How do you set up an automated build for EJB client JAR’s from the IBM Rational Software Delivery 7 development environment for WebSphere 6?

This question came up recently in my work for a major insurance company. When one extends the EJB client class, that is all a developer has to do as far as RAD 7 is concerned. When the developer deploys the JAR to the server, RAD 7 quietly generates stub source Java classes, compiles them and includes them in the JAR file.

An automated build in this context means that all the code the developer created in RAD 7 and checked into version control, is checked out of version control without RAD 7 and built exactly the way the developer intended. This is what OpenMake Meister is for.

One developer I was working with was concerned with how to generate those same source files in the automated build, which in his case was using OpenMake. He was familiar with how OpenMake uses the ejbdeploy command for building EAR’s with EJB server-side code and expected some equivalent for the EJB client.

Mercifully RAD 7 actually leaves the generated source files behind in the Eclipse project, in the standard source location. This means that we get the source code for free and there is really no need to regenerate it. All one has to do is check in the generated source to version control along with the developer coded source and build a normal JAR file in the automated build.

For the developer, this means:

1. Check in all the Java source code in the project. This is easy – better than picking and choosing which Java source to check in as the developer thought he had to do. This would be ultra-high risk for making a mistake and breaking the team’s automated build.
2. Assign the “Default Java JAR” configuration mapping for the EJB client project using the OpenMake Target Generator Eclipse plug-in.

A lesson to learn from this is that not all technologies or technology variants will have an impact to the build process. The developer was considering an idealist approach to reproduce every minute step of RAD 7, but the best solution was something practical and simple. Build management is part art and part dirty science. Having a “generate” step for the EJB client Java classes in the automated build only introduces an additional point of possible failure, and we build-meisters know we don’t need any more of those!

With the Web 2.0 evolution, information flow between people has changed from a ‘push’ paradigm (I send you an email) to a pull paradigm (I follow you on Twitter). How could this possibly relate to code management such as branching, merging and history? Well, Git’s distributed repository model and how one obtains code updates from “friend” repositories is similar to Twitter and how you obtain status updates on the people you choose to follow. Instead of communicating micro-blog entries or status updates, Git is communicating source code branch updates.

Also like how Facebook or Twitter allows you to specify a person’s name in lieu of the communication protocol identifier (email address or web page), Git uses aliases for long repository locations so you have a more direct, natural language and human feel to what you are doing: “git fetch linus” will pull changes from Linus’ repository, which you have only had to define once.

Here is a scenario where Steve and I are working on a part of the Linux file system to provide information useful for build management and dependency tracking, which Meister and other tools can take advantage of. Steve started by cloning the master Linux repository and started working away making changes. Steve asked me to work on another part of this project, so I cloned his repository, allowing me to pick up all his changes. I am now automatically following (Git calls it remote-tracking) Steve’s “master” branch of his repository since I started my repository by cloning his. The “master” branch is a.k.a. the “trunk” code stream. I can pick up his updates periodically with:

Now, I may also want to get updates directly from the master Linux repository, but it has a complicated URL that I won’t remember and only want to look up once. So, as a one-time command I do:

Forever after:

The “fetch” command doesn’t put the master Linux changes directly into my workspace, but off to the side for me to examine first (very nice). If I want, I can accept the changes into my local work tree. To tell me which repositories I am following (which friends), I do:

“origin/master” is my own trunk. I could also get the full repository information associated with the short names, but as long as it works, I don’t want to know what it is. For me, this type of friendly and fluid interaction with repositories is one of the major advantages over CVS and Subversion.

If you are a hard-core open source programmer, you probably use Git for project code change management instead of Subversion (I chose those words carefully). There is a lot of passion from Git advocates and, while it is not a very mature solution, it has a lot of momentum to push it forward. Merely being conceived of and written by Linus Torvalds and being used on a few large open source projects, such as the very Linux kernel itself, is enough to garner wide support.

