Why do we do Continuous Integration?

Continuous Integration is now very much a central process of most agile development efforts, but it hasn’t been around all that long. It may be widely regarded as a “development best practice” but some teams are still waiting to adopt C.I. Seriously, they are.

And it’s not just agile teams that can benefit from C.I. The principles behind good C.I. can apply to any development effort.

This article aims to explain where C.I. came from, why it has become so popular, and why you should adopt it on your development project, whether you’re agile or not.

Back in the Day…

Are you sitting comfortably? I want you to close your eyes, relax, and cast your mind back, waaay back, to 2003 or something like that…

You’re in an office somewhere, people are talking about The Matrix way too much, and there’s an alarming amount of corduroy on show… and developers are checking in code to their source control system….

Suddenly a developer swears violently as he checks out the latest code and finds it doesn’t compile. Someone’s check-in has broken the codebase.

He sets about fixing it and checking it back in.

Suddenly another developer swears violently….

Rinse and repeat.

CI started out as a way of minimising code integration headaches. The idea was, “if it’s painful, don’t put it off, do it more often”. It’s much better to do small and frequent code integrations rather than big ugly ones once in a while. Soon tools were invented to help us do these integrations more easily, and to check that our integrations weren’t breaking anything.


Fossilized C.I. System

Fossil of a Primitive C.I. System

Excavations of fossilized C.I. systems from the early 21st Century suggest that these primitive C.I. systems basically just compiled code, and then, when unit tests became more popular, they started running unit tests as well. So every time someone checked in some code, the build would make sure that this integration would still result in a build which would compile, and pass the unit tests. Simple!

C.I. systems then started displaying test results and we started using them to run huuuuge overnight builds which would actually deploy our builds and run integration tests. The C.I. system was the automation centre, it ran all these tasks on a timer, and then provided the feedback – this was usually an email saying what had passed and broken. I think this was an important time in the evolution of C.I. because people started seeing C.I. as more of an information generator, and a communicator, rather than just a techie tool that ran some builds on a regular basis.

Information Generator

Management teams started to get information out of C.I. and so it became an “Enterprise Tool”.

Some processes and “best practices” were identified early on:

  • Builds should never be left in a broken state.
  • You should never check in on a broken build because it makes troubleshooting and fixing even harder.

With this new-found management buy-in, C.I. became a central tenet of modern development practices.

People started having fun with C.I. plugging lava lamps, traffic lights and talking rabbits into the system. These were fun, but they did something very important in the evolution of C.I. –  they turned it into an information radiator and a focal point of development efforts.

Automate Everything!

Automation was the big selling point for C.I. Tasks that would previously have been manual, error-prone and time-consuming could now be done automatically, or at night while we were in bed. For me it meant I didn’t have to come in to work on the weekends and do the builds! Whole suites of acceptance, integration and performance tests could automatically be executed on any given build, on a convenient schedule thanks to our C.I. system. This aspect, as much as any other, helped in the widespread adoption of C.I. because people could put a cost-saving value on it. C.I. could save companies money. I, on the other hand, lost out on my weekend overtime.

Code Quality

Static analysis and code coverage tools appeared all over the place, and were ideally suited to be plugged in to C.I. These days, most code coverage tools are designed specifically to be run via C.I. rather than manually. These tools provided a wealth of feedback to the developers and to the project team as a whole. Suddenly we were able to use our C.I. system to get a real feeling for our project’s quality. The unit test results combined with the static analysis could give us information about the code quality, the integration  and functional test results gave us verification of our design and ensured we were making the right stuff, and the nightly performance tests told us that what we were making was good enough for the real world. All of this information got presented to us, automatically, via our new best friend the Continuous Integration system.

Linking C.I. With Stories

When our C.I. system runs our acceptance tests, we’re actually testing to make sure that what we’ve intended to do, has in fact been done. I like the saying that our acceptance tests validate that we built the right thing, while our unit and functional tests verify that we built the thing right.

