Category: Uncategorized

Grails upgrade – lessons learned

Grails is a great framework for rapidly developing web applications on top the proven java/servlet platform. Especially smaller, short-lived projects can be a real breeze with all the scaffolding, GORM and convention-over-configuration built into grails.

We happen to use it for a quite complex web application project for almost 3 years now. Half a year ago we upgrade from grails 1.0.x to 1.3.4. That makes 3 major versions in one upgrade step and produced obviously a lot of work and many small bugs. I do not want to put the blame on the grails guys here, because most of the stuff was mentioned in the release notes and it was a big step we decided to take when the decision came to continue the project for several years to come.

Our upgrade policy changed due to that experience and we try to stay a lot more current to be able to adapt our software to framework changes more incrementally. Some weeks ago we upgraded from 1.3.4 to 1.3.7 and this experience was not pleasant. Even though we skimmed through the changelogs and release notes and thought the update should be uncritical for us grails behaviour changed in two aspects which broke things for us:

  1. An API-change where GroovyPagesException was changed to GrailsTagException
  2. Behavioural change where no application context and injections are available in functional tests anymore

Item 1 was easy to fix but you need really good testing to spot it before it slips into production. Such subtle API changes should not happen in micro-version updates as that can easily break parts of the system whithout you knowing because of groovy/grails’ dynamic nature. No compiler saves you here.

Item 2 produced some amount of work for us because we build a quite extensive acceptance test suite using services and domain objects to setup the initial environment for each test. Luckily, there is the grails remote control plugin which you can use for things like that.

Lessons learned

  • You should have extensive automated test suites when developing a grails application over a longer period because things can break in unexpected ways without code changes on your side.
  • Try to plan upgrades some time ahead of releases and dedicate time to scanning the release notes and actually performing the upgrade. It may take you significantly longer than the smooth upgrade procedure itself suggests.

The grails team seems to be increasingly aware of backwards compatibility but they still have some way to go. We hope and expect to see fewer unexpected breakages to occur in the future.

Test Framework Classpath Forgery

A lesson learnt when using HttpUnit with all its dependencies. Xerces changed the system behaviour, but with the test classpath only.

Recently, I had an interesting problem using a testing framework with third-party dependencies. When writing integration tests with JUnit against a very small embedded web application (think of the web based management console for your printer as an example), I chose to use HttpUnit as an auxiliary framework to reduce and clarify the test code.

HttpUnit for testing web applications

If you need to test a classic request/response web application, HttpUnit serves its purpose very well. You can write test code concise and to the point. Downloading and integrating HttpUnit is straight-forward, you can immediately get it to work. Here is an example of a test that asserts that there is at least one link on the web application’s main page:

WebConversation web = new WebConversation();
WebResponse response = web.getResponse(fromServer(port));
WebLink[] allLinks = response.getLinks();
assertTrue("No links found on main webpage", ArrayUtil.hasContent(allLinks));

Test failures appear

After this test was written and included into the build, the continuous integration suddenly reported test failures – in the unit tests. I didn’t change any test there and had no need to change the production code, either. So what was causing the test to fail?

The failing unit test class was very old, ensuring the persistence of some data structure to XML and back. The test that actually failed took care of the XML parser behaviour when an empty XML file was read:

public void testReadingEmptyXML() throws IOException {
    try {
        new XMLQueryPersister(new StringReader(XMLQueryPersisterTest.EMPTY_XML), null).loadQueries();
        Assert.fail();
    } catch (ParseException e) {
        Assert.assertEquals("Error on line 1: Premature end of file.", e.getMessage());
    }
}

The assertion that checks the exception message failed, stating that the actual message was now “Error on line -1: Premature end of file.”

Hunting the bug

How can the inclusion of a new integration test have such an impact on the rest of the system? Thanks to continuous integration, the cause for the behaviour change could only lie in the most recent commit. A quick investigation revealed the culprit:

HttpUnit has a third-party dependency on the Xerces xml parser (or another equivalent org.xml.sax parser), see their FAQ for details. When I included the libraries, I accidentally changed the default xml parser for the whole system to Xerces in the version that HttpUnit delivers. This altered the handling of the “premature end of file” case to the new behaviour, causing the test to fail. As these libraries are only included in the classpath when tests are run, the change only happens in the test environment, not in production.

