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
<h1>The Main Event</h1>


=== List Test
+++ pod
=over
=item * one
=item * two
=back

+++ html
<ul>
<li>one</li>
<li>two</li>
</ul>

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{<html.*};
run_like 'foo', 'match';

run_unlike(data_name, regexp | data_name);

The run_unlike function is similar to run_like, except the opposite.

run_unlike 'foo', qr{<html.*};
run_unlike 'foo', 'no_match';

delimiters($chunk_delimiter, $data_delimiter)

Override the default delimiters of === and ---.

spec_file($file_name)

By default, Test::Chunks reads its input from the DATA section. This function tells it to get the spec from a file instead.

spec_string($test_data)

By default, Test::Chunks reads its input from the DATA section. This function tells it to get the spec from a string that has been prepared somehow.

filters( @filters_list or $filters_hashref )

Specify a list of additional filters to be applied to all chunks. See FILTERS below.

You can also specify a hash ref that maps data section names to an array ref of filters for that data type.

filters {
    xxx => [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:

=== <chunk name 1>
<optional chunk description lines>
--- <data section name 1> <filter-1> <filter-2> <filter-n>
<test data lines>
--- <data section name 2> <filter-1> <filter-2> <filter-n>
<test data lines>
--- <data section name n> <filter-1> <filter-2> <filter-n>
<test data lines>

=== <chunk name 2>
<optional chunk description lines>
--- <data section name 1> <filter-1> <filter-2> <filter-n>
<test data lines>
--- <data section name 2> <filter-1> <filter-2> <filter-n>
<test data lines>
--- <data section name n> <filter-1> <filter-2> <filter-n>
<test data lines>

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 <ingy@cpan.org>

COPYRIGHT

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

To install Test::Chunks, copy and paste the appropriate command in to your terminal.

cpanm

cpanm Test::Chunks

CPAN shell

perl -MCPAN -e shell
install Test::Chunks

For more information on module installation, please visit the detailed CPAN module installation guide.

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NAME

DEPRECATED

SYNOPSIS

DESCRIPTION

EXPORTED FUNCTIONS

chunks( [data-section-name] )

next_chunk()

run(&subroutine)

run_is(data_name1, data_name2)

run_is_deeply(data_name1, data_name2)

run_like(data_name, regexp | data_name);

run_unlike(data_name, regexp | data_name);

delimiters($chunk_delimiter, $data_delimiter)

spec_file($file_name)

spec_string($test_data)

filters( @filters_list or $filters_hashref )

filters_delay( [1 | 0] );

filter_arguments()

tie_output()

default_object()

WWW() XXX() YYY() ZZZ()

TEST SPECIFICATION

FILTERS

Scalar vs List

norm

trim

chomp

unchomp

chop

append

lines

array

join

eval

eval_stdout

eval_stderr

eval_all

regexp[=xism]

get_url

exec_perl_stdout

yaml

dumper

strict

base64_decode

base64_encode

escape

Rolling Your Own Filters

OO

THE Test::Chunks::Chunk CLASS

name()

description()

seq_num()

chunks_object()

run_filters()

is_filtered()

original_values()

SUBCLASSING

OTHER COOL FEATURES

AUTHOR

COPYRIGHT

Module Install Instructions

THE `Test::Chunks::Chunk` CLASS