Linking the ATs to the stories is very important, because then we can start seeing, via the C.I. system, how many of the stories have been completed and pass their acceptance criteria. At this point, the C.I. system becomes a barometer of how complete our projects are.

So, it’s time for a brief recap of what our C.I. system is providing for us at this point:

1. It helps us identify our integration problems at the earliest opportunity

2. It runs our unit tests automatically, saving us time and verifying or code.

3. It runs static analysis, giving us a feel for the code quality and potential hotspots, so it’s an early warning system!

4. It’s an information radiator – it gives us all this information automatically

5. It runs our ATs, ensuring we’re building the right thing and it becomes a barometer of how complete our project is.

And we’re not done yet! We haven’t even started talking about deployments.


Ok now we’ve started talking about deployments.

C.I. systems have long been used to deploy builds and execute tests. More recently, with the introduction of advanced C.I. tools such as Jenkins (Hudson), Bamboo and TeamCity, we can use the C.I. tool not only to deploy our builds but to manage deployments to multiple environments, including production. It’s now not uncommon to see a Jenkins build pipeline deploying products to all environments. Driving your production deployments via C.I. is the next logical step in the process, which we’re now calling “Continuous Delivery” (or Continuous Deployment if you’re actually deploying every single build which passes all the test stages etc).

Below is a diagram of the stages in a Continuous Delivery system I worked on recently. The build is automatically promoted to the next stage whenever it successfully completes the current stage, right up until the point where it’s available for deployment to production. As you can imagine, this process relies heavily on automation. The tests must be automated, the deployments automated, even the release email and it’s contents are automated.So what exactly is the cost saving with having a C.I. system?

Yeah, that’s a good question, well done me. Not sure I can give you a straight answer to that one though. Obviously one of the biggest factors is the time savings. As I mentioned earlier, back when I was a human C.I. machine I had to work weekends to sort out build issues and get working code ready for Monday morning. Also, C.I. sort of forces you to automate everything else, like the tests and the deployments, as well as the code analysis and all that good stuff. Again we’re talking about massive time savings.

But automating the hell out of everything doesn’t just save us time, it also eliminates human error. Consider the scope for human error in a system where some poor overworked person has to manually build every project, some other poor sap has to manually do all the testing and then someone else has to manually deploy this project to production and confidently say “Right, now that’s done, I’m sure it’ll work perfectly”. Of course, that never happened, because we were all making mistakes along the line, and they invariably came to light when the code was already live. How much time and money did we waste fixing live issues that we’d introduced by just not having the right processes and systems in place. And by systems, of course, I’m talking about Continuous Integration. I can’t put a value on it but I can tell you we wasted LOTS of money. We even had bugfix teams dedicated to fixing issues we’d introduced and not caught earlier (due in part to a lack of C.I.).


While for many companies C.I. is old news, there are still plenty of people yet to get on board. It can be hard for people to see how C.I. can really make that much of a difference, so hopefully this blog will help to highlight some of the benefits and explain how C.I. has been adopted as one of the most important and central tenets of modern software delivery.

For me, and for many others, Continuous Integration is a MUST.


Continuous Delivery using build pipelines with Jenkins and Ant

My idea of a good build system is one which will give me fast, concise, relevant feedback, but I also want it to produce a proper finished article when I’ve checked in my code. I’d like every check-in to result in a potential release candidate. Why? Well, why not?

I used to employ a system where release candidates were produced separately to my check-in builds (also known as “snapshot” builds). This encouraged people to treat snapshot builds as second rate. The main focus was on the release builds. However, if every build is a potential release build, then the focus on each build is increased. Consequently, if every build could be a potential release candidate, then I need to make sure every build goes through the most rigorous testing possible, and I would like to see a comprehensive report on the stability and design of the build before it gets released. I would also like to do all of this automatically, as I am inherently lazy, and have a facebook profile to constantly update!