Test classpath versus production classpath

The real issue here isn’t the change in behaviour, this can be taken into account if you have a good test coverage. The issue is different classpaths for test and production environments. If you don’t want to deploy all your test scope libraries (thus making the production classpath similar to the test classpath), you should pay extra attention to what you include in your test classpath. It might alter your system, so that you don’t test the real behaviour anymore.

Resolving the issue

In my case, it was sufficient to remove the Xerces jar from the classpath again. A compliant org.xml.sax parser is already included in the Java core API. It’s the parser that already got used in production and should be used for the tests, too.

Update/Correction: After removing Xerces, HttpUnit stopped working correctly. The quick fix now is to include Xerces in the production classpath and deal with the behaviour changes. I will investigate this issue further and append the outcome as a comment to this blog entry. Update 2: Issue resolved, see comment section for the solution.

This taught me a lesson to always be aware of the dependencies, even if it’s “only” the test scope dependencies.

Summary

Including the Xerces xml parser as a dependency for a testing framework (HttpUnit) changed the behaviour of my system under test, albeit for the tests only. The issue was easily resolved by removing it again, but now I know that testing frameworks have side effects, too.

SSD? Don’t think! Just Buy!

SSDs makes everything blazingly fast – even Grails + IDEA development

My personal experience with SSDs began with an Intel X25M that I built into a Lenovo Thinkpad R61. It replaced a Seagate 160 GB 5400rpm which in combination with Windows Vista … well, let’s just say, it wasn’t that fast.

The SSD changed everything. It was not just faster, it was downright awesome! As if I had a completely new computer.

With that in mind I thought about my desktop PC. It’s a little more than 2 year old Windows XP box, Intel Core2Duo 2.7 GHz, 4GB RAM, with a not so slow Samsung HDD. I use it mainly for programming, which is most of the time Grails programming under IntelliJ IDEA.

And let me tell you, the Grails + IDEA combination can get dog slow at times. The start-up time of IDEA alone gives you time to skim over the first three pages of Hacker News and read the latest XKCD.

So the plan was to put an extra SSD into the Windows box and put only programming related stuff on it. This would save me the potential hassle of moving my whole system but would still give me development speed-up.

I had to be a little careful because the standard settings for IDEA’s so-called “system path” and “config path” is in the user’s home directory. (Btw, this settings can be changed in file “idea.properties” which resides in “IDEA_INSTALLATION_DIR\bin”, e.g.: c:\Progam Files\JetBrains\IntelliJ IDEA 9.0.4\bin)

I think you already guessed the result. Three words: fast, faster, SSD. It’s just amazing! IDEA start-up is so fast now, I barely have time for a quick look at the newest headlines on InfoQ.

The next step is of course to put the whole system on SSD but that will probably have to wait until we upgrade the whole company to Win7. Can’t wait… 🙂

Acceptance testing a grails app with selenium-rc

Having extensive acceptance tests is the basis of delivering high quality releases with very few regressions for long time projects. This is even more true when your environment uses dynamically typed languages and changing requirements. One of our Grails projects is running for several years now and continues to evolve and grow.  We are in dire need of more acceptance tests and especially their automated execution. Manual testing is not feasible and our coverage through unit and integration tests is not enough. We have a nice set of Selenium IDE acceptance tests already though. They were executed very infrequently which let some bugs slip through into production.

I want to describe our approach to automated and extensive acceptance testing below:

  1. We create the acceptance tests using capture & replay with selenium IDE. This is a fast way to exercise a new feature through a repeatable test.
  2. We think that maintaining the tests in code offers much more flexiblity and is easier to run in continuous integration (CI) than maintaining the selenium IDE html code. So we export the captured test to play nicely with the grails selenium-rc plugin. Kurt Harriger explains the setup and usage of the selenium-rc grails plugin. You need to make some changes to the exported code for everything to work nicely:
    1. Delete or change package declaration.
    2. Choose a grails functional test compatible file name like MyAcceptanceTests.groovy. We use the Junit4 export but Groovy export works also because the difference is only marginal and both must be adjusted in some places.
    3. Change the class name to match the file name without extension if they are not equal already.
    4. Change the exported test to extends from GroovyTestCase instead of *SeleneseTestCase.
    5. Add the @Mixin(SeleniumAware) annotation to the test class.
    6. Remove the setup and teardown methods.
    7. Replace verifyTrue() and friends with junit assertions.
  3. Each test has to setup it’s initial state. This leads to independent acceptance tests at the expense of some longer running time but is well worth the cost imho.
  4. The resulting selenium-rc tests can be run easily using grails test-app -functional and thus integration in the build process is pretty straighforward. We currently use ant to wrap the grails calls, but other ways may be more feasible depending on your infrastructure.

