Readable Code Needs Time and Care

A few weeks ago I was about to write an acceptance test involving socket communication. Since I was only interested in a particular sequence of exchanged data, I needed to wait for the start command and ignore all information sent prior to that command. In this blog post I’d like to present the process of enhancing the readability of the tiny piece of code responsible for this task.

The first version, written without thinking much about readability looked something like the following:

private void waitForStartCommand(DataInputStream inputStream) {
  String content = inputStream.readUTF();
  while (!START_COMMAND.equals(content)) {
    content = inputStream.readUTF();
  }
}

The aspect that disturbed me most about this solution was calling inputStream.readUTF() twice (Remember: DRY). So I refactored and came up with:

private void waitForStartCommand(DataInputStream inputStream) {
  String content = null;
  do {
    content = inputStream.readUTF();
  } while (!START_COMMAND.equals(content)) {
}

In this version the need to declare and initialize a variable grants far too much meaning to an unimportant detail. So, a little refactoring resulted in the final version:

private void waitForStartCommand(DataInputStream inputStream) {
  while (startCommandIsNotReadOn(inputStream)) {
    continue;
  }
}

private boolean startCommandIsNotReadOn(DataInputStream inputStream) {
  return !START_COMMAND.equals(inputStream.readUTF());
}

This example shows pretty well how even rather simple code may need to be refactored several times in order to be highly readably and understandable. Especially code that handles more or less unimportant side aspects, should be as easily to understand as possible in order to avoid conveying the impression of being of major importance.

Hibernate Objects in HTTP Sessions

While discussing common problems and pitfalls in web development
we stumbled on the pitfall of storing hibernate objects in a HTTP session.

While discussing common problems and pitfalls in web development we stumbled on the pitfall of storing hibernate objects in a HTTP session. I do not recommend doing this mainly because of the common pitfall with detached objects.
Usually the lifecycle of a hibernate session is attached to a request/response. So after the end of the response the hibernate object is detached. Accessing non loaded associations of detached objects causes a LazyInitializationException. Even if you try to update or save the detached object you will get an exception if the object isn’t in the hibernate session already. You could re-attach/merge the detached object but this writes your changes on the detached object to the database.
A better way would be to store only the identifier of the hibernate object in the HTTP session,
so you can load the object from the database when you need it.

When it comes to multithreading, better be safe than sorry

Writing multithreaded applications in Java is hard. Here are five problems and how to avoid them without much effort (mostly).

Recently, I attended a code review of the core parts of a web application, written in Java. The application is used by a large customer base and occassionally, there are error reports and exceptions in the log files. Some of these exceptions are the dreaded ConcurrentModificationExceptions, indicating conflicting read/write access on an unsynchronized collection data structure. In the code review, we found several threading flaws, but not after an exhaustive reading of the whole module. Here, I want to present the flaws and give some advice on how to avoid them:

The public lock

In some parts of the code, methods were defined as synchronized through the method declaration keyword:

public synchronized String getLastReservation() { [...]

While there is nothing wrong with this approach in itself, it can be highly dangerous in combination with synchronized blocks. The code above effectively wraps a synchronized block using the object instance (this) as a lock. No information of an object is more publicly visible as the object reference (this), so you have to check all direct or indirect clients of this object if they synchronize on this instance, too. If they do, you have chained two code blocks together, probably without proper mentioning of this fact. The least harmful defect will be performance losses because your code isn’t locked as fine grained as it could be.

The easiest way to avoid these situations it to always hide the locks. Try not to share one object’s locks with other objects. If you choose publicly accessible locks, you can never be sure about that.

The subtle lock change

In one class, there were both instance and class (static) methods, using the synchronized keyword:

public synchronized String getOrderNumberOf(String customerID) { [...]
public  synchronized static int getTotalPendingOrders() { [...]

And while they were both accessing the same collection data structure (a static hashmap), they were using different locks. The lock of the instance method is the instance itself, while the lock of the static method is the class object of the type. This is very dangerous, as it can be easily missed when writing or altering the code.

The best way to prevent this problem it to avoid the synchronized modifier for methods completely. State your locks explicitely, all the time.