This presents me with a problem: I want instant feedback on check-in builds, and to have full static analysis performed on them and yet I still want every check-in build to undergo a full suite of testing, be packaged correctly AND be deployed to our test environments. Clearly this will take a lot longer than I’m prepared to wait! The solution to this problem is to break the build process down into smaller sections.

Pipelines to the Rescue!

The concept of build pipelines has been around for a couple of years at least. It’s nothing new, but it’s not yet standard practice, which is a pity because I think it has some wonderful advantages. The concept is simple: the build as a whole is broken down into sections, such as the unit test, acceptance test, packaging, reporting and deployment phases. The pipeline phases can be executed in series or parallel, and if one phase is successful, it automatically moves on to the next phase (hence the relevance of the name “pipeline”). This means I can setup a build system where unit tests, acceptance tests and my static analysis are all run simultaneously at commit stage (if I so wish), but the next stage in the pipeline will not start unless they all pass. This means I don’t have to wait around too long for my acceptance test results or static analysis report.

Continuous Delivery

Continuous delivery has also been around for a while. I remember hearing about it in about 2006 and loving the concept. It seems to be back in the news again since the publication of “Continuous Delivery”, an excellent book from Jez Humble and David Farley. Again the concept is simple, roughly speaking it means that every build gets made available for deployment to production if it passes all the quality gates along the way. Continuous Delivery is sometimes confused with Continuous Deployment. Both follow the same basic principle, the main difference is that with Continuous Deployment it is implied that each and every successful build will be deployed to production, whereas with continuous delivery it is implied that each successful build will be made available for deployment to production. The decision of whether or not to actually deploy the finished article to the production environment is entirely up to you.

Continuous Delivery using Build Pipelines

You can have continuous delivery without using build pipelines, and you can use build pipelines without doing continuous delivery, but the fact is they seem made for each other. Here’s my example framework for a continuous delivery system using build pipelines:

I check some code in to source control – this triggers some unit tests. If these pass it notifies me, and automatically triggers my acceptance tests AND produces my code-coverage and static analysis report at the same time. If the acceptance tests all pass my system will trigger the deployment of my project to an integration environment and then invoke my integration test suite AND a regression test suite. If these pass they will trigger another deployment, this time to UAT and a performance test environment, where performance tests are kicked off. If these all pass, my system will then automatically promote my project to my release repository and send out an alert, including test results and release notes.

So, in a nutshell, my “template” pipeline will consist of the following stages:

  • Unit-tests
  • Acceptance tests
  • Code coverage and static analysis
  • Deployment to integration environment
  • Integration tests
  • Scenario/regression tests
  • Deployments to UAT and Performance test environment
  • More scenario/regression tests
  • Performance tests
  • Alerts, reports and Release Notes sent out
  • Deployment to release repository

Introducing the Tools:

Thankfully, implementing continuous delivery doesn’t require any special tools outside of the usual toolset you’d find in a normal Continuous Integration system. It’s true to say that some tools and applications lend themselves to this system better than others, but I’ll demonstrate that it can be achieved with the most common/popular tools out there.

Who’s this Jenkins person??

Jenkins is an open-source Continuous Integration application, like Hudson, CruiseControl and many others (it’s basically Hudson, or was Hudson, but isn’t Hudson any more. It’s a trifle confusing*, but it’s not important right now!). So, what is Jenkins? Well, as a CI server, it’s basically a glorified scheduler, a cron job if you like, with a swish front end. Ok, so it’s a very swish front end, but my point is that your CI server isn’t usually very complicated, in a nutshell it just executes the build scripts whenever there’s a trigger. There’s a more important aspect than just this though, and that’s the fact that Jenkins has a build pipelines plugin, which was written recently by Centrum Systems. This pipelines plugin gives us exactly what we want, a way of breaking down our builds into smaller loops, and running stages in parallel.


Ant has been possibly the most popular build scripting language for the last few years. It’s been around for a long while, and its success lies in its simplicity. Ant is an XML based scripting language tailored specifically for software build related tasks (specifically Java. Nant is the .Net version of Ant and is almost identical).


