tree changing operations
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2015-04-02 03:30:20 +02:00
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Copyright (C) Lumiera.org
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2015, 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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2017-02-22 01:54:20 +01:00
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/** @file tree-mutator-test.cpp
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2017-02-22 03:17:18 +01:00
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** unit test \ref TreeMutator_test
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2016-11-03 18:20:10 +01:00
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*/
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2015-04-02 03:30:20 +02:00
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#include "lib/test/run.hpp"
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2015-08-28 23:09:10 +02:00
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#include "lib/format-util.hpp"
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2015-04-02 03:30:20 +02:00
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#include "lib/test/test-helper.hpp"
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#include "lib/diff/tree-mutator.hpp"
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2016-01-07 03:58:29 +01:00
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#include "lib/format-cout.hpp"
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2016-08-29 23:04:44 +02:00
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#include "lib/format-util.hpp"
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2015-04-02 03:30:20 +02:00
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#include "lib/util.hpp"
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#include <string>
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2016-08-29 23:04:44 +02:00
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#include <vector>
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2015-04-02 03:30:20 +02:00
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using util::isnil;
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2016-08-29 23:04:44 +02:00
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using util::join;
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2016-01-08 08:20:59 +01:00
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using util::typeStr;
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settle on a concrete implementation approach based on inheritance chain
After some reconsideration, I decide to stick to the approach with the closures,
but to use a metaprotramming technique to build an inheritance chain.
While I can not decide on the real world impact of storing all those closures,
in theory this approach should enable the compiler to remove all of the
storage overhead. Since, when storing the result into an auto variable
right within scope (as demonstrated in the test), the compiler
sees the concrete type and might be able to boil down the actual
generated virtual function implementations, thereby inlining the
given closures.
Whereas, on the other hand, if we'd go the obvious conventional route
and place the closures into a Map allocated on the stack, I wouldn't
expect the compiler to do data flow analysis to prove this allocation
is not necessary and inline it away.
NOTE: there is now guarantee this inlining trick will ever work.
And, moreover, we don't know anything regarding the runtime effect.
The whole picture is way more involved as it might seem at first sight.
Even if we go the completely conventional route and require every
participating object to supply an implementation of some kind of
"Serializable" interface, we'll end up with a (hand written!)
implementation class for each participating setup, which takes
up space in the code segment of the executable. While the closure
based approach chosen here, consumes data segment (or heap) space
per instance for the functors (or function pointers) representing
the closures, plus code segment space for the closures, but the
latter with a way higher potential for inlining, since the closure
code and the generated virtual functions are necessarily emitted
within the same compilation unit and within a local (inline, not
publickly exposed) scope.
2015-04-05 18:26:49 +02:00
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2016-08-29 23:04:44 +02:00
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using std::string;
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using std::vector;
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2015-04-02 03:30:20 +02:00
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namespace lib {
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namespace diff{
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namespace test{
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/*****************************************************************************//**
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* @test Demonstrate a customisable component for flexible bindings
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* to enable generic tree changing and mutating operations to
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* arbitrary hierarchical data structures.
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2016-08-29 23:04:44 +02:00
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* - we use lambdas to link into our private implementation
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* - this test demonstrates the behaviour of an attribute setter
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* - plus some of the _primitive operations_ available on collections
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2015-04-02 03:30:20 +02:00
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*
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* @see TreeMutator
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2016-08-29 23:04:44 +02:00
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* @see DiffComplexApplication_test a way more complex usage scenario
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2015-04-02 03:30:20 +02:00
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* @see GenNodeBasic_test
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2016-02-26 22:57:49 +01:00
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* @see GenericRecordRepresentation_test
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2015-04-02 03:30:20 +02:00
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*/
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2016-02-26 22:57:49 +01:00
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class TreeMutator_test : public Test
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2015-04-02 03:30:20 +02:00
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{
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virtual void
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run (Arg)
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{
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simpleAttributeBinding();
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2016-08-29 23:04:44 +02:00
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simpleCollectionBinding();
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2015-04-02 03:30:20 +02:00
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}
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void
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simpleAttributeBinding()
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{
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2016-08-29 23:04:44 +02:00
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MARK_TEST_FUN;
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2015-04-02 03:30:20 +02:00
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string localData;
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settle on a concrete implementation approach based on inheritance chain
After some reconsideration, I decide to stick to the approach with the closures,
but to use a metaprotramming technique to build an inheritance chain.