Partial locking

In a few classes, collection datatypes like lists were indeed synchronized by internal synchronized-blocks in the methods, using the private collection instance as lock. The synchronized blocks were applied to the altering methods like putX(), removeX() and getX(). But the toString() method, building a comma-separated list of the textual list entries, wasn’t synchronized to the list. The method contained the following code:

public String toString() {
    StringBuilder result = new StringBuilder();
    for (String entry : this.list) {
        result.append(entry);
        result.append(",");
    }
    [...]
    return result.toString();
}

I’ve left out some details and special cases, as they aren’t revelant here. The problem with the foreach loop is that an anonymous Iterator over the list is used and it will relentlessly monitor the list for any changes and throw a ConcurrentModificationException as soon as one of the properly synchronized sections changes it. The toString() method was used to store the list to a session dependent data storage. Every once in a while, the foreach loop threw an exception and failed to properly persist the list data, resulting in data loss.

The most straight-forward solution to this problem might be to add the missing synchronization block in the toString() method. If you don’t want to block the user session while writing to disk, you might traverse the list without an Iterator (and be careful with your assumptions about valid indices) or work on a copy of the list, given that an in-memory copy of the list would be cheap. In an ACID system scenario, you should probably choose to complete your synchronized block guards.

Locking loophole

Another problem was a collection that was synchronized internally, but could be accessed through a getter method. No client could safely modify or traverse the collection, because they had the collection, but not the lock object (that happened to be the collection, too, but who can really be sure about that in the future?). It would be ridiculous to also provide a getter for the lock object (always hide your locks, remember?), the better solution is to refactor the client code to a “tell, don’t ask” style.

To prevent a scenario when a client can access a data structure but not its lock, you shouldn’t be able to gain access to the data structure, but pass “command objects” to the data structure. This is a perfect use case for closures. Effectively, you’ll end up with something like Function or Operation instances that are applied to every element of the collection within a synchronized block and perform your functionality on them. Have a look at op4j for inspirational syntax.

Local locking

This was the worst of all problems and the final reason for this blog entry: In some methods, the lock objects were local variables. In summary, these methods looked like this:

public String getData() {
    Object lock = new Object();
    synchronized (lock) {
        [...]
    }
}

Of course, it wasn’t that obvious. The lock objects were propagated to other methods, stored in datastructures, removed from them, etc. But in the end, each caller of the method got his own lock and could henceforth wreck havoc in code that appeared very well synchronized on first look. The error in its clarity is too stupid to be widespread. The problem was the obfuscation around it. It took us some time to really understand what is going on and where all that lock objects really come from.

My final advice is: If you have to deal with multithreading, don’t outsmart yourself and the next fellow programmer by building complex code structures or implicit relationships. Be as concise and explicit as you can be. Less clutter is more when dealing with threads. The core problem is the all-or-none law of thread synchronization: Either you’ve got it all right or you’ve got it all wrong – you just don’t know yet.

Hide your locks, name your locks explicitely, reduce the scope of necessary locking so that you can survey it easily, never hand out your locked data, and, most important, remove all clutter around your locking structures. This might make the difference between “just works” and endless ominous bug reports.

Embedding Python into C++

In one of our projects the requirement to run small user-defined Python scripts inside a C++ application arose. Thanks to Python’s C-API, nicknamed CPython, embedding (really) simple scripts is pretty straightforward:

Py_Initialize();
const char* pythonScript = "print 'Hello, world!'\n";
int result = PyRun_SimpleString(pythonScript);
Py_Finalize();

Yet, this approach does neither allow running extensive scripts, nor does it provide a way to exchange data between the application and the script. The result of this operation merely indicates whether the script was executed properly by returning 0, or -1 otherwise, e.g. if an exception was raised. To overcome these limitations, CPython offers another, more versatile way to execute scripts:

PyObject* PyRun_String(const char* pythonScript, int startToken, PyObject* globalDictionary, PyObject* localDictionary)

Besides the actual script, this function requires a start token, which should be set to Py_file_input for larger scripts, and two dictionaries containing the exchanged data:

PyObject* main = PyImport_AddModule("__main__");
PyObject* globalDictionary = PyModule_GetDict(main);
PyObject* localDictionary = PyDict_New();
PyObject* result = PyRun_String(pythonScript, Py_file_input, globalDictionary, localDictionary);

Communication between the application and the script is done by inserting entries to one of the dictionaries prior to running the script:

PyObject* value = PyString_FromString("some value");
PyDict_SetItemString(localDict, "someKey", value);

Doing so makes the variable “someKey” and its value available inside the Python script. Accessing the produced data after running the Python script is just as easy:

char* result = String_AsString(PyDict_GetItemString(localDict, "someKey"));

If a variable is created inside the Python script, this variable also becomes accessible from the application through PyDict_GetItemString (or PyDict_GetItem), even if it was not entered into the dictionary beforehand.

The following example shows the complete process of defining variables as dictionary entries, running a small script and retrieving the produced result in the C++ application:

Py_Initialize();
//create the dictionaries as shown above
const char* pythonScript = "result = multiplicand * multiplier\n";
PyDict_SetItemString(localDictionary, "multiplicand", PyInt_FromLong(2));
PyDict_SetItemString(localDictionary, "multiplier", PyInt_FromLong(5));
PyRun_String(pythonScript, Py_file_input, globalDictionary, localDictionary);
long result = PyInt_AsLong(PyDict_GetItemString(localDictionary, "result"));
cout << result << endl;
Py_Finalize();

Readability of Boolean Expressions

Readability of boolean expressions lies in the eyes of the beholder.

Following up on various previous posts on code readability and style I want to provide two more examples today – this time under the common theme of “handling of boolean values”.

Consider this (1a):

bool someMethod()
{
  if (expression) {
    return true;
  } else {
    return false;
  }
}

Yes, there are people who consider this more readable than (1b)

bool someMethod()
{
  return (expression);
}

Another example is this (2a):

  if (someExpression() == true)
    ...

versus my preferred version (2b):

  if (someExpression())
    ...

So what could be the reason for these different viewpoints? One explanation I thought of is as follows: Let’s say you have a background in C and you are therefore used to do something like:

#define FALSE (0)
#define TRUE (!FALSE)

In other words, you may not see boolean as a type of its own, like int and double, with a well-defined value range. Instead you see it more like an enumerated type which makes it feel very naturally do a expression == true comparison.

At the same time it feels not very natural to see the result of a boolean expression as being of type bool with all the consequences – e.g. to be able to return it immediately as in the first example.

Another explanation is that 1a and 2a are as verbose as it can be. You don’t have to make any mental efforts to understand what the code does.

While these may be possible explanations, my guess is that most of you, like me,  still see 1a and 2a as unnecessary visual clutter and consider 1b and 2b as far more readable.

Grails Gems: Command Objects

A series about the (little) gems found in Grails which can help many projects out there.

Besides domain objects command objects are another way to get validation and data binding of parameters. But why (or when) should you use them?
First when you do not want to persist the data. Like validating parameters for a search query.
Second when you just want a subset of the parameters which has no corresponding domain object. For example for keeping malicious data away from your domain objects.
Third when you get a delta of the new data. When you just want to add to a list and do not want to check if you get a single or a multiple value for your a parameter.

Usage

Usually you put the class of the command in the same file as the controller you use them in. The command object is declared as a parameter of the action closure. You can even use multiple one:

class MyController {
  def action = { MyCommand myCommand, YourCommand yourCommand ->
    ...
  }
}

Grails automatically binds the request parameters to the commands you supply and validates them. Then you can just call command.hasErrors() to see if the validation succeeded.

Separate your code domains

You can improve your code reusability by separating the technical domain code from the business domain code. This article tries to explain how to start.

When you develop software, you most likely have to think in two target domains at the same time. One domain will be the world of your stakeholder. He might talk about business rules and business processes and business everything, so lets call it the business domain. The other domain is the world you own exclusively with your colleagues, it’s the world of computers, programming languages and coding standards. Lets call it the technical domain. It’s the world where your stakeholders will never follow you.