Sonar is a quality measurement and reporting tool, which produces metrics on build quality such as unit test coverage (using Cobertura) and static analysis tools (Findbugs, PMD and Checkstyle). I like to use Sonar as it provides a very readable report and contains a great deal of useful information all in one place.

Setting up the Tools

Installing Jenkins is incredibly simple.  There’s a debian package for Operating Systems such as ubuntu, so you can install it using apt-get. For Redhat users there’s an rpm, so you can install via yum.

Alternatively, if you’re already running Tomcat v5 or above, you can simply deploy the jenkins.war to your tomcat container.

Yet another alternative, and probably the simplest way to quickly get up and running with Jenkins is to download the war and execute:

java -jar jenkins.war

This will run jenkins through it’s own Winstone servlet container.

You can also use this method for installing Jenkins on Windows, and then, once it’s up and running, you can go to “manage jenkins” and click on the option to install Jenkins as a Windows Service! There’s also a windows installer, which you can download from the Jenkins website

Ant is also fairly simple to install, however, you’ll need the java jdk installed as a pre-requisite. To install ant itself you just need to download and extract the tar, and then create the environment variable ANT_HOME (point this to the directory you unzipped Ant into). Then add ${ANT_HOME}/bin (or %ANT_HOME%/bin if you’re on Windows) to your PATH, and that’s about it.

Configuring Jenkins

One of the best things about Jenkins is the way it uses plugins, and how simple it is to get them up and running. The “Manage Jenkins” page has a”Manage Plugins” link on it, which takes you a list of all the available plugins for your Jenkins installation:

To install the build pipeline plugin, simply put a tick in the checkbox next to “build pipeline plugin” (it’s 2/3 of the way down on the list) and click “install”. It’s as simple as that.

The Project

The project I’m going to create for the purpose of this example is going to be a very simple java web application. I’m going to have a unit test and an acceptance test stage.  The build system will be written in Ant and it will compile the project and run the tests, and also deploy the build to a tomcat server. Sonar will be used for producing the reports (such as test coverage and static analysis).

The Pipelines

For the sake of simplicity, I’ve only created 6 pipeline sections, these are:

  • Unit test phase
  • Acceptance test phase
  • Deploy to test phase
  • Integration test phase
  • Sonar report phase
  • Deploy to UAT phase

The successful completion of the unit tests will initiate the acceptance tests. Once these complete, 2 pipeline stages are triggered:

  • Deployment to a test server


  • Production of Sonar reports.

Once the deployment to the test server has completed, the integration test pipeline phase will start. If these pass, we’ll deploy our application to our UAT environment.

To create a pipeline in Jenkins we first have to create the build jobs. Each pipeline section represents 1 build job, which in this case runs just 1 ant task each. You have to then tell each build job about the downstream build which is must trigger, using the “build other projects” option:

Obviously I only want each pipeline section to do the single task it’s designed to do, i.e. I want the unit test section to run just the unit tests, and not the whole build. You can easily do this by targeting the exact section(s) of the build file that you want to run. For instance, in my acceptance test stage, I only want to run my acceptance tests. There’s no need to do a clean, or recompile my source code, but I do need to compile my acceptance tests and execute them, so I choose the targets “compile_ATs” and “run_ATs” which I have written in my ant script. The build job configuration page allows me to specify which targets to call:

Once the 6 build jobs are created, we need to make a new view, so that we can start to visualise this as a pipeline:

We now have a new pipeline! The next thing to do is kick it off and see it in action:

Oops! Looks like the deploy to qa has failed. It turns out to be an error in my deploy script. But what this highlights is that the sonar report is still produced in parallel with the deploy step, so we still get our build metrics! This functionality can become very useful if you have a great deal of different tests which could all be run at the same time, for instance performance tests or OS/browser-compatibility tests, which could all be running on different Operating Systems or web browsers simultaneously.