While I can not decide on the real world impact of storing all those closures,
in theory this approach should enable the compiler to remove all of the
storage overhead. Since, when storing the result into an auto variable
right within scope (as demonstrated in the test), the compiler
sees the concrete type and might be able to boil down the actual
generated virtual function implementations, thereby inlining the
given closures.
Whereas, on the other hand, if we'd go the obvious conventional route
and place the closures into a Map allocated on the stack, I wouldn't
expect the compiler to do data flow analysis to prove this allocation
is not necessary and inline it away.
NOTE: there is now guarantee this inlining trick will ever work.
And, moreover, we don't know anything regarding the runtime effect.
The whole picture is way more involved as it might seem at first sight.
Even if we go the completely conventional route and require every
participating object to supply an implementation of some kind of
"Serializable" interface, we'll end up with a (hand written!)
implementation class for each participating setup, which takes
up space in the code segment of the executable. While the closure
based approach chosen here, consumes data segment (or heap) space
per instance for the functors (or function pointers) representing
the closures, plus code segment space for the closures, but the
latter with a way higher potential for inlining, since the closure
code and the generated virtual functions are necessarily emitted
within the same compilation unit and within a local (inline, not
publickly exposed) scope.
2015-04-05 18:26:49 +02:00
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auto mutator =
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TreeMutator::build()
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2015-05-03 05:24:06 +02:00
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.change("data", [&](string val)
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settle on a concrete implementation approach based on inheritance chain
After some reconsideration, I decide to stick to the approach with the closures,
but to use a metaprotramming technique to build an inheritance chain.
While I can not decide on the real world impact of storing all those closures,
in theory this approach should enable the compiler to remove all of the
storage overhead. Since, when storing the result into an auto variable
right within scope (as demonstrated in the test), the compiler
sees the concrete type and might be able to boil down the actual
generated virtual function implementations, thereby inlining the
given closures.
Whereas, on the other hand, if we'd go the obvious conventional route
and place the closures into a Map allocated on the stack, I wouldn't
expect the compiler to do data flow analysis to prove this allocation
is not necessary and inline it away.
NOTE: there is now guarantee this inlining trick will ever work.
And, moreover, we don't know anything regarding the runtime effect.
The whole picture is way more involved as it might seem at first sight.
Even if we go the completely conventional route and require every
participating object to supply an implementation of some kind of
"Serializable" interface, we'll end up with a (hand written!)
implementation class for each participating setup, which takes
up space in the code segment of the executable. While the closure
based approach chosen here, consumes data segment (or heap) space
per instance for the functors (or function pointers) representing
the closures, plus code segment space for the closures, but the
latter with a way higher potential for inlining, since the closure
code and the generated virtual functions are necessarily emitted
within the same compilation unit and within a local (inline, not
publickly exposed) scope.
2015-04-05 18:26:49 +02:00
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{
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2015-05-02 01:39:58 +02:00
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cout << "\"data\" closure received something "<<val<<endl;
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settle on a concrete implementation approach based on inheritance chain
After some reconsideration, I decide to stick to the approach with the closures,
but to use a metaprotramming technique to build an inheritance chain.
While I can not decide on the real world impact of storing all those closures,
in theory this approach should enable the compiler to remove all of the
storage overhead. Since, when storing the result into an auto variable
right within scope (as demonstrated in the test), the compiler
sees the concrete type and might be able to boil down the actual
generated virtual function implementations, thereby inlining the
given closures.
Whereas, on the other hand, if we'd go the obvious conventional route
and place the closures into a Map allocated on the stack, I wouldn't
expect the compiler to do data flow analysis to prove this allocation
is not necessary and inline it away.
NOTE: there is now guarantee this inlining trick will ever work.
And, moreover, we don't know anything regarding the runtime effect.
The whole picture is way more involved as it might seem at first sight.
Even if we go the completely conventional route and require every
participating object to supply an implementation of some kind of
"Serializable" interface, we'll end up with a (hand written!)
implementation class for each participating setup, which takes
up space in the code segment of the executable. While the closure
based approach chosen here, consumes data segment (or heap) space
per instance for the functors (or function pointers) representing
the closures, plus code segment space for the closures, but the
latter with a way higher potential for inlining, since the closure
code and the generated virtual functions are necessarily emitted
within the same compilation unit and within a local (inline, not
publickly exposed) scope.
