NAME
Test::Chunks - A Data Driven Testing Framework
DEPRECATED
NOTE - This module has been deprecated and replaced by Test::Base. This
is basically just a renaming of the module. Test::Chunks was not the
best name for this module. Please discontinue using Test::Chunks and
switch to Test::Base.
Helpful Hint: change all occurences of "chunk" to "block" in your test
code, and everything should work exactly the same.
SYNOPSIS
use Test::Chunks;
use Pod::Simple;
delimiters qw(=== +++);
plan tests => 1 * chunks;
for my $chunk (chunks) {
# Note that this code is conceptual only. Pod::Simple is not so
# simple as to provide a simple pod_to_html function.
is(
Pod::Simple::pod_to_html($chunk->pod),
$chunk->text,
$chunk->name,
);
}
__END__
=== Header 1 Test
This is an optional description
of this particular test.
+++ pod
=head1 The Main Event
+++ html
The Main Event
=== List Test
+++ pod
=over
=item * one
=item * two
=back
+++ html
DESCRIPTION
There are many testing situations where you have a set of inputs and a
set of expected outputs and you want to make sure your process turns
each input chunk into the corresponding output chunk. Test::Chunks
allows you do this with a minimal amount of code.
EXPORTED FUNCTIONS
Test::Chunks extends Test::More and exports all of its functions. So you
can basically write your tests the same as Test::More. Test::Chunks
exports a few more functions though:
chunks( [data-section-name] )
The most important function is "chunks". In list context it returns a
list of "Test::Chunks::Chunk" objects that are generated from the test
specification in the "DATA" section of your test file. In scalar context
it returns the number of objects. This is useful to calculate your
Test::More plan.
Each Test::Chunks::Chunk object has methods that correspond to the names
of that object's data sections. There is also a "name" and a
"description" method for accessing those parts of the chunk if they were
specified.
"chunks" can take an optional single argument, that indicates to only
return the chunks that contain a particular named data section.
Otherwise "chunks" returns all chunks.
my @all_of_my_chunks = chunks;
my @just_the_foo_chunks = chunks('foo');
next_chunk()
You can use the next_chunk function to iterate over all the chunks.
while (my $chunk = next_chunk) {
...
}
It returns undef after all chunks have been iterated over. It can then
be called again to reiterate.
run(&subroutine)
There are many ways to write your tests. You can reference each chunk
individually or you can loop over all the chunks and perform a common
operation. The "run" function does the looping for you, so all you need
to do is pass it a code block to execute for each chunk.
The "run" function takes a subroutine as an argument, and calls the sub
one time for each chunk in the specification. It passes the current
chunk object to the subroutine.
run {
my $chunk = shift;
is(process($chunk->foo), $chunk->bar, $chunk->name);
};
run_is(data_name1, data_name2)
Many times you simply want to see if two data sections are equivalent in
every chunk, probably after having been run through one or more filters.
With the "run_is" function, you can just pass the names of any two data
sections that exist in every chunk, and it will loop over every chunk
comparing the two sections.
run_is 'foo', 'bar';
NOTE: Test::Chunks will silently ignore any chunks that don't contain
both sections.
run_is_deeply(data_name1, data_name2)
Like "run_is" but uses "is_deeply" for complex data structure
comparison.
run_like(data_name, regexp | data_name);
The "run_like" function is similar to "run_is" except the second
argument is a regular expression. The regexp can either be a "qr{}"
object or a data section that has been filtered into a regular
expression.
run_like 'foo', qr{ [qw(chomp lines)],
yyy => ['yaml'],
zzz => 'eval',
};
If a filters list has only one element, the array ref is optional.
filters_delay( [1 | 0] );
By default Test::Chunks::Chunk objects are have all their filters run
ahead of time. There are testing situations in which it is advantageous
to delay the filtering. Calling this function with no arguments or a
true value, causes the filtering to be delayed.
use Test::Chunks;
filters_delay;
plan tests => 1 * chunks;
for my $chunk (@chunks) {
...
$chunk->run_filters;
ok($chunk->is_filtered);
...
}
In the code above, the filters are called manually, using the
"run_filters" method of Test::Chunks::Chunk. In functions like "run_is",
where the tests are run automatically, filtering is delayed until right
before the test.
filter_arguments()
Return the arguments after the equals sign on a filter.
sub my_filter {
my $args = filter_arguments;
# is($args, 'whazzup');
...
}
__DATA__
=== A test
--- data my_filter=whazzup
tie_output()
You can capture STDOUT and STDERR for operations with this function:
my $out = '';
tie_output(*STDOUT, $buffer);
print "Hey!\n";
print "Che!\n";
untie *STDOUT;
is($out, "Hey!\nChe!\n");
default_object()
Returns the default Test::Chunks object. This is useful if you feel the
need to do an OO operation in otherwise functional test code. See OO
below.