Finally, I’ve got my deploy scripts working so all my stages are looking green! I’ve also edited my pipeline view to display the results of the last 3 pipeline builds:


The pipelines plugin also works for Hudson, as you would expect. However, I’m not aware of such a plugin for Bamboo. Bamboo does support the concept of downstream builds, but that’s really only half the story here. The pipeline “view” in Jenkins is what really brings it all together.

“Go”, the enterprise Continuous Integration effort from ThoughtWorks not only supports pipelines, but it was pretty much designed with them in mind. Suffice to say that it works exceedingly well, in fact, I use it every day at work! On the downside though, it costs money, whereas Jenkins doesn’t.

As far as build tools/scripts/languages are concerned, this system is largely agnostic. It really doesn’t matter whether you use Ant, Nant, Gradle or Maven, they all support the functionality required to get this system up and running (namely the ability to target specific build phases). However, Maven does make hard work of this in a couple of ways – firstly because of the way Maven lifecycles work, you cannot invoke the “deploy” phase in an isolated way, it implicitly calls all the preceding phases, such as the compile and test phases. If your tests are bound to one of these phases, and they take a long time to run, then this can make your deploy seem to take a lot longer than you would expect. In this instance there’s a workaround – you can skip the tests using –DskipTests, but this doesn’t work for all the other phases which are implicitly called. Another drawback with maven is the way it uses snapshot and release builds. Ultimately we want to create a release build, but at the point of check-in we want a release build. This suggests that at some point in the pipeline we’re going to have to recompile in “release mode”, which in my book is a bad thing, because it means we have to run ALL of the tests again. The only solution I have thought of so far is to make every build a release build and simply not use snapshots.

* A footnote about the Hudson/Jenkins “thing”: It’s a little confusing because there’s still Hudson, which is owned by Oracle. The whole thing came about when there was a dispute between Oracle, the “owners” of Hudson, and Kohsuke Kawaguchi along with most of the rest of the Hudson community. The story goes that Kawaguchi moved the codebase to GitHub and Oracle didn’t like that idea, and so the split started.

Fixing java heap issue with maven sites

I’ve suddenly started getting a few java heap (OutOfMemory) errors with my maven builds, mainly when I run the mvn site phase, but also sometimes when I run sonar:sonar.

I’m running the builds on both linux (centos) and windows.

To fix the issue on Windows:

Edit mvn.bat (this lives in your maven bin directory) and add

set MAVEN_OPTS=-Xmx512m

In theory you could add an environment variable called MAVEN_OPTS and give it the same value as above (Xmx512m) but this didn’t actually work very well for me.

To fix on linux:

Edit your mvn file (which for me was in /usr/local/maven/bin/) and add:

export MAVEN_OPTS=”-Xms256m -Xmx512m”

You could of course add this to your bash profile (don’t forget to source it afterward) or add it to etc/profile, but I found adding it to the mvn file to work best.

To fix on Continuous Integration Servers:

I’ve been getting this error on a number of our CI servers as well, so rather than go around adding “export MAVEN_OPTS” all over the place, I am passing it via the CI system. Hudson, Jenkins, Bamboo and Go all have simple UIs for adding extra parameters to your build commands.

Build Pipelines Using Hudson/Jenkins or Bamboo

I’m currently working with the Go CI system from Thoughtworks. One of the things I really like about it is the way that you can have a build “pipeline”. What this means is that you can have a build job which is broken down into, let’s say, 3 different steps:

  1. Checkout and build code, and run unit tests
  2. Package and deploy to a test server
  3. Run acceptance test suite

The way they’ve set this up is pretty decent, it means that every check-in build can be setup to do all of those steps. But you don’t want to wait an hour just to get some feedback on your commit, so Go handles this by giving feedback at the end of each step, rather than having to wait to the end of the whole job.

In the past, when I’ve used Bamboo or Hudson, I’ve setup separate jobs for the check-in builds (which mainly just run unit tests and some light static analysis) and the nightly builds (which do the same, but also include much more static analysis as well as run automated functional tests).

