Testing C++ code with OpenCV dependencies

This is a documentation of a problem I ran into when I wrote C++ tests for a simple function, inclusive workaround.

The story:

Pushing for more quality and stability we integrate google test into our existing projects or extend test coverage. One of such cases was the creation of tests to document and verify a bugfix. They called a single function and checked the fields of the returned cv::Scalar.

TEST(ScalarTest, SingleValue) {
  cv::Scalar actual = target.compute();
  ASSERT_DOUBLE_EQ(90, actual[0]);
  ASSERT_DOUBLE_EQ(0, actual[1]);
  ASSERT_DOUBLE_EQ(0, actual[2]);
  ASSERT_DOUBLE_EQ(0, actual[3]);

Because this was the first test using OpenCV, the CMakeLists.txt also had to be modified:


Unfortunately, the test didn’t run through: it ended either with a core dump or a segmentation fault. The analysis of the called function showed that it used no pointers and all variables were referenced while still in scope. What did gdb say to the segmentation fault?

(gdb) bt
#0  0x00007ffff426bd25 in raise () from /lib64/libc.so.6
#1  0x00007ffff426d1a8 in abort () from /lib64/libc.so.6
#2  0x00007ffff42a9fbb in __libc_message () from /lib64/libc.so.6
#3  0x00007ffff42afb56 in malloc_printerr () from /lib64/libc.so.6
#4  0x00007ffff54d5135 in void std::_Destroy_aux<false>::__destroy<testing::internal::String*>(testing::internal::String*, testing::internal::String*) () from /usr/lib64/libopencv_ts.so.2.4
#5  0x00007ffff54d5168 in std::vector<testing::internal::String, std::allocator<testing::internal::String> >::~vector() ()
from /usr/lib64/libopencv_ts.so.2.4
#6  0x00007ffff426ec4f in __cxa_finalize () from /lib64/libc.so.6
#7  0x00007ffff54a6a33 in ?? () from /usr/lib64/libopencv_ts.so.2.4
#8  0x00007fffffffe110 in ?? ()
#9  0x00007ffff7de9ddf in _dl_fini () from /lib64/ld-linux-x86-64.so.2
Backtrace stopped: frame did not save the PC

Apparently my test had problems at the end of the test, at the time of object destruction. So I started to eliminate every statement until the problem vanished or no statements were left. The result:

#include "gtest/gtest.h"
TEST(DemoTest, FailsBadly) {
  ASSERT_EQ(1, 0);

And it still crashed! So the code under test wasn’t the culprit. Another change introduced previously was the addition of OpenCV libs to the linker call. An incompatibility between OpenCV and google test? A quick search spitted out posts from users experiencing the same problems, eventually leading to the entry in OpenCVs bug tracker: http://code.opencv.org/issues/1608 or http://code.opencv.org/issues/3225. The opencv_ts library which appeared in the stack trace, exports symbols that conflict with google test version we link against. Since we didn’t need opencv_ts library, the solution was to clean up our linker dependencies:




/usr/bin/c++ CMakeFiles/demo_tests.dir/DemoTests.cpp.o -o demo_tests -rdynamic ../gtest-1.7.0/libgtest_main.a -lopencv_calib3d -lopencv_contrib -lopencv_core -lopencv_features2d -lopencv_flann -lopencv_gpu -lopencv_highgui -lopencv_imgproc -lopencv_legacy -lopencv_ml -lopencv_nonfree -lopencv_objdetect -lopencv_photo -lopencv_stitching -lopencv_ts -lopencv_video -lopencv_videostab ../gtest-1.7.0/libgtest.a -lpthread -lopencv_calib3d -lopencv_contrib -lopencv_core -lopencv_features2d -lopencv_flann -lopencv_gpu -lopencv_highgui -lopencv_imgproc -lopencv_legacy -lopencv_ml -lopencv_nonfree -lopencv_objdetect -lopencv_photo -lopencv_stitching -lopencv_ts -lopencv_video -lopencv_videostab


find_package(OpenCV REQUIRED core highgui)


/usr/bin/c++ CMakeFiles/demo_tests.dir/DemoTests.cpp.o -o demo_tests -rdynamic ../gtest-1.7.0/libgtest_main.a -lopencv_highgui -lopencv_core ../gtest-1.7.0/libgtest.a -lpthread

Lessons learned:

Know what you really want to depend on and explicitly name it. Ignorance or trust in build tools’ black magic is a recipe for blog posts.

Prioritizing: order of tasks

This is an entry that extends the post on microprojects by two additional prioritizing strategies.

Strategy: Cover your ass

This is the strategy suggested by the previous post. After preparing a list of milestones and their estimates, you pick the next most problematic milestone and work on it. A list of tasks ordered by this strategy helps you to “fail fast”: in less than half of estimated time you will know, whether you will succeed or bust the budget – even when little is known about the concrete implementation or the esimates are off by some amount.

Strategy: Most value first

In lot of projects, this is the strategy used by customers. All features not absolutely necessary to achieve the goal are cut or declared optional. If you look at minesweeper: you can play it without the highscore, the timer, the modifiable field side or even probably without the random component (i.e. make 99 fields), but not without the mines. After you determined that your budget is too small, you know what the customer can live without and if you have the option to cut features, then this is probably the strategy for you.

Strategy: Most painful, when omitted

This is the strategy best applied before the pain is real. In contrast to other two strategies, it does contain hard to quantify criteria like:

  • Quality
  • Security
  • Performance

The cost to implement them is non-linear and not directly visible. The temptation is big to use time and money to create more profitable features instead. They can be prioritized by:

  • probability of occurence
  • damage in the case of occurence
  • implementation cost
  • growth of the above factors with time

This is a lot of work for a single task – most likely you will setup project wide guidelines and default scenarios that will be reviewed by recurring audits.