A great place to learn about Git is Sam Vilain’s Tutorial. He goes into a lot of detail on the benefits and how-to’s of using Git. Some of the highlights include repository space savings of over 90% and local-to-repository sync times dropping from hours in Subversion to minutes with Git. The real power of Git is in the highly distributed repositories and the ease and control of moving and accepting changes between repositories. For an open source project with a large number of developers it seems Git will really shine. Git has fine control over branching, merging and accepting or not accepting project changes according to various criteria.

A popular way to use Git is to have Git pull from a public Subversion or CVS repository with convenient integration with those tools to a local Git repository and work from there. Friends working on the same project can easily pass changes between each other with Git and later commit back to the centralized CVS or Subversion repository. GitHub provides a simple Git repository hosting service. Doing a lot of Java work with JBoss and WebSphere, I am naturally interested in an Eclipse plug-in for Git and indeed one exists. It looks like a newborn infant, but I will check it out.

I also have a Perl open source project that is currently pretty anemic, but I hope to revitalize it soon. I really hate the fact that I’m locked into using Subversion on SourceForge and I never came to like Subversion. I’m eager to explore moving the project to GitHub, even though I’ll probably be the only committer for awhile. Since I’m a hardcore software management person and robust Perl developer, I think Git might be my tool. I’ll let you know.

Here comes buildr: yet another Java build tool. Hopefully I, or one of my other cohorts will check this out in detail soon. But, with my experience working with all manner of build tools, with 100 companies and many more development teams, I can already make a few observations.

First of all, why another build tool for Java? I am occasionally told that Maven or Ant is a perfect tool, but clearly the people behind buildr don’t think so. The choice of JRuby as the vehicle for delivering this tool, I think is probably a good one. JRuby is a scripting language in the same vain as Perl, which is used by Meister.

Doing software builds is an ugly business involving lots of file and operating system interaction. This is not where Java shines, but scripting languages can. As long as operating systems are written in C and not Java, C-like tools will be better and faster at interacting with them. Plain Ruby itself is C-based, and JRuby no doubt inherits C-like operating traits. Calling out to a Java compiler from Perl or JRuby, though it has its own JVM, does not represent a significant overhead compared with the file system operations and the compilation/translation itself.

Both Maven and Ant are relatively difficult to extend compared most other build tools, and I’ll be buildr beats them here. If you have all the Maven plug-ins and Ant tasks you need, then good for you. If not, then you have to start developing in Java and it becomes too much of an investment to sink into a build system. It is much cheaper to extend in JRuby or Perl. My frequently cited example is the XMLBeans compile step in Meister, written in Perl, which is only 40 lines of real code. The Maven plug-in is 60 pages of Java code and no one can tell me really what it is doing (I asked on all the forums). Less code is usually more transparent, which is also good for build audits.

I am a little disappointed to see them try to placate the Maven and Ant users by promising it is a drop-in replacement for Maven and they have all the Ant tasks covered. Both tools have their drawbacks and I don’t want to see another tool with the same deficiencies. They should have the cajones (or coñejos) to apply all their resources to what they think is a better tool (with its own unique benefits and deficiencies). I imagine offering Ant task equivalents is pretty easy because of the ease of coding in JRuby compared with Java.

They also don’t mention who is supposed to use the tool. Is it for individuals, small development teams, the enterprise? Maven falls short because it is only appropriate for development teams and not for stable, controlled, enterprise builds. Ant is not even tool, but a means to create some tools for small teams. I don’t think Meister will fear buildr either.

Well, since buildr is only in incubation status with Apache, I’m not sure how much time I’ll be able to spend on it, but I am curious and I’ll let you know if I find out more.

The first rule for Bash/C/Korn shell scripts in a Perl program environment is to re-write them all in Perl. If your Perl environment has any sophistication, you will have common code, standardized logging (perhaps with Log::Log4perl), testing with Test::More, etc. and your shell scripts just can’t keep pace.

If you share the environment with any non-Perl applications, however, you will still have to deal with the environment profile(s). I also have some legacy shell scripts that we can’t justify converting to Perl unless they have another reason to change. (Don’t change tested code in my house ~~ head bobble + finger wave ~~, nuh-uh!)