The end result is fast creation of acceptance tests and much flexibility setting up the test fixture and maintaining the tests. Using the grails plugin you gain easy execution of the tests on the developer machines and CI servers as well. With extensive automated acceptance tests the danger of regressions is greatly reduced. But be sure to not neglect unit and integration tests!

Active Object with POCO’s Active Methods

POCO’s ActiveMethods require minimal additional code to implement the Active Object design pattern

Active Object is a well known design pattern for synchronizing access to an object and/or resource. The basic idea is to separate method invocation from method execution which is done in a dedicated thread.

Instead of using the objects interface directly, a client of an Active Object uses some kind of  proxy which enqueues a so-called Method Request for later execution. The proxy finishes immediately and returns to the client some sort of callback, or variable, by which the client can receive the result. These intermeditate result variables are also known as Futures.

As always, there are lots of ways to implement this pattern. For example, if you had an interface like this

class MyObject
{
  public:
    int doStuff(const std::string& param) =0;
    std::string doSomeOtherThing(int i) =0;
};

applying a straight forward implementation, you would first transform this into a proxy and method request classes:

class MyObjectProxy
{
  public:
    MyObjectProxy(MyObject* theObject);
    // proxy methods
    Future<int> doStuff(const std::string& param);
    Future<std::string> doSomeOtherThing(int i);
  private:
    MyObject * _myObject;
};

class MethodRequest_DoStuff :
  public AbstractMethodRequest
{
  public:
    MethodRequest_DoStuff(const std::string& param);
    // all method request classes must implement execute()
    virtual void execute(MyObject* theObject);

  private:
    const std::string _param;
};

… and so on (for more details see this basic paper by Douglas C. Schmidt, or read chapter Concurrency Patterns in POSA2).

It’s easy to see that this implementation produces a lot of boilerplate code. To solve this, you could either cook up some code generation, or look for some language support to reduce the amount of characters you have to type. In C++, some sort of template solution can be the way to go, or…

Introducing Active Methods

With class ActiveMethod together with support classes ActiveDispatcher and ActiveResult the POCO C++ libraries provide very simple and elegant building blocks for implementing  the Active Object pattern.

ActiveMethod:  this is the core piece. When called, an ActiveMethod executes in its own thread.

ActiveResult: this is what I referred to earlier as a Future. Instances of ActiveResult are used to pass the result of an ActiveMethod call back to the client.

ActiveDispatcher: if you only use ActiveMethods, every ActiveMethod thread can execute in parallel.  With ActiveDispatcher as base class, ActiveMethod calls are serialized, thus implementing real™ Active Object behaviour.

Here my earlier example using ActiveMethods:

class MyObject
{
  public:
    // ActiveMethods are initialized in the ctors
    // initializer list
    MyObject()
      : doStuff(this, &MyObject::doStuffImpl),
        doSomeOtherThing(this, &MyObject::doSomeOtherThingImpl)
    {}

    ActiveMethod<int, std::string, MyObject> doStuff;
    ActiveMethod<std::string, int, MyObject> doSomeOtherThing;
  private:
    int doStuffImpl(const std::string& param);
    std::string doSomeOtherThingImpl(int i);
};

This is used as follows:

MyObject myObject;
ActiveResult<std::string> result = myObject.doSomeOtherThing(42);
...
result.wait();
std::cout << result.data() << std::endl;

This solution requires minimal amounts of additional code to transform your lame and boring normal object into a full-fledged Active Object. The only downside is that Active Methods currently can only have one parameter. If you need more, you have to use tuples or parameter objects.

