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 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 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