There are two ways I know of that you can extend the benefits of your Perl implementation towards your legacy and profile shell scripts. The first is through Bahut’s excellent tip on embedding POD documentation in shell script. This solves my problem of generating HTML documentation from POD in Perl scripts and having upsetting holes where the shell scripts are. I also have some controls for the Perl scripts that run podchecker before committing to version control, which fails if no documentation is found. Now, I can extend this control to the shell scripts.

The second Perl tool you can extend is the testing functionality. I’ve found the functionality in Test::More to be useful for validating that the changes to the shell environment profiles are correct and do not introduce defects. Profiles can be notoriously tricky to change when they get fat and you have variables depending on other variables. Mostly the profiles in my case are used to set environment variables that control the version control and build system, and these can be easily validated in a test script called profiles.t via checks like:

ok( $ENV{CODE_ROOT} eq ‘/opt/code’, “CODE_ROOT set to ‘/opt/code’”);

You then just rattle off tests for all the variables that are set and you have a great way to validate that everything will still work after the profile change. For a legacy script, you may not be able to have a crack at the internals, but you can at least check the return code and maybe some external effect it has somewhere, such as a file timestamp change.

eval { `legacy_script.sh`};

ok( !$?, “legacy_script”); #– $? is zero if script executes successfully

Profile.t and any other test scripts used to test legacy shell code can be bundled with all the other Perl tests via Test::Harness for a single test suite that really tests everything shell and Perl.

As a follow up to my article on automating XML updates, I’d like to report that I did use Excel and Perl’s XML::Twig to successfully generate XML descriptors for my web service consumer, and it was a lot easier than I thought. I’m using XFire 1.2.6 web services stack running under JBoss and using MyEclipse IDE 5.0. I’m happy to say I went from blank spreadsheet and no plan to generated XML files from spreadsheet values in one and half hours. The implementation is of course expandable and reusable. This implementation should work for WebSphere and .NET as well.

I needed to create different configurations for my web application so that the service request went to different endpoints for different environments. The endpoint is at an enterprise service bus (ESB) and there is a different ESB for each environment. I need to have my ‘dev’ instance of the consumer hit the ‘dev’ instance of the ESB, the ‘qa’ instance of my web app hit the ‘qa’ instance of the ESB, etc. We’ve set up Meister to pick up the correct XML file for the target environment for the build of the WAR.

I started by setting up the spreadsheet as follows. I had an unnecessary column for Host indicating JBoss, but I hope to include WebSphere and maybe .NET as well some day. My web app actually connects to two services a.k.a. providers, so there is a column there. And, next is the configuration label for my web app with the name corresponding to the environment it is designed for. So, the first three columns of the spreadsheet look like:

Then I needed a way to indicate the resource that would change. Right now I only have XML files, but I chose to stick with a generic URL for that. Unlike Maven or Ant generators, we start with an XML file that actually works and has been tested - not some hacked up parameterized version that takes additional effort to create. The fourth column of the spreadsheet looks like the following (with repeated entries omitted):

Next, I needed a way to specify a target location to change within the XML file. Now, I know I’m going to use XPath, but I’ll want this to one day work for properties files as well, so I came up with a URL-like thing called a Universal Datum Locator (UDL) which pre-pends the method of locating the datum to change on to a method-specific locator. It could be a property name, an XPath or a Perl regex, for example. In this case it is XPath and then the last column contains the replacement value for the datum indicated by the UDL. XPath is also very intuitive and easier to construct than it may look.