Have fun!

Is Groovy++ already doomed?

<disclaimer>I really like Groovy and other cool languages like Scala, Fantom, Gosu or Clojure targetting the JVM.</disclaimer>

I know the title is a bit provocative but I want to express a concern regarding Groovy++. In my perception most people think of Groovy++ as an extension for Groovy which trades dynamic dispatching for static typing and dispatching yielding performance. So you can just look for hot spots in your code and resolve them with some annotations. Sounds nice, doesn’t it?.

That seems to be the promise of Groovy++ but it isn’t. Alex Tkachman, the founder of the Groovy++ project states this clearly in this comment to an issue with Groovy++: “100% compatibility with regular Groovy is nice when possible and we do our best to keep it but it is not a must.”.

Imho the mentioned issue together with this statement reduces the target audience to a few people who think of Groovy++ as a better Java, not a faster and type-safe Groovy where needed. I do not think there are too many people thinking that way. I think wide adoption of such a Groovy++ will not happen given the alternatives mentioned in the disclaimer above and Groovy itself. I hope they will strive for 100% compatibility with Groovy…

Bug hunting fun with std::sort

Small errors in custom comparison functions used with std::sort can lead to hard-to-find bugs.

The other day I came across a nice little C++-shoot-yourself-in-the-foot at one of our customers. Let’s see how fast you can spot the problem. The following code crashes with segmentation fault sometime, somewhere in the sort call (line 31).

#include <iostream>
#include <vector>
#include <boost/shared_ptr.hpp>
#include <boost/bind.hpp>

using namespace std;
using namespace boost;

enum SORT_ORDER
{
  SORT_ORDER_ASCENDING,
  SORT_ORDER_DESCENDING
};

bool compareValues(const std::string& valueLeft,
                   const std::string& valueRight,
                   SORT_ORDER order)
{
  const bool compareResult = (valueLeft < valueRight);
  if (order == SORT_ORDER_DESCENDING) {
    return !compareResult;
  }
  return compareResult;
}

int main(int argc, char *argv[])
{
  std::vector<std::string> strValues(300);
  std::fill(strValues.begin(), strValues.end(),
            "Hallo");
  std::sort(strValues.begin(), strValues.end(),
            bind(compareValues, _1, _2, SORT_ORDER_DESCENDING));
  return EXIT_SUCCESS;
}

Any ideas? The tricky thing about this bug is that the stacktrace output in the debugger gives absolutely no hint at all about its cause. And this is a simplified version of the real code which has to sort boost::shared_ptrs instead of strings. Believe me, you don’t want to see that stacktrace. Because of the use of boost::bind together with boost::shared_ptrs it looks, well, let’s say intimidating.

Still no idea?

I’ll give you a hint. If the SORT_ORDER is set to SORT_ORDER_ASCENDING everything is fine. …

Ok, the problem is that std::sort algorithm must be given a comparison function (object) that defines a strict weak ordering on the elements that are to be sorted. In other words the comparison function object must implement the ‘<‘ (less than) relationship on the elements.

Unfortunately, lines 20 to 22 break this ordering when SORT_ORDER_DESCENDING is given. The initial idea of this code was that, well, if compareResult gets returned on ascending sort order, lets just return the negation of it when the “negation” of acscending order is requested. This, of course, destroys the strict weak ordering requirement because whenever valueLeft == valueRight, the function returns true, meaning instead that valueLeft < valueRight. And this somehow wreaks havoc inside std::sort.

A better version of the function could be:

...
bool compareValues(const std::string& valueLeft,
                   const std::string& valueRight,
                   SORT_ORDER order)
{
  // solution: return false independent of sort order
  // whenever valueLeft == valueRight
  if (valueLeft == valueRight) {
    return false;
  }
  const bool compareResult = (valueLeft < valueRight);
  if (order == SORT_ORDER_DESCENDING) {
    return !compareResult;
  }
  return compareResult;
}
...

The really annoying thing about this whole issue is that std::sort just randomly crashes with a stack trace that shows nothing but some weird memory corruption going on. After the initial shock, this sends you down the complete wrong bug hunting road where you start looking for spots where memory could be overwritten or the like.