2015-04-05 18:26:49 +02:00
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localData = val;
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});
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2015-04-02 03:30:20 +02:00
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2015-05-02 01:39:58 +02:00
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cout << "concrete TreeMutator size=" << sizeof(mutator)
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2016-01-08 08:20:59 +01:00
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<< " type="<< typeStr(mutator)
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2015-05-02 01:39:58 +02:00
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<< endl;
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2015-04-02 03:30:20 +02:00
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2016-10-04 03:24:44 +02:00
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mutator.init();
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2015-04-02 03:30:20 +02:00
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CHECK (isnil (localData));
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2016-05-28 03:41:03 +02:00
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string testValue{"that would be acceptable"};
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mutator.assignElm ({"lore", testValue});
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2015-05-02 01:39:58 +02:00
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CHECK ( isnil (localData)); // nothing happens, nothing changed
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2016-05-28 03:41:03 +02:00
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mutator.assignElm ({"data", testValue});
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settle on a concrete implementation approach based on inheritance chain
After some reconsideration, I decide to stick to the approach with the closures,
but to use a metaprotramming technique to build an inheritance chain.
While I can not decide on the real world impact of storing all those closures,
in theory this approach should enable the compiler to remove all of the
storage overhead. Since, when storing the result into an auto variable
right within scope (as demonstrated in the test), the compiler
sees the concrete type and might be able to boil down the actual
generated virtual function implementations, thereby inlining the
given closures.
Whereas, on the other hand, if we'd go the obvious conventional route
and place the closures into a Map allocated on the stack, I wouldn't
expect the compiler to do data flow analysis to prove this allocation
is not necessary and inline it away.
NOTE: there is now guarantee this inlining trick will ever work.
And, moreover, we don't know anything regarding the runtime effect.
The whole picture is way more involved as it might seem at first sight.
Even if we go the completely conventional route and require every
participating object to supply an implementation of some kind of
"Serializable" interface, we'll end up with a (hand written!)
implementation class for each participating setup, which takes
up space in the code segment of the executable. While the closure
based approach chosen here, consumes data segment (or heap) space
per instance for the functors (or function pointers) representing
the closures, plus code segment space for the closures, but the
latter with a way higher potential for inlining, since the closure
code and the generated virtual functions are necessarily emitted
within the same compilation unit and within a local (inline, not
publickly exposed) scope.
2015-04-05 18:26:49 +02:00
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CHECK (!isnil (localData));
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2015-05-02 01:39:58 +02:00
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cout << "localData changed to: "<<localData<<endl;
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CHECK (localData == "that would be acceptable");
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2015-04-02 03:30:20 +02:00
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}
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void
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2016-08-29 23:04:44 +02:00
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simpleCollectionBinding()
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2015-04-02 03:30:20 +02:00
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{
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2016-08-29 23:04:44 +02:00
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MARK_TEST_FUN;
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vector<string> values;
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values.push_back("a");
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values.push_back("b");
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cout << join(values) <<endl;
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CHECK (2 == values.size());
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CHECK ("a, b" == join(values));
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auto mutator =
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TreeMutator::build()
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.attach (collection(values));
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cout << "concrete TreeMutator size=" << sizeof(mutator)
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<< " type="<< typeStr(mutator)
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<< endl;
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2016-10-04 03:24:44 +02:00
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mutator.init();
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2016-08-29 23:04:44 +02:00
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CHECK (isnil (values));
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CHECK (mutator.matchSrc (GenNode("a")));
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mutator.skipSrc (GenNode("a"));
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CHECK (mutator.matchSrc (GenNode("b")));
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CHECK (mutator.injectNew (GenNode("c")));
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CHECK (mutator.acceptSrc (GenNode("b")));
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cout << join(values) <<endl;
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CHECK (2 == values.size());
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CHECK ("c, b" == join(values));
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2015-04-02 03:30:20 +02:00
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}
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};
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/** Register this test class... */
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2016-02-26 22:57:49 +01:00
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LAUNCHER (TreeMutator_test, "unit common");
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2015-04-02 03:30:20 +02:00
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}}} // namespace lib::diff::test
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