The value for the UDL looks like:

xpath://beans/bean[@factory-bean=’xfireProxyFactory’]/ constructor-arg[@index=’1′]/value

So the fifth column contains the UDL’s, which in my case is always the same XPath expression. The final column of the spreadsheet contains the replacement value of the datum indicated by the UDL:

My nifty Perl script is only about 80 lines of real code and because XML::Twig is nearly the best thing in the world, I pass the entire XPath in as a hash key to modify the source XML file:

my $twig = XML::Twig->new(

pretty_print => ‘indented’,

twig_handlers => {

“$xpath” => sub {

$_->set_text($new_datum);

}

}

);

Here, “$xpath” is directly from the “UDL” column of the spreadsheet with only the ‘xpath://’ stripped off and “$new_datum” is directly from the “Value” column. That’s a pretty useful one line subroutine if you ask me. I had the new XML files each generated into a different folder (dev/,int/, etc). Then, I checked them into version control (CA Harvest) and built each of them with Meister. If you want the full code, let me know and I’ll post it somewhere.

I did find working with the Excel 2003 XML Spreadsheet format a tiny bit awkward. You have to keep track of the column and row indices, but not bad other than that. I see Microsoft Word 2007 allows you to save as an XML document directly, but you apparently have to define bindings. I’ll have to check that out.

When you work with a locking-type version control tool like CA Harvest, your Meister build project will appear in your Eclipse workspace as read-only when you check out an existing workspace. I’ve been using Eclipse for WebSphere development (WebSphere Studio Application Developer) and for JBoss via MyEclipse IDE. If you want to regenerate your Java targets, you first have to check out the Meister build project so that the files are writable.

Since this can lock the targets exclusively and prevent others from updating the target, you may not want to check out the build project, but you may still want to develop freely and update your local targets for Meister to build it. For this situation I recommend creating a separate build project that you may never check in to version control. It will be writable and it allows you great freedom for a maximally agile development environment. The ‘official’ build project may reference all the built archives in the workspace, but having your own local build project can allow you to focus for a unit build. For example, my workspace may contain an EAR project, a WAR project and one or more JAR projects. If I am principally working only on one of the JAR projects, my local build project can reference only that one JAR project.

When it’s time to release your JAR code updates to the system build and test environments, synchronize your workspace and check out the VC build project. Generate your targets, do a local system build and then check everything in. Your team system build will work fine!

I wanted to share a specific benefit I enjoyed while using Meister for Java development. As part of my role to help develop an automated JBoss build and deploy system, I ended up taking on a developer role for a web services security project for both JBoss and WebSphere. While the project involved about 1000 lines of Perl, it also got me writing simple web services and consumers for JBoss and WebSphere and building them using Meister and its Eclipse plug-in.

Believe it or not, I am still using WebSphere Studio Application Developer 5.1. While my specific tale involves that IDE, it is equally applicable to MyEclipse and Rational Application Developer set of Eclipse IDE’s. In my environment, CA Harvest is the version control/SCM tool and Meister is the build tool. After code is checked in from my desktop using the CA Harvest eclipse plug-in, the code is replicated out to a Linux server, where Meister performs the official system build that is sanctioned for deployment to the application server. There is also a Meister Eclipse plug-in that scans the WSAD workspace for build targets and dependencies. Meister stores this information in one XML file per build target and those files are also checked in to CA Harvest right along side the source code.

Working intensely within the WSAD Eclipse environment as the project manager cracked the whip, I worked with a consumer application and updated it according to the changes in the service WSDL and service endpoint URL’s. One thing I learned is that if one of the parameters for the consumer is tweaked, don’t bother tweaking the XML or generated code, just regenerate the whole client. WSAD will even check out the files before if they need to be. So everything looked good on my desktop with the service and consumer deployed to two separate WebSphere servers on ports 9080 and 9081. Now to get it into the enterprise ‘dev’ environment…

Using the ‘Generate Target Definitions’ feature of the Meister plug-in I updated the Meister build target XML definition files and checked in all my code. I then promoted the code in CA Harvest which automatically kicked off a ‘dev’ build in the Linux environment. I got an error back from Meister saying ‘jdmpview.jar’ doesn’t exist.

Since I knew my consumer app and its elementary nature, I knew that jdmpview.jar wasn’t one of my JAR’s and it must be one of WebSphere’s. Given that 200 other Java apps use the same build environment with the same standards, I probably didn’t use some new feature of WebSphere that no one else is using. Therefore, it must a problem on my local desktop with the version of JVM I was using.