So beware of custom comparison functions or function objects. They might look innocent and easy, but they can give you lot’s of headaches.

Developing Grails Apps – Some Dark Sides

Most of the time, developing Grails apps is a nice experience. But there are also dark sides. One of which is when bugs do appear or do not appear depending on how you started your app.

Usually, I try to avoid it but this time a Disclaimer is in order: This is not a Grails rant. Most of the time developing Grails projects is fast and smooth. Using Grails brought many advantages for us. But there are also dark sides…

My main criticism is that Grails abstractions are more than leaky! In every list of examples for the definition of the term Leaky Abstraction Grails should be top. As soon as you leave the tutorial/scaffolding/helloworld level you have to know a lot about the underlying stack. And with Hibernate and Spring neither of the words small, easy and lightweight do apply.

GORM, too, is only easy to use at first sight. The very informative blog series about GORM gotchas should absolutely become part of the user guide or the refence docs.

And there are those times where it gets really unpleasant. This is e.g. when a bug does appear in your grails application running in a servlet container (packaged in a .war)  but does not appear when the application is started from within the IDE. Our last one of those was a naming conflict in a .gsp file. The controller handed a model like this to the .gsp:

...
return [fieldValue: 'THE_VALUE', ...]

The model entry ‘fieldValue’ was used in the .gsp to set the value of a combo box. Unfortunately, ‘fieldValue’ is also the name of a built-in Grails tag …

Admittedly, ‘fieldValue’ was not the wisest choice of names and I would certainly expect to get scolded loudly by Grails for that – ideally with a nice descriptive exception. But what happend instead led to a loud scolding of Grails from us. And to some big question marks: What is the difference between executing Grails from the IDE and within a servlet container with respect to naming resolution? Why is there a difference, at all?

We had a hard time figuring out this one, not least because the error message was not very telling. And since this was not the first of those works-in-the-IDE-but-not-in-a-real-environment bugs there is always this slightly uneasy feeling…

As I said in the beginning, most of the time developing Grails applications is nice and shiny. I would not support their slogan, though. My personal search for the best web development tool is definitively not over.

How about your search?

Combine cobertura with the awesomeness of crap4j

Want the awesomeness of crap4j without running your tests twice in your build? Just combine it with your cobertura data using crapertura.

You may have heard of crap4j when it was still actively developed. Crap4j is a software metric that points you to “crappy” methods in your projects by combining cyclomatic complexity numbers with test coverage data. The rationale is that overly complex code can only be tamed by rigorous testing or it will quickly reduce to an unmaintainable mess – the feared “rotten code” or “crappy code”, as Alberto Savoia and Bob Evans, the creators of crap4j would put it. The crap4j metric soon became our most important number for every project. It’s highly significant, yet easy to grasp and mandates a healthy coding style.

Some enhancements to crap4j

Crap4j got even better when we developed our own custom enhancements to it, like the CrapMap or the crap4j hudson plugin. We have a tool that formats the crap4j data like cobertura’s report, too.

A minor imperfection

The only thing that always bugged me when using crap4j inside our continuous integration build cycle was that at least half the data was already gathered. Cobertura calculates the code coverage of our tests right before crap4j does the same again. Wouldn’t it be great if the result of the first analysis could be re-used for the crap metric to save effort and time?

Different types of coverage

Soon, I learnt that crap4j uses the “path coverage” to combine it with the complexity of a method. This is perfectly reasonable given that the complexity determines the number of different pathes through the method. Cobertura only determines the “line coverage” and “branch coverage”. As it stands, you can’t use the cobertura data for crap4j because they represent different approaches to measure coverage. That’s still true and probably will be for a long time. But the allurement of the shortcut approach was too high for me to resist. I just tried it out one day to see the real difference.

A different metric

So, here it is, our new metric, heavily inspired by crap4j. I just took the line and branch coverage for every method and multiplied them. If you happen to have a perfect coverage (1.0 on both numbers), it stays perfect. If you only have 75% coverage on both numbers, it will result in a “crapertura coverage” of 56,25%. Then I fed this new coverage data into crap4j and compared the result with the original data. Well, it works on my project.