WWW() XXX() YYY() ZZZ()
These debugging functions are exported from the Spiffy.pm module. See
Spiffy for more info.
TEST SPECIFICATION
Test::Chunks allows you to specify your test data in an external file,
the DATA section of your program or from a scalar variable containing
all the text input.
A *test specification* is a series of text lines. Each test (or chunk)
is separated by a line containing the chunk delimiter and an optional
test "name". Each chunk is further subdivided into named sections with a
line containing the data delimiter and the data section name. A
"description" of the test can go on lines after the chunk delimiter but
before the first data section.
Here is the basic layout of a specification:
===
---
---
---
===
---
---
---
Here is a code example:
use Test::Chunks;
delimiters qw(### :::);
# test code here
__END__
### Test One
We want to see if foo and bar
are really the same...
::: foo
a foo line
another foo line
::: bar
a bar line
another bar line
### Test Two
::: foo
some foo line
some other foo line
::: bar
some bar line
some other bar line
::: baz
some baz line
some other baz line
This example specifies two chunks. They both have foo and bar data
sections. The second chunk has a baz component. The chunk delimiter is
"###" and the data delimiter is ":::".
The default chunk delimiter is "===" and the default data delimiter is
"---".
There are some special data section names used for control purposes:
--- SKIP
--- ONLY
--- LAST
A chunk with a SKIP section causes that test to be ignored. This is
useful to disable a test temporarily.
A chunk with an ONLY section causes only that chunk to be used. This is
useful when you are concentrating on getting a single test to pass. If
there is more than one chunk with ONLY, the first one will be chosen.
A chunk with a LAST section makes that chunk the last one in the
specification. All following chunks will be ignored.
FILTERS
The real power in writing tests with Test::Chunks comes from its
filtering capabilities. Test::Chunks comes with an ever growing set of
useful generic filters than you can sequence and apply to various test
chunks. That means you can specify the chunk serialization in the most
readable format you can find, and let the filters translate it into what
you really need for a test. It is easy to write your own filters as
well.
Test::Chunks allows you to specify a list of filters. The default
filters are "norm" and "trim". These filters will be applied (in order)
to the data after it has been parsed from the specification and before
it is set into its Test::Chunks::Chunk object.
You can add to the the default filter list with the "filters" function.
You can specify additional filters to a specific chunk by listing them
after the section name on a data section delimiter line.
Example:
use Test::Chunks;
filters qw(foo bar);
filters { perl => 'strict' };
sub upper { uc(shift) }
__END__
=== Test one
--- foo trim chomp upper
...
--- bar -norm
...
--- perl eval dumper
my @foo = map {
- $_;
} 1..10;
\ @foo;
Putting a "-" before a filter on a delimiter line, disables that filter.
Scalar vs List
Each filter can take either a scalar or a list as input, and will return
either a scalar or a list. Since filters are chained together, it is
important to learn which filters expect which kind of input and return
which kind of output.
For example, consider the following filter list:
norm trim lines chomp array dumper eval
The data always starts out as a single scalar string. "norm" takes a
scalar and returns a scalar. "trim" takes a list and returns a list, but
a scalar is a valid list. "lines" takes a scalar and returns a list.
"chomp" takes a list and returns a list. "array" takes a list and
returns a scalar (an anonymous array reference containing the list
elements). "dumper" takes a list and returns a scalar. "eval" takes a
scalar and creates a list.
A list of exactly one element works fine as input to a filter requiring
a scalar, but any other list will cause an exception. A scalar in list
context is considered a list of one element.
Data accessor methods for chunks will return a list of values when used
in list context, and the first element of the list in scalar context.
This usually does the right thing, but be aware.
norm
scalar => scalar
Normalize the data. Change non-Unix line endings to Unix line endings.
trim
list => list
Remove extra blank lines from the beginning and end of the data. This
allows you to visually separate your test data with blank lines.
chomp
list => list
Remove the final newline from each string value in a list.
unchomp
list => list
Add a newline to each string value in a list.
chop
list => list
Remove the final char from each string value in a list.
append
list => list
Append a string to each element of a list.
--- numbers lines chomp append=-#\n join
one
two
three
lines
scalar => list
Break the data into an anonymous array of lines. Each line (except
possibly the last one if the "chomp" filter came first) will have a
newline at the end.
array
list => scalar
Turn a list of values into an anonymous array reference.
join
list => scalar
Join a list of strings into a scalar.
eval
scalar => list
Run Perl's "eval" command against the data and use the returned value as
the data.
eval_stdout
scalar => scalar
Run Perl's "eval" command against the data and return the captured
STDOUT.
eval_stderr
scalar => scalar
Run Perl's "eval" command against the data and return the captured
STDERR.
eval_all
scalar => list
Run Perl's "eval" command against the data and return a list of 4
values:
1) The return value
2) The error in $@
3) Captured STDOUT
4) Captured STDERR
regexp[=xism]
scalar => scalar
The "regexp" filter will turn your data section into a regular
expression object. You can pass in extra flags after an equals sign.