Sure enough, the consumer app was using the base_v51 WebSphere runtime instead of the ee_v51. (I did inherit the initial version of the app from someone else!) And, oddly enough, there is an extra JAR in the base that is missing in the more fully featured Enterprise Edition. Meister correctly forced the runtime environment to be EE for the Linux build, overriding the developer selection. I switched the runtime in the Java build path properties, regenerated the Meister target definitions, checked them in and promoted them to a successful ‘dev’ build. Regenerating the target definitions had the effective of switching out the list of JAR files in the library path from the base_v51 set to the ee_v51 set. The whole thing including one bad and one good build took about 4 minutes.

The great benefit for me was the balance between developer and SCM functions. We could have applied more controls at the desktop level, but from my perspective, I prefer an Agile environment with more freedom even if it means occasionally hanging myself with my own rope. In this scenario I let the tools dot the I’s and cross the T’s and it took no more time than say, waiting for Outlook over VPN.

In developing Java applications for multiple server environments (e.g. dev, test and prod) there is a common pain-point of having to manage deployment descriptor or configuration files specific to each server. For example, you may have an XML log4j configuration file with some parameters different for different server environments. You may want to turn on debug messaging for the development server, but turn it off for production. At the same time, the Java source code will (eventually) be the same in production as it was in development. A similar situation applies for .NET application development.

Like many build management tasks, managing these environment-specific files is generally left to either manual or some type of scripting. This is really something that needs to have a high level of automation applied. Particularly in larger environments, much like scripted build management solutions, existing tactics fall short. This situation is in a far worse state than even the compile part of build management. It is not enough to simply have a script that can spit out some files. One of the biggest problems is information management and the fact that parameter values in the configuration files may be determined by different teams! How do a production engineering team and an application developer both feed inputs into the same XML file?

I’ve worked on this problem for several years and with a number of companies. The critical functionality can be broken down into two different items – information management and a processing engine. In an effort come up with something better, I’ve done a review of what’s out there and here is what I came up with:

• Ant ‘filter‘ task: As with many Ant tasks, this works great if you are an individual with a few items that need updating. It is a nightmare if you are working in a multi-team enterprise with multiple server environments. The main problem is that you have to constantly take working copies of XML files and insert a token for Ant to later re-replace. This leads to a management nightmare to synchronize parameterized copies of XML files with their working copies from the desktop environment. The advantage is that it works for any file type so you can use it for properties files as well as XML files.
• OOPS Consultancy Ant ‘xmltask‘: This is a good engine for specifying and performing changes to the XML and has a full feature set. In fact, we use this in some of the Meister build services. The problem is that it is only for Ant and therefore you have all the reuse, standardization and hard coding issues. Xmltask can provide part of the solution we are looking for, but we still have an information management problem to deal with.
• Maven: Maven has what is essentially the Ant filter task. The specifications are abstracted in the pom files, which is better than Ant, but it encourages templating of configuration files leading to all the problems associated with that (synchronizing templates with working files, testing templates, etc.)
• XML:DB XUpdate: This is a working draft of a specification to encode XML update instructions into an XML document. There is a Java implementation of XUpdate listed on the site called ‘Lexus’, but I couldn’t find anything on it. Since the build management task requires us to generate XML files, I’m not keen on generating XML files using xupdate tags that will allow me to generate other XML files.
• Perl XML::Twig: This has worked wonderfully for me on a back-end web services security effort and I could not be more happy with such a precise, elegant and brief XML library, which includes XPath. This is not a solution for Java or .NET developers, but it could serve as an engine to mimic xmltask or implement the XUpdate specification.
• Excel. Yes, I’ve seen Excel used effectively as the information management front-end to updating the XML. It is a convenient format to share among teams, it is centralized source of information, it can be checked into version control and it can be saved as an XML file itself for processing by another engine. In a large environment, you may have 5 or more server environments, lots of different components to configure, so you could have literally hundreds of parameters to manage. Excel gives you a nicely transparent way to view those values.