Presenting crapertura

Encouraged by this result, I wrote a complete ant task that acts similar to the original crap4j ant task. You can nearly use it as a drop-in replacement, given that the cobertura XML report file is already present. Here is an example ant call:


<crapertura
coberturaReportFile="/path/to/cobertura/coverage.xml"
targetDirectory="/where/to/place/the/crap4j/report"
classesDirectory="/your/unarchived/project/class/files"
/>

It will output the usual crap4j report files to the given target directory. Please note that even if it looks like crap4j data, it’s a different metric and should be treated as such. Therefore, online comparison of numbers is disabled.

The whole project is published on github. Feel free to browse the code and compile it for yourself. If you want a binary release, you might grab the latest jar from our download server.

The complete usage guide can be found on the github page or inside the project. If you have questions or issues, please use the comment section here.

Conclusion

If crapertura is able to give you nearly the numbers that crap4j gave you is up to your project, really. Our test project contained over 20k methods, but very little crap. The difference between crap4j and crapertura was negligible. Both metrics basically identified the same methods as being crappy. Your mileage may vary, though. If that’s the case, let us know. If your experience is like ours, you’ve just saved some time in your build cycle without sacrificing quality.

Why I give lectures in software engineering

I’m often asked why I give lectures in software engineering, as they appear to not pay off for me. I think they do and here is why.

<a href="http://de.fotolia.com/id/21746212" mce_href="http://de.fotolia.com/id/21746212" title="" alt="">falcn</a> - Fotolia.com

For more than eight years, I give lectures in software engineering, object oriented programming and software development “best practices”. I have to spend nearly a day every week for six months in the year to prepare and hold the classes. My normal work schedule is always very stuffed with tasks, I have to affront my other duties sometimes in order to show up in front of the students. On many occassions, I’ve been asked why I keep giving lectures despite pressing liabilities, inferior payment and generally better alternatives elsewhere.

Here is a list with answers I’ve given to this questions over and over again. I do not want to convince you that giving lectures is the best thing right after sliced bread or that you will experience any of these if you manage to get in the same position. It’s just a rational explanation why the question still strikes me as odd.

  • It’s pure fun – This surely doesn’t count for everyone, but for me, speaking (ranting, raving, arguing) about software development counts as fun times. Being “on stage” in front of the students helps me to free my thoughts from dead freight and completely concentrate on the topics.
  • I’m being paid to recapitulate the basics – This are two advantages in one: being paid cannot be bad (albeit payment can always improve) and to repeat the basics of my craft on a regular schedule can be seen in the tradition of katas. I’m very bulletproof in discussions about fundamental topics of software engineering because I’ve heard most questions and had to answer them multiple times already.
  • I’m constantly learning new facets about well-known topics – My students always bring in unique and original thoughts about topics that I thought to have mastered. And then, a new way to access things emerges, at least for me. I feel very certain that I’m still learning more during the lectures than my students do. And feedback suggests that they learn a lot.
  • I’m honing my verbal abilities – Giving a lecture is all about speaking without script and responding to the audience. You have to make your points, but you cannot force them. Sometimes I feel like a stand-up comedian for technical knowledge. Having the ability to speak fluently while preparing the next topic in the back of your head is a great advantage in every situation including verbal communication.
  • Roughly 100 aspiring developers remember me every year – What they will remember me for can be debated about, but they will remember. This is all about “networking”, but focussed on members of my own profession. The reach of this network amazes me every time when it loops back.
  • I can contact every local company with job training – Due to the nature of the Cooperative State University where I’m giving my lectures, I can also establish contact to every software company in the vicinity. Many contacts would never happen without my function as lecturer.
  • I keep in touch with hypes – Students are easy prey for IT hypes. Their experience with different technologies isn’t embittered by analogies from the past. All I have to do is to listen to them when they tell me about their work and hobby projects. And then I can draw my own conclusions based on their first-hand experience.

All these reasons and some more are enough for me to stick with the job. You can see a lot of short term benefits and some aspects that might pay off at medium term. On the long run, I’m convinced that my personal advantages from this job will outweight the (sometimes serious) drawbacks. And then, I haven’t yet included the advantages that my students took along from my lectures, hopefully.

If you happen to give lectures too, I would be pleased if you blog about your reasons for doing so, and announce your post here. Or just use the comment section.