Mixing the domains

Whenever you create source code, you probably try to solve problems in the business domain with the means of your technical domain – e.g. the programming language you’ve chosen on the hardware platform you anticipate the software to run on. Inevitably, you’ll mix parts of the business domain with parts of the technical domain. The main question is – will it blend? Most of the time, the answer is yes. Like milk in coffee, the parts of two domains will blend into an inseparable mixture. Which isn’t necessarily a bad thing – your solution works just fine.

The hard part comes when you want to reuse your code. It’s like reusing the milk in your coffee, but without the coffee. You’ve probably done it, too (reusing domain-blended code, not extracting the milk from your coffee) and it wasn’t the easy “just copy it over here and everything’s fine” reusability you’ve dreamt of.

Separating the domains

One solution for this task begins by realizing which code belongs to which domain. There isn’t a clear set of rules that you can just check and be sure, but we’ve found two rules of thumb helpful for this decision:

  • If you have a strong business domain data type model in your code (that is, you’ve modelled many classes to directly represent concepts and items from your stakeholder’s world), you can look at a line of code and scan for words from the business domain. If there aren’t any, chances are that you’ve found a line belonging to the technical domain. If you prefer to model your data structures with lists and hashmaps containing strings and integers, you’re mostly out of luck here. Hopefully, you’ve chosen explicit names for your variables, so you don’t end with a line stating map.get(key), when in fact, you’re looking up orders.getFor(orderNumber).
  • For every line of code, you can ask yourself “do I want to write it or do I have to write it?”. This question assumes that you really want to solve the problems of the business domain. Every line of code you just have to write because otherwise, the compiler, the QA department, your colleagues or, ultimately, your coder idol of choice would be disappointed is a line from the technical domain. Every line of code that would only disappoint your stakeholder if it would be missing is a line from the business domain. Most likely, everything that your business-driven tests assert is code from the business domain.

Once you categorized your lines of code into their associated domain, you can increase the reusability of your code by separating these lines of code. Effectively, you try to avoid the blending of the parts, much like in a good latte macchiato. If you achieve a clear separation of the different code parts, chances are that you have come a long way to the anticipated “copy and paste” reusability.

Example one: Local separation

Well, all theory is nice and shiny, but what about the real (coding) life? Here are two examples that show the mechanics of the separation process.

In the first example, we’re given a compressed zip file archive as an InputStream. Our task is to write the archive entries to disk, given that certain rules apply:

public void extractEntriesFrom(InputStream in) {
    ZipInputStream zipStream = new ZipInputStream(in);
    try {
         ZipEntry entry = null;
         while ((entry = zipStream.getNextEntry()) != null) {
             if (rulesApplyFor(entry)) {
                 File newFile = new File(entry.getName());
                 writeEntry(zipStream,
                      getOutputStream(basePath(), newFile));
             }
             zipStream.closeEntry();
         }
    } catch (IOException e) {
        e.printStackTrace();
    } finally {
        IOHandler.close(zipStream);
    }
}

This is fairly common code, nothing to be proud of (we can argue that the method signature isn’t as explicit as it should be, the exceptions are poorly handled, etc.), but that’s not the point of this example. Try to focus your attention to the domain of each code line. Is it from the business or the technical domain? Let me refactor the example to a form where the code from both domains is separated, without changing the additional flaws of the code:

public void extractEntriesFrom(InputStream in) {
    ZipInputStream zipStream = new ZipInputStream(in);
    try {
         ZipEntry entry = null;
         while ((entry = zipStream.getNextEntry()) != null) {
             handleEntry(entry, zipStream);
             zipStream.closeEntry();
         }
    } catch (IOException e) {
        e.printStackTrace();
    } finally {
        IOHandler.close(zipStream);
    }
}

protected void handleEntry(ZipEntry entry,
        ZipInputStream zipStream) throws IOException {
    if (rulesApplyFor(entry)) {
        File newFile = new File(entry.getName());
        writeEntry(zipStream,
            getOutputStream(basePath(), newFile));
    }
}

In this version of the same code, the method extractEntriesFrom(…) doesn’t know anything about rules or how to write an entry to the disk. Everything that’s left in the method is part of the technical domain – code you have to write in order to perform something useful within the business domain. The new method handleEntry(…) is nearly free of technical domain stuff. Every line in this method depends on the specific use case, given by your business domain.

