Author: Miq

We have an application in maintenance mode for some years now. One part of the app displays messages in a certain format. They contained %-characters which have a special meaning. Both we and our customer thought they were about encoding line endings or some such. One day our customer reported missing parts within these messages. We dove down into the issue, analysed the raw messages containing a few %-signs and noticed some weird looking code:

public String parse(String message) {
    StringChunker tok = new StringChunker(message, Text.PERCENT);
    DirectChunkBuffer result = new DirectChunkBuffer(Text.NEWLINE);
    if (tok.hasMoreChunks()) {
        result.add(tok.getNextChunk());
    }
    return result.toString();
}

The if-statement feels unusual here as most would expect a while loop essentially splitting the original message by % and putting it together again with newlines in between. Almost immediately we thought of a bug that never until now occurred in production triggered by malformed raw messages.

But our unit tests documented clearly the current behaviour as correct. So we decided to talk again with our customer. He then asked his experts and they confirmed the behaviour and explained their workflow. The %-characters were used as comment characters to hide text blocks the expert workers used as templates. Nothing after the first %-character should be displayed. They also confirmed that the displayed message was correct and the whole error report was indeed some kind of communication problem somewhere in the organisation.

The tests saved us from breaking specified and correctly working behaviour.

After the clarification by the experts and we improved the situation by refactoring the code to communicate its intent clearer. We also documented the message format in the javadocs and a wikipage in addition to the tests.

Disclaimer

This post is not intended as some kind of Groovy-bashing. It rather points out some of the common problems and maybe differences in the mindset between Java and Groovy developers.

Groovy powers Grails and Grails empowers us to build modern web application conveniently and fast. We have come to love many of the features of Groovy and Grails. Sadly, every once in a while you stand puzzled before some bizarre error message. Often it is our own fault, but sometimes it is a real problem in the software stack we are using. Grails was quite buggy some time ago (pre 1.2) but we are more and more happy with its development. Groovy itself has been quite solid for a long time but things like dynamic dispatch with null values, boolean truth  and its  NullObjects (e.g. null + null == "nullnull"!) may yield some surprises.

A few days ago we received an failure report from a client and found the corresponding exception in our logs:

java.lang.UnsupportedOperationException
at $Proxy12.toString(Unknown Source)
at java.lang.Throwable.getLocalizedMessage(Throwable.java:267)
at java.lang.Throwable.toString(Throwable.java:343)
at java.lang.String.valueOf(String.java:2827)
at org.apache.commons.logging.impl.SLF4JLocationAwareLog.error(SLF4JLocationAwareLog.java:211)
at org.apache.commons.logging.Log$error$0.call(Unknown Source)
at UserGroupController.callSendMail(UserGroupController.groovy:117)

You may be able to see that the UnsupportOperationException is thrown in an attempt to log a Throwable. Yeah right, toString() of some generated proxy object throws an exception. This is not exactly what you would expect from a call to the toString()-method and violates the principle of least astonishment. Equally bad in this case is he fact that it shadows the real cause of the problem. Some research revealed that this is a bug in Groovy itself.

This leads me to my highly speculative and opionated “mindset” hypothesis. In the java world people try to avoid the unexpected at the cost of clunkyness and verbosity. Examples for this mindset are static typing and checked exceptions. Sources of uncertainty, like reflection and downcasts, are minimized and put in defined areas of code often documented and annotated. Everyone tries to strictly define everything and relies on these definition. All this sums up to quite a burden and may feel like a cage.

Enter Groovy which removes a lot of the burden by its more liberal syntax and other language features. Suddenly, you are free and do not have to define everything and life feels a lot easier at first. You do not need well designed class hierarchies, just use duck typing. Everything is dynamic and you can cope with things at runtime. Not only the above bug (it is still a bug, not intended behaviour!) but also the handling of boolean values or null may make the seasoned java developer shout “WTF!” from time to time.

So what is the bottom line now? Both worlds have their pros and cons. In Groovy fundamental things can surprise you at runtime so there is an even greater need for good test coverage and a thorough knowledge of the language. Groovy (and Grails for that part) are not as easy as they seem in smaller projects when going big. The Java camp has a solid base but should strive to make life more convenient for the developer. It is mostly the verbosity and some missing features like type inference or closures that are driving people away from Java. Imho both languages and the Java platform with its great community and eco-system have a bright future ahead but there are some bumps on the way ahead.

We developed an applet some years ago that was supposed to run 24/7. Basically, it fetches some values periodically and plots them in a chart. It always shows the last 24 hours. Everything seemed to work fine but every few weeks it crashed. The problems were clouded by hardware instabilities and the use of some obscure JVM. After quite a bit of analysis it became clear that it was a bug in our applet: a memory leak. Here is the troublemaker code:

for (int i = 0; i < dataSeries.getItemCount(); i++) {
    XYDataItem dataItem = dataSeries.getDataItem(i);
    if (dataItem.getX().longValue() < startDate.getTime().getTime()) {
        dataSeries.remove(i);
    } else {
        break;
    }
}
dataSeries.add(newDataPoint);

This simple and innocently looking piece of code essentially removes old items from a list by index. Many of you may already have spotted the main problem leading to the leak. Removing items by index means that following items shift one place towards the head of the list. As the index is incremented one element is skipped by the next iteration. This added up over time and lead to an OutOfMemoryError after some weeks.

Now, even if the code is not great it does relatively look straightforward on first sight, yet it is not and is contains errors. Making things worse, the code was buried somewhere in some tangled logic. This leads me to the point of my post:

Write unit tests even for relatively simple code.

Most likely writing a unit test for this cleanup work would have led to a class that manages the dataSeries and nothing more. Quite easy to understand and test in isolation. The problem would never have slipped into production and caused months of  investigating different stability problems.

The programmer may not have been aware of iterators but he should have made sure that this block works as intended. The best way to do this is automated unit tests. They make sure that your building blocks are as solid as you expect them to be. Use acceptance testing to make sure you have put your building blocks together the right way. Together unit and acceptance tests will save countless hours hunting regressions.

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!