However, it is possible to mimic the Go behaviour using Hudson/Jenkins or Bamboo.  These all support the practice of dependent builds. Hudson jobs have a “Build after other projects are built” option for example. So, to recreate what Go does, you can create separate jobs/plans which run in series. For example:

Job 1: Checks out code, compiles and runs unit tests.

Job 2: Runs coverage report and other static analysis tools such as CPD, PMD and FindBugs

Job 3: Packages up distributable, deploys it and initiates automated tests (e.g. using Selenium)

The developers will get their compile & unit test feedback as quickly as usual, but the process of running a much more exhaustive system will have been spawned as well. No need to wait for the results of the nightly builds to get the full build reports 🙂

Principles of Continuous Integration

I’m a big fan of CI, and as a simple best practice/process for development teams I think it’s right up there as one of the most important to get right. Most software places now have a Continuous Integration system, but do they actually practice Continuous Integration? In my experience the answer isn’t always “yes”.

For me, practicing CI goes a lot further than simply having a CI system setup, and running check-in/nightly builds and running unit test/code inspections etc and so on.

So what is my definition of “Practicing Continuous Integration”? Well, for me it is simply:

  • Having a CI system (obviously)
  • Adopting the principles of Continuous Integration

I did a quick google to see if I could find a simple list of some good CI principles – I know I’ve read many of them before in numerous good books such as Paul M Duvall’s “Continuous Integration” and Jez Humble’s “Continuous Delivery”, but I couldn’t find an easy cut-out-and-use version on the internet, so I’ve decided to knock something up here 🙂

Everyone loves a good list so here’s a list of what I believe to be some principles of CI:

  1. Fix your build failures, immediately. Never leave a build broken. If you do, the build team should be within their right to roll back your last commit.
  2. Every check-in should be an improvement on the last. Each check-in should have value, otherwise why do it? An improvement could be defined as a new piece of functionality, a bug fix, an increase in code coverage etc. If the improvement cannot be easily measured, ensure you detail it in your commit log.
  3. Never check-in on a broken build (unless you’re fixing the breakage)! If you have multiple commits to a codebase after the original breaking commit, then this leaves a bit of a bad smell in your CI system. Firstly, unless your commits are coming thick and fast, it means you aren’t reacting to your build failures, and this is not acceptable – it basically means you’re not playing ball and your letting the rest of the team down. Secondly, the cause of the original breakage can potentially be a lot harder to identify if several other check-ins have happened since the original check-in which broke the build!
  4. Code should be built and tested before checking in to source control. You can either do this by compiling the build locally yourself and checking that the tests pass, or you could invest in a CI/VC system that can perform pre-commit builds, such as Pulse, TeamCity and ElectricCommander (list obtained from Jez Humble’s Continuous Delivery book). However, you can use pre-commit hooks in svn to achieve something similar, and I’m led to believe this can also be done in Git and buildbot.
  5. Everyone should be interested in the output of the CI system. And that includes project managers and testers. Working on the principle that at some point a build works, and every commit thereafter is an improvement, we should be proud to display the results of every build to the whole project team, and we should react quickly if, for any reason, our last check-in was not an improvement to the state of our software.
  6. Functional automated tests should be run as part of the CI process. If you have a suite of automated regression tests, the chances are it’s no great effort to hook them up to the CI system, and definitely worth it in the long run.
  7. Make the builds fit for purpose. I absolutely hate having to wait ages for a simple check-in build to compile, when all I really want to know is whether I have broken my unit tests or violated some important rule. Make the check-in builds quite lightweight, run your unit tests and code coverage tests only, perhaps. Do you really need to run all your code inspections and analysis tools as well as run automated functional tests every time there’s a check-in? Chances are you don’t, so keep the check-in builds light, and leave the heavy duty work for the nightly builds, while we’re all asleep!
  8. React to the feedback. The CI system is a tool to help drive improvement and quality as much as anything else, so we should never ignore it. However, it’s easy to ignore your CI system if the feedback isn’t relevant. The solution is to work closely with the build team, as well as the rest of the project members, to ensure the build feedback is targeted and relevant

That’s about it for my list so far, I’m sure I’ll add to it as time goes by. Please feel free to add your suggestions!