There are times in build management that you need to encrypt something – often a password. In the last blog, I gave an overview of the encryption process. Now, I’ll show how you can accomplish something.

Besides just having an encryption algorithm, there are a number of important details to be minded: key, block management algorithm, initialization vector, binary-to-text encoding. Here is what I ended up doing. The encrypted text ended up in the text field of an element in XML and it was successfully decrypted on the other end in pure Java.

First, you need your basic cipher We’ll use the Rijndael algorithm specified by AES. I used a 128-bit key generated with help from the Crypt::Random module:

use Crypt::Rijndael;

my $base_cipher = Crypt::Rijndael->new(
 $key,
 Crypt::Rijndael::MODE_CBC( )
);

Next you use this cipher within a block algorithm:

use Crypt::CBC;  #--Cipher block chaining

my $block_cipher = Crypt::CBC->new(
  -cipher => $base_cipher,
  -header => 'none',
  -iv    =>    $iv,
  -padding => 'space'
);

Even though the initialization vector, $iv, does not need to be secret, I enjoyed making it “randomy” with the ultra-cool Data::Random module. Also note that the padding strategy, adding spaces, is not binary safe so it works for encrypting text, but not for binary format files. Now you just encrypt:

my $encrypted_raw_binary =
               $block_cipher->encrypt( $plain_text );

use MIME::Base64;

my $encrypted_text_string  = encode_base64(
  $encrypted_raw_binary,
  ''
);

#-- empty 2nd arg means “don’t break up long lines”

The last step is necessary to give you something you can easily manipulate as a string to read and write into files.

So that’s it. To decrypt you just do the reverse.

In my previous blog I showed how to test that a subroutine fails properly when given incorrect arguments. I generally use a similar method to test command line Perl programs that are run as Meister activities.

use Test::More qw(no_plan); 

my $program = 'actvity_01.pl';
 my $rc = eval {`$program`} ; 

ok( !$?,  "Runs fine or not: $rc");

I take it a bit farther and test calling the script with different inputs to make sure it fails with missing args and succeeds in a situation when it should. When running scripts with Meister and Mojo, it is important to make sure you end with the correct return code so you have the proper handling in the workflow.

I am aware of the Test::Exception and just now really looked at it. It has some attractive functions like ‘throws_ok’. This function tests for a failure and that the error message matches a regex. That is nice and compact. Still, for what I am doing these days, I don’t have standard error messages so it is overkill. I’ll keep it in mind for the future.

A common problem in the UNIX and Linux worlds is managing the shell profile. The profile is used by the shell to set basic parameters such as environment variables that all programs running under the shell would have access to. An example is the OPENMAKE_SERVER environment variable that must have as its value the URL of the KB server servlet.

Profiles tend to grow and may contain a number of different settings important to different programs. It is very common to hear about changes to the profile causing some unintended effect to one or more settings, which in turn causes some program or other to stop working. For example, if I have an OpenMake client installed on a Linux box, and then I want to use a COBOL compiler, I may need to set a number of new variables like COBDIR in my profile. A common practice would be to copy the profile from one of the developers who regularly compiles COBOL, on top of your profile. This way you ensure your compiles work. However, by copying you just overwrote all the setting you had before.

Now, of course, you wouldn’t intentionally lose all your settings you careful defined, but maybe the sys admin copied it for you, trying to be helpful. This very simplistic example is easy to handle, but with lots of settings and lots of programs depending on them, you could easily have a situation where some parameter change is not detected until something crashes in production. So, what do you do? You want to test your changes, but the profiles are usually in some shell script language and are outside the common application lifecycle processes.

It turns out Perl is ideal for this task. All you have to do (for a login-type profile) is to check the environment variables. This effectively gives you both functional and regression tests for your profile.

use Test::More;

ok( exists $ENV{COBDIR}, "COBDIR exists");

ok($ENV{COBDIR} eq '/usr/cob', "COBDIR set correctly");

Because it is so easy to write these tests (vi: yy j p dw <your change>), I like to make them pretty granular. In this example, I will know from the two tests if COBDIR is set incorrectly, or not set at all. That can tell me a lot about what caused the problem. This is an incredibly small investment to ensure your profile changes don’t cause unintended production failures.