If the text contains more than one line and no flags are specified, then
the 'xism' flags are assumed.
get_url
scalar => scalar
The text is chomped and considered to be a url. Then LWP::Simple::get is
used to fetch the contents of the url.
exec_perl_stdout
list => scalar
Input Perl code is written to a temp file and run. STDOUT is captured
and returned.
yaml
scalar => list
Apply the YAML::Load function to the data chunk and use the resultant
structure. Requires YAML.pm.
dumper
scalar => list
Take a data structure (presumably from another filter like eval) and use
Data::Dumper to dump it in a canonical fashion.
strict
scalar => scalar
Prepend the string:
use strict;
use warnings;
to the chunk's text.
base64_decode
scalar => scalar
Decode base64 data. Useful for binary tests.
base64_encode
scalar => scalar
Encode base64 data. Useful for binary tests.
escape
scalar => scalar
Unescape all backslash escaped chars.
Rolling Your Own Filters
Creating filter extensions is very simple. You can either write a
*function* in the "main" namespace, or a *method* in the
"Test::Chunks::Filter" namespace. In either case the text and any extra
arguments are passed in and you return whatever you want the new value
to be.
Here is a self explanatory example:
use Test::Chunks;
filters 'foo', 'bar=xyz';
sub foo {
transform(shift);
}
sub Test::Chunks::Filter::bar {
my $self = shift;
my $data = shift;
my $args = $self->arguments;
my $current_chunk_object = $self->chunk;
# transform $data in a barish manner
return $data;
}
If you use the method interface for a filter, you can access the chunk
internals by calling the "chunk" method on the filter object.
Normally you'll probably just use the functional interface, although all
the builtin filters are methods.
OO
Test::Chunks has a nice functional interface for simple usage. Under the
hood everything is object oriented. A default Test::Chunks object is
created and all the functions are really just method calls on it.
This means if you need to get fancy, you can use all the object oriented
stuff too. Just create new Test::Chunks objects and use the functions as
methods.
use Test::Chunks;
my $chunks1 = Test::Chunks->new;
my $chunks2 = Test::Chunks->new;
$chunks1->delimiters(qw(!!! @@@))->spec_file('test1.txt');
$chunks2->delimiters(qw(### $$$))->spec_string($test_data);
plan tests => $chunks1->chunks + $chunks2->chunks;
# ... etc
THE "Test::Chunks::Chunk" CLASS
In Test::Chunks, chunks are exposed as Test::Chunks::Chunk objects. This
section lists the methods that can be called on a Test::Chunks::Chunk
object. Of course, each data section name is also available as a method.
name()
This is the optional short description of a chunk, that is specified on
the chunk separator line.
description()
This is an optional long description of the chunk. It is the text taken
from between the chunk separator and the first data section.
seq_num()
Returns a sequence number for this chunk. Sequence numbers begin with 1.
chunks_object()
Returns the Test::Chunks object that owns this chunk.
run_filters()
Run the filters on the data sections of the chunks. You don't need to
use this method unless you also used the "filters_delay" function.
is_filtered()
Returns true if filters have already been run for this chunk.
original_values()
Returns a hash of the original, unfiltered values of each data section.
SUBCLASSING
One of the nicest things about Test::Chunks is that it is easy to
subclass. This is very important, because in your personal project, you
will likely want to extend Test::Chunks with your own filters and other
reusable pieces of your test framework.
Here is an example of a subclass:
package MyTestStuff;
use Test::Chunks -Base;
our @EXPORT = qw(some_func);
# const chunk_class => 'MyTestStuff::Chunk';
# const filter_class => 'MyTestStuff::Filter';
sub some_func {
(my ($self), @_) = find_my_self(@_);
...
}
package MyTestStuff::Chunk;
use base 'Test::Chunks::Chunk';
sub desc {
$self->description(@_);
}
package MyTestStuff::Filter;
use base 'Test::Chunks::Filter';
sub upper {
$self->assert_scalar(@_);
uc(shift);
}
Note that you don't have to re-Export all the functions from
Test::Chunks. That happens automatically, due to the powers of Spiffy.
You can set the "chunk_class" and "filter_class" to anything but they
will nicely default as above.
The first line in "some_func" allows it to be called as either a
function or a method in the test code.
OTHER COOL FEATURES
Test::Chunks automatically adds
use strict;
use warnings;
to all of your test scripts. A Spiffy feature indeed.
AUTHOR
Brian Ingerson
COPYRIGHT
Copyright (c) 2005. Brian Ingerson. All rights reserved.
This program is free software; you can redistribute it and/or modify it
under the same terms as Perl itself.
See http://www.perl.com/perl/misc/Artistic.html