Best Practices for Build and Release Management Part 2

Ok, as promised in Part 1, I’ll go into a bit more detail about each of the areas outlined previously, starting with…

The Build Process

This area, perhaps more than any other area I’ll be covering in this section, has benefited most from the introduction of some ultra handy tools. Back in the day, building/compiling software was fairly manual, and could only be automated to a certain degree, make files and batch systems were about as good as it got, and even that relied on a LOT of planning and could quite often be a nightmare to manage.

These days though, the build phase is exceedingly well catered for and is now a very simple process, and what’s more, we can now get an awful lot more value out of this single area.

As I mentioned before, one of the aims of release management is to make software builds simple, quick and reliable. Tools such as Ant, Nant (.Net version of Ant), Maven, Rake and MSBuild help us on our path towards our goal in many ways. Ant, MSBuild and Nant are very simple XML based scripting languages which offer a wide ranging level of control – for instance, you can build entire solutions with a single line of script, or you can individually compile each project and specify each dependency – it’s up to you to decide what level of control you need. I believe that build scripts should be kept simple and easy to manage, so when dealing with NAnt and MSBuild for .Net solutions I like to build each project by calling an .proj file rather than specifically compiling each library. The .proj files should be constructed correctly and stored in source control. Each build should get the latest proj file  (and the rest of the code, including shared libraries – more on that later) and compile the project.

For Java projects. Ant and Maven are the most popular tools. Ant, like Nant, gives the user a great deal of control, while Maven has less inherent flexibility and enforces users to adhere to its processes. However, both are equally good at helping us make our build simple, quick and reliable. Maven uses POM files to control how projects are built. Within these POM files a build engineer will define all the goals needed to compile the project. This might sound a little tedious but the situation is made easier by the fact that POM files can inherit from master/parent POM files, reducing the amount of repetition and keeping your project build files smaller, cleaner and easier to manage. I would always recommend storing as much as possible in parent POM files, and as little as you can get away with in the project POMs.

One of the great improvements in software building in recent years has been the introduction of Continuous Integration. The most popular CI tools around are CruiseControl, CruiseControl.Net, Hudson and Bamboo. In their simplest forms, CI tools are basically just schedulers, and they essentially just kick off your build tools. However, that’s just the tip of the iceberg, because these tools can do much, MUCH more than that – I’ll explain more later, but for now I’ll just say that they allow us to do our builds automatically, without the need for any human intervention. CI tools make it very easy for us to setup listeners to poll our source code repositories for any changes, and then automatically kick off a build, and then send us an email to let us know how the build went. It’s very simple stuff indeed.

So let’s take a look at what we’ve done with our build process so far:

  • We’ve moved away from manually building projects and started using simple build scripts, making the build process less onerous and not so open to human error. Reliability is on the up!
  • We’ve made our build scripts as simple as possible – no more 1000 line batch files for us! Our troubleshooting time has been significantly reduced.
  • We’ve moved away from using development UIs to make our builds – our builds are now more streamlined and faster.
  • We’ve introduced a Continuous Integration system to trigger our builds whenever a piece of code is committed – our builds are now automated.

So in summary, we’ve implemented some really simple steps and already our first goal is achieved – we’ve now got simple, quick and reliable builds. Time for a cup of tea!

Best Practices for Build and Release Management Part 1

Firstly, Release Management has been around for long enough for it to no longer mean what it used to mean. Release Management used to be concentrated on the discipline of “creating a release of software”, that generally involved the following key points:

  • How to create or build a reliable “release”
  • How to get that reliable release out into the wild

The sorts of issues that these key points in turn raised were things like:

  • How to reliably and repeatably “build” (compile) software
  • How to make software builds quicker
  • How to make software builds easier
  • How to package software builds (zips, .msi etc)

We used to spend our time working with make files, batch files and countless checklists, running manual builds, and then we’d painstakingly create installers or configure zip files to deploy our releases. And when things went wrong, they usually went seriously wrong, and repeating the build and release process could take days.