One of the things I find annoying when coding is having a subroutine that you’ve called with wrong arguments merrily process away with inappropriate inputs. I try to check for values for all the args at the beginning of the subroutine:

sub my_sub {
  my $partition  = shift;
  confess "$partition not passed"   #-- you 'use Carp;'d
    unless $partition;
}

Something like that depending on the exact requirements. Since my subroutine is nearly always in a Perl module to make it easy to test, I use ‘confess’ instead of ‘die’ to get the full stack trace in case of an error. This lets you know which code called the subroutine incorrectly, rather than simply the line of the die command in your perl module. Duh – not useful.

And now, since I want to make sure the subroutine fails if I call it wrong, I write a test script that goes something like this:

use Test::More;

use_ok('MyModule', "Use is fine");  #- tests that 'use' works

eval { MyModule::my_sub('') };
ok( ! $@, "Failed as it's s'posed to: $@");

There are a couple of important things to note here. When you test for an error, make sure you print the error back out. While it would be nice to then do an ok to check the error message, that is really diminishing returns in terms of how much coding you do for your testing. I find it is enough to print out the error message. This lets you know if your script is failing for an entirely different reason than the one you intended. Usually this is related to simply getting the test script working correctly, and once you get that working, you don’t have to worry about it. If you print the error message, though, you will have to get used to seeing error messages wiz by as your tests are run, but as long as you run all your tests through Test::Harness, you only have to be concerned about seeing the ‘All Tests Successful’ message at the very end.

I do a fair amount of Perl development for our clients and I’ve become a big fan of the Test::More package for testing my Perl code. I’ve found it very useful for unit testing the Perl code itself and also for running integration tests that involve commercial product command line API’s. To get started you have to look at Michael Schwern’s tutorials that also explain the philosophy and best practices of testing. (Michael G. Schwern’s Perl Testing Tutorial)

I found I had to change my coding architecture slightly to pull subroutines in Perl scripts out into Perl modules. This makes sense, of course, because you can load the Perl module from a test script and test the handling of arguments and returns of each subroutine. Schwern says “test the manual” and that would be great if in my environment someone would pay for the documentation. Well, there should always be the POD (plain ol’ documentation), but I actually use the test script as my list of requirements and specs. I simultaneously write and run the test scripts as I write the code for the functionality of interest. When I finish my last line of coding and I run the test script and it completes successfully, I know I’m done with development.

Well, those are the basics. I’ll share some more of experiences writing and running tests in Perl in future blogs.

Note that the Perl rename function is actually an implementation of the UNIX rename C system call. This call does what you would normally associate with the UNIX ‘mv’ command or DOS ‘move’ and ‘rename’ commands. That is you can “rename” a file in Perl to a new directory location, not just rename the file in place. I learned this recently working on a deployment script for JBoss.

I had earlier implemented the archive extraction by using the ‘move’ function from the File::Copy package. This function actually does a very careful recursive copy, preserving permissions, and then deleting the original at the end. This makes it very slow compared to the ‘rename’ function which just alters the directory entry and does not actually copy any data from one section on disk to another.

With this function, a new directory entry is created for the target file that points to the areas on disk of the original file data. After this is complete, the source directory entry is deleted without affecting the data chunks of the original. This is a much faster operation than copying each data area to create a second complete copy.

If you take any respectable source code and you make a copy of it for a new project, you will more than double the number of bugs you started with. The original code, assuming it has some complexity, can have “X” number of bugs that will be exposed sooner or later. Now that you have made a copy of it, you have two pieces of code with the exact same bugs – double the number that you started with, equal to 2X.

Now, you start modifying your new copy of the source code. This new development could lead to a few more bugs equal to “Y”. So you started with X number of bugs, but now you have 2X + Y! (Merging the code back, of course, can get you down to X + Y.)

Just something to think about!