NAME
    Test::Chunks - Chunky Data Driven Testing Framework

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');

  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.

  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.

  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.

  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.

  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
    scalar => scalar

    Decode 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 $current_chunk_object = $self->chunk;
            my $data = shift;
            my $args = shift;
            # 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.

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
    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