Since those bad old days, Release Management has come a long way. Lots of the old issues have been addressed by some exceedingly neat tools which have placed emphasis on automation and quality (I’m thinking Ant/Nant, Cruise Control, the Continuous Integration process, Hudson and loads more). But one other major thing has happened in the world of Release Management, and that’s ITIL.

ITIL has redefined the practice of Release Management as more of a planning and coordinating role, it even goes so far as to say Release Management involves communicating with customers and managing customer expectation. This is a million miles away from writing complex batch files, hundreds of lines long, to compile and deploy software to a QA environment! In an ITIL world, the issues listed earlier either don’t exist, or have been addressed already and are no longer a concern to a Release Manager.

So why does the ITIL version of Release Management differ so much from the real world job of a Release Manager?

Well, I would guess that the “build management” aspect is simply not considered part of release management, and that it should be covered somewhere else, but that’s just my guess, I’m seeking some advice from ITIL about that right now.

What we’re left with now is a world where “Release Management” means one thing to one person, and something completely different to another. I’m from the old school of Release Management, I like to actually produce stuff. In a second I’ll outline what I consider to be the main roles and objectives of Release Management, and then later I’ll take each one and explain some ways that I’ve used for tackling them.

So, I like to think of Release Management as a practice which:

  • Helps make software builds simple, quick and reliable. This is achieved by employing the best tools for the job. This means understanding all the various build tools, seeing how they integrate with the systems that already exist in the workplace, and making an informed choice. There’s no way you’re going to make software builds easier, more reliable and repeatable by implementing a manual solution, so get to grips with the various build tools out there and make them work for you.
  • Helps make software deployments simple, quick, reliable and repeatable. Again, this is a bit like the above, but there are fewer tools to choose from. Manually deploying releases is painful and risky, and it also belongs in the dark ages and should be outlawed. There are still plenty of options and combinations of tools to make this task fully automated.
  • Helps take care of configuration management. When I say configuration management, I’m talking about all those issues with how to make a software release look, feel and behave the same from one environment to the next. For me this falls into Release Management because Release Management, unlike development, QA or Operations, has a direct involvement in every environment along the way to releasing into the wild. It’s pointless asking the development team to tackle the issues of configurations between environments when they have very little or no visibility of the production environment, and besides, their time would be much better spent making that button look cooler because that’s what the business has asked for!
  • Helps drive software quality. Thanks to the Continuous Integration process, and the tools that have been built around it, it’s now possible for us to build software every single time a piece of code is checked in, run a suite of unit tests, analyse the code for lazy programming and report on the amount of test coverage a project has. And that’s just the start. There are tools out there for doing much much more than this, and I’ll go into more detail about this later.
  • Helps optimise development and QA time. By giving the dev team the feedback on the quality of their code and telling them where they’re going right and going wrong, we’re helping them target their efforts. Furthermore, if were busy providing these solutions for them, doing the builds, configurations and releases, the developers can get busy doing the stuff they’re skilled at doing. For the QA team, we’re finding bugs and failing releases before the releases even get to them! (of course, if we find too many bugs and fail a release, that release won’t even get o QA)
  • Speeds up time to market. Ok, so we’ve made builds quicker, easier and more reliable, we’ve sped up the process of fine tuning code quality, we’ve spotted bugs before a round of QA has even begun and we’ve made the process of releasing our software out into the wild quicker and simpler. Basically we’ve saved a heap of time in dev, QA and Operations and so our new, higher quality software, can be released efficiently into the wild. Happy days!

As promised earlier, I’ll spend a while giving a few examples of how to actually implement what I’ve broadly outlined above. I’ll try to be generic where I can, but I’ll include specifics for some examples. All that and more in Part 2!