Example two: Full separation

Technically, the first example only consisted of a simple refactoring (Extract Method). But by separating the code domains, we’ve done the first step of a journey towards code reusability. It begins with a simple refactoring and ends with separated classes in separated packages from two separated project parts, named something like “application” and “framework”. Even if you only find a class named “Tools” or “Utils” in your project, you’ve done intermediate steps towards the goal: Separating your technical domain code from your business domain code in order to reuse the former (because no two businesses are alike).

The next example shows a full separation in action:

WriteTo.file(target).using(new Writing() {
    @Override
    public void writeTo(PrintWriter writer) {
        writer.println("Hello world!");
        writer.println("Hello second line.");
        // more business domain code here
    }
});

Everything other than the first line (and the necessary java boilerplate) is business domain code. In the first line, only the specified target file isn’t technical. Everything related to opening the file output stream, handling exceptions, closing all resources and all the other fancy stuff you want to do when writing to a file is encapsulated in the WriteTo class. The equivalent to the handleEntry(…) method from the first example is the writeTo(…) method of the Writing interface. Everything within this method is purely business domain related code. The best thing is: you can nearly forget about the technical domain when filling out the method, as it is embedded in a reusable “code clamp” providing the proper context.

Conclusion

If you want to write reusable code, consider separating your two major code domains: the technical domain and the business domain. The first step is to be aware of the domains and distinguish between them. Your separation process then can start with simple extractions and finally lead to a purely technical framework where you “only” have to fill in the business domain code. By the way, it’s a variation of the classic “separation of concerns” principle, if you want to read more.

A shot at definitions beyond “unit test”

When doing research on which kinds of programmatic tests different developers and companies utilize and how they handle them, I realized that there is no common definition of terms and concepts. While most sources agree on what is and what is not a unit test, there are various contradictory definitions of what a test is, if it is not a unit test. In this blog post I’d like to present a brief overview of the definitions we are currently using. Since we steadily try to enhance and refine our development process and tools, the terms and concepts presented here are almost certain to change in the future.

Please note that this post is not intended to fully describe all the details of the different test approaches, but rather to give an idea and first impression on how we distinguish them.

Unit Tests

The most basic kind of programmatic tests, unit-tests, are likely to be the most commonly used kind of test. They help to determine that a small piece of code, e.g. a single method or class, behaves as intended by its developer. If properly applied, unit-tests provide a solid foundation to build an application upon. Figure 1 schematically depicts the scope of a unit-test in an exemplary software system.

Depending on the complexity of the tested system, techniques like mocking of dependencies may be required. Especially system resources need to be replaced by mocks, since unit tests need to be completely independent from them (Michael Feathers describes this and some other requirements of unit tests in his blog post “A Set of Unit Testing Rules”). Furthermore, unit tests are not meant to be long running, but instead have to execute within a split second.

Schematic view of a unit test of a component in an exemplary system
Figure 1: Schematic view of a unit test of an component in an exemplary system

Integration Tests

A more sophisticated approach to testing are integration tests which challenge a part or sub-system of an application made up of several units in order to determine whether these units properly cooperate. In contrast to unit tests, integration tests may include system resources and may also determine the test’s outcome by checking the state of these resources. This larger scope and the fact that the tested functionality is typically made up of several actions, leads to integration tests taking a multitude of the time taken by unit tests. Figure 2 schematically illustrates an integration test’s view on an exemplary system.

Schematic of an integration test in an exemplary system
Figure 2: Schematic of an integration test in an exemplary system

Acceptance Tests

The by far most involved technique to test the behavior of an application is the utilization of acceptance tests. While the other approaches challenge only parts of an application, acceptance tests are meant to challenge the application as a whole from a user’s point of view. This includes using system resources, as well as to control the application and verify its proper function as a user would: Through its (G)UI and without knowing anything about the internals of the software.

Schematic of an acceptance test in an exemplary system
Figure 3: Schematic of an acceptance test in an exemplary system

Conclusion

While some developers only distinguish between unit tests and other tests, defining the latter ones more clearly proved very useful when creating, using and explaining them to other developers and customers. Yet, these definitions are not carved in stone and certainly need to be refined over time. Thus, I would like to get to know your opinion on these definitions. Do you agree or do you have a completely different way of distinguishing between test approaches? How many kinds of tests do you distinguish? And why do you do so?

