Perl Testing - by Gavin Sherlock

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Guideline for Writing Tests for Perl Code


To build robust code, you need to incorporate the notion of testing into your general coding practices. This is one of the cornerstones of the Extreme Programming philosophy, which tries to lower the cost of change (i.e. code maintenance). If you don't have a test-suite, then you will be faced with hours of error-prone, manual testing every time you release a new version of your code. Alternatively, you can skip the testing, and release bug-ridden code on a regular basis, full of regressions, or things that never worked. Not soon after, you can enjoy nobody wanting to use your code ever again. Having a test-suite enables you to automate testing, checking that the entire set of functionality that your code purports to have really works. It takes a lot of effort to write a test-suite from scratch, but it saves hours/days/weeks of maintenance down the line. In Perl speak, not having a test suite is false-laziness. If you adopt a philosophy that does not permit engineers to check code into the repository that does not pass the test-suite, and couple this with nightly running of the test-suite against the top of the tree of your repository (i.e. CVS head), you will write much more robust code in the long term, because you will catch regressions before they ship, rather than afterwards. Everytime a new problem surfaces, a new test should be written for it, so that once fixed, that problem should never creep back in.

Unit-tests vs. Integration tests

The goal of unit testing is to isolate each part of the program and show that the individual parts are correct. If you write code to unit test every non-trivial function or method, your code will likely much more robust than it is currently, but it may still have bugs lurking in there, which can only be found with integration testing, which may combine many non-trivial functions together. Having unit tests is good, because it will enable you to later refactor code and test that it still works, and because it will make integration testing easier to design later on.

How to begin a test suite

The easiest way to create and use a test suite is to take advantage of Perl's inbuilt mechanisms and modules. If your code comes with a Makefile, and is installed using the typical:

perl Makefile.PL 
make install

You need to insert a make test step between make and make install. You can use this for your own development purposes, and it also ensures that everyone installing your code will run your test-suite on their machine, and will hopefully send back reports of anything that breaks.

make test will, via your generated Makefile, invoke ExtUtils::MakeMaker (, which will:

  1. Check for the existence of a file named in the current directory and if it exists, execute the script with the proper set of perl -I options.
  2. Also check for any files matching glob("t/*.t"). It will execute all matching files in alphabetical order via the Test::Harness module with the -I switches set correctly.

If you'd like to see the raw output of your tests, set the TEST_VERBOSE variable to true:

make test TEST_VERBOSE=1

Thus, to write a test suite, you should create a t/ directory in at the top level of your distribution, and populate it with .t files, that will test the functionality of your code base.

Writing tests

Test scripts are perl scripts, but with a .t extension. Ideally, you should split up your tests into small discrete chunks, such that each .t script will test a particular part of your code base. You want to design your tests well, as you will be faced with maintaining them, in addition to maintaining your regular code base.

To write tests, you should use either the Test::Simple or the Test::More modules. Typically, you should start with Test::Simple, and when you get the hang of it, start using Test::More (which is entirely compatible with tests written for Test::Simple).

For a good tutorial on testing, see

Test::Simple contains a single function, called ok(). The basic philosophy is that it allows you to determine whether you get the expected result from your code. You have to tell Test::Simple how many tests you will will be performing, and write the tests, then it will take care of the tedious details. For instance, suppose you have a function in one of your modules that should always return a number between 1 and 100. A test script to test that functionality is as simple as:

#!/usr/bin/perl -w
use Test::Simple tests => 2;
use Mylib qw (function1); 
ok (function1() >= 1); 
ok (function1() <= 100);

when you run this, you should get something like:

ok 1
ok 2

If for some reason you introduce a bug, and function1() now starts producing values greater than 100, you might get:

ok 1 
not ok 2

Failed test (test.t at line 9) 
Looks like you failed 1 tests of 2.

It's now pretty easy to track down your regression. At some point, you will hopefully have hundreds of tests, so ok() allows you to provide some useful descriptive text for them too:

#!/usr/bin/perl -w
use Test::Simple tests => 2; 
use Mylib qw (function1); 
ok (function1() >= 1, "function1()'s return value is greater than or equal to 1");
ok (function1() >= 100, "and it's less than or equal to 100");

which will now give:

ok 1 - function1()'s return value is greater than or equal to 1 
ok 2 - and it's less than or equal to 100

which makes it even easier for you to maintain your test-suite.

Testing Documentation

Your code should always be fully documented with pod - that is, if you export a function, or if you have a public method, there should be pod documentation that describes the expected inputs and outputs of those functions/methods. If it's not documented, then it doesn't exist. A useful approach to pod documentation is to test that it exists, and that what does exist is error free: Test::Pod::Coverage ( checks for pod coverage in your distribution, and is trivial to use. Just create a .t file with the following content:

use Test::More; 
eval "use Test::Pod::Coverage 1.00"; 
plan skip_all => "This is not an error, Test::Pod::Coverage 1.00 required for testing POD coverage" if $@;
my @modules = Test::Pod::Coverage::all_modules(); 
plan tests => scalar(@modules); 
for my $module (@modules) { 

This will test all of your modules for pod coverage. To test that the pod documentation is syntactically correct, use Test::Pod (, which again can be easily used, with a .t file containing:

use Test::More; 
eval "use Test::Pod 1.00"; 
plan skip_all => "This is not an error, Test::Pod 1.00 is required for testing POD" if $@; 

This test file will test that all of the pod in any files with a .pm or a .pl extension in the distribution have syntactically correct pod. if the Test::Pod modules are not installed, then the scripts are skipped.

Testing Coverage of your Test-Suite

Ideally, your test suite should full exercise your code base, covering all possible code paths. In practice, this is very difficult to accomplish, but help is at hand:

Devel::Cover ( provides code coverage metrics for Perl, and can be used in conjunction with a test-suite, like:

cover -delete 
HARNESS_PERL_SWITCHES=-MDevel::Cover make test 

You can see the author's coverage analysis of a large number of modules from CPAN at:

In this way, you can design new tests for your code base, designed to cover as yet un-exercised code paths.

Testing Performance

One of the things that you should consider testing for is performance. It may be that your code passes all your unit and integration tests, but in the process of refactoring it and shaking out bugs that your test suite found, you made it three times as slow. It's now perfect, but nobody wants to use it. If you adopt the philosophy that a decrease in performance is a regression, then you can avoid introducing performance problems into production code (beyond those that already existed). If you set up a system where you record performance numbers for your code every time there is a new check-in to your code repository, you can track whether performance regresses beyond just noise in the measurements. To profile your code in Perl, you can use Devel::Profile (, which will allow you to determine where your bottlenecks are.