Prepare for the unexpected

In most larger projects there are many details which cannot be foreseen by the development team. Specifications turn out to be wrong, incomplete or not precise enough for your implementation to work without further adjustments. New features have to work with production data that may not be available in your development or testing environment.

The result I often observed is that everything works fine in your environment including great automated tests but fails nevertheless when deployed to production systems. Sometimes it is minor differences in the operating system version or configuration, the locale for example, may cause your software to fail. Another common problem is  real production data containing unexpected characters, inconsistencies in the data (sometimes due to bugs) or its sheer size.

What can we do to better prepare for unexpected issues after deployment?

The thing is to expect such issues and to implement certain countermeasures to better cope with them. This may conflict with the KISS principle but usually is worth a bit of added complexity. I want to provide some advice which proved useful for us in the past and may help you in the future too:

  1. Provide good, detailed and persistent debug output for certain features: Once we added a complex rule system which operated on existing domain objects. To check every possible combination of domain object states would have been a ton of work, so we wrote tests for the common cases and difficult cases we could think of. Since the correctness of the functionality was not critical we decided to rather display slightly incorrect information instead of failing and thus breaking the feature for the user. We did however provide extensive and detailed logs whenever our rule system detected a problem.
  2. Make certain parts of your communication interface to third party systems configurable: Often your system communicates to different kinds of users and other systems. Common examples are import/export functionality, web service APIs or text protocols. Even if most of the time details like date and number formats, data separators, line endings, character encoding and so forth are specified it often proves valuable to make them configurable. Many times the specification changes or is incorrect, some communication partner implements the protocol slightly different or a format deviates from your assumption breaking your application. It is great if you can change that with a smile in front of your client and make the whole thing work in minutes instead of walking home frustrated to fix the issues.

The above does not mean building applications with ultimate flexibility and configurability and ignoring automated tests or realistic test environments. It just means that there are typical aspects of an application where you can prepare for otherwise unexpected deviations of theory and praxis.

A VisualBasic.NET cheat sheet for Java developers

If you want to learn VisualBasic.NET coming from a Java perspective, we’ve prepared a little cheat sheet to ease the transition.

Sometimes, we cannot choose what language to implement a project in. Be it because of environmental restrictions (everything else is programmed in language X) or just because there’s an existing code base that needs to be extended and improved. This is when our polyglot programming mindset will be challenged. In a recent project, we picked up the current incarnation of VisualBasic, a language most of us willfully forgot after brief exposure in the late nineties, more than 10 years ago.

Spaceward Ho!

So we ventured into the land of VisualEverything, installing VisualStudio (without ReSharper at first) and finding out about the changes in VisualBasic.NET compared to VisualBasic 6, the language version we used back in the days. Being heavily trained in Java and “javaesque” languages, we were pleasantly surprised to find a modern, object-oriented language with a state-of-the-art platform SDK (the .NET framework) and only little reminiscences of the old age. Microsoft did a great job in modernizing the language, cutting out maybe a bit too much language specific stuff. VisualBasic.NET feels like C# with an uninspired syntax.

Making the transition

To ease our exploration of the language features of VisualBasic.NET, one of our student workers made a comparison table between Java and VisualBasic.NET. This cheat sheet helped us tremendously to wrap our heads around the syntax and the language. The platform SDK is very similar to the Java API, as you can see in the corresponding sections of the table. And because it helped us, it might also help you to gain a quick overview over VisualBasic.NET when you are heading from Java.

I have to thank Frederik Zipp a lot for his work. My only contribution to this cheat sheet is the translation from german to english. I can only try to imagine his effort of putting everything together. And while you might read the whole comparison in about 21 minutes (as stated in the title), it’s worth several hours of searching.

The downloads

And without much further ado, here are the download links for the HTML and PDF versions of the “Java vs. VisualBasic.NET cheat sheet”:

You may use and modify the documents as you see fit. If you redistribute it, please adhere to the Creative Commons Attribution-ShareAlike license. Thank you.