Object Calisthenics: Change the way you think

Some time ago I spoke with my colleague about skill sharpening and training the brain to come up with new solutions. He proposed a two hour session at the weekend implementing a small game using object calisthenics.


The rules are described in The ThoughtWorks Anthology book. Here is the list for quick reference.

  1. Use only one level of indentation per method.
  2. Don’t use the else keyword.
  3. Wrap all primitives and strings.
  4. Use only one dot per line.
  5. Don’t abbreviate.
  6. Keep all entities small.
  7. Don’use any classes with more than two instance variables.
  8. Use first-class collections.
  9. Don’t use any getters/setters/properties.

Most of the rules seemed simple enough. Rules 2 and 5 are standard in Softwareschneiderei, 1, 4, 6 and 8 are stricter versions of common sense, 3 is a tedious object wrapping. The rules I was anxious about were 7 and 9. To increase the learning effect, I added an extra rule to the list that is critical in real life programming:

  1.   Write tests for your code.

It doesn’t matter whether to write test first, test after or even test driven. Only then is the code “value added”.


The game was minesweeper. It contains a nice mix of algorithms, data structures and UI. I concentrated the efforts on the algorithmic part. My first step was to analyse and create the needed data structures.

  • The smallest unit is the cell.
  • A cell can be either hidden or revealed, have a mine or be empty.
  • The game field contains such cells in rows and columns.
  • The position of a cell in a field is defined by its coordinate that contains the x and y position.

To associate anything with coordinates the coordinates had to be comparable to each other. Rule 9 forbids exposure of internal state, so the Coordinate class got its equals() and hashCode(). Only the creator of the coordinate had the knowledge about the number of dimensions and the values of the positions. Even the tests had no access to the inner state and tested only those two methods.

Since the revealed flag concept and a mine flag concept had similar properties, I decided not to track cells but to track their flags. Through this architectural decision, I had a field with two flag containers, one for revealed cells and one for cells with mines. An additional benefit was that it was enough to put only the coordinate into the container to mark a cell as a mine.

The next step was to link the parts together and add some behaviour. Setting a mine, then revealing a cell and obtaining the number of mines also. Setting a mine and marking the cell as revealed is a simple task with the containers. Testing that the revealed cell contained the mine was more tricky. To achieve that, the reveal method got an additional parameter, a closure with a hasMine parameter.

public void reveal(final Coordinate coordinate, final CellContainerVisitor revealedCellsVisitor) {
    visit(coordinate, revealedCellsVisitor);

private void visit(final Coordinate coordinate, final CellContainerVisitor revealedCellsVisitor) {
    revealedCellsVisitor.visit(coordinate, hasMineAt(coordinate));

public void containsMines() {
    final CellContainer target = new CellContainer();

    final List<Coordinate> mineCells = new ArrayList<Coordinate>();
    target.reveal(someCoordinate(), (coordinate, hasMine) -> {
        if (hasMine.equals(new HasMine(true))) {

    assertThat(mineCells, hasSize(1));
    assertThat(mineCells, contains(someCoordinate()));

The next game rule consumed the rest of the session: calculating the number of mines in the neighborhood. The main obstacle was to compute the coordinate of the neighbour. To do this it is necessary to add an offset to a position in a coordinate without exposing its internal structure. In the end I reverted to using more closures.


To achieve my goal I had to reverse the order in which I normally develop business logic: Rule 9 seems to support top-down approach: The interfaces of domain objects are nearly completely dominated by the way they are used by their containers.

Most of the time in this two hour session was spent staring at the screen and to think how to write readable code and readable tests without exposing internal details of the objects. Time well spent.

Know Your Tools: Why Mockitos when() works

Some days ago, my colleague asked how Mockito can differentiate between a method invocation outside of an expectation and one inside. If you want to know it too, read on.

Some days ago, my colleague asked how Mockito can differentiate between a method invocation outside of an expectation and one inside. If you want to know it too, read on.

The difference

Typically a mocking framework follows a Record/Replay/Verify model. In the first phase the expectations are recorded, in the second the mocked methods are called by the code under test and finally the expectations are verified. Consider an example with EasyMock straight from their documentation:

mock = createMock(Collaborator.class);
mock.documentAdded("New Document");
classUnderTest.addDocument("New Document", new byte[0]);

Now, with Mockito the difference between the phases is not as clear as with EasyMock:

LinkedList mockedList = mock(LinkedList.class);

The invocation of get() is evaluated before the invocations of when() or println() so there is no way to change the phase before the call. There is also no way to tell whether the current expectation is the last to start the replay mode automatically. How does it work then? All necessary code is contained in the following classes: MockitoCore, MockHandlerImpl, OngoingStubbing and MockingProgressImpl with its wrapper ThreadSafeMockingProgress.


LinkedList mockedList = mock(LinkedList.class);

In the second line, a mock is created via the mock method. This call is delegated to MockitoCore, which initiates a creation of a proxy and a registration of MockHandlerImpl as the handler for its invocations.

The third line actually contains three steps. First the method to mock is invoked on the mock. Because MockHandlerImpl has been registered for all method calls on this proxy, it is now called. It keeps the current invocation, adds it to the list of all invocations recorded and creates the object to collect the expectations, the “OngoingStubbing”. The instance of OngoingStubbing is stored in an instance of the MockingProgressImpl. To keep the instance between the calls to the framework, a ThreadLocal member of singleton ThreadSafeMockingProgress is used. Since no mocked answer for the call to mock exists, a default result is returned. The second step is the invocation of when(), which returns the instance of OngoingStubbing previously deposited by MockHandlerImpl in MockingProgressImpl. OngoingStubbing implements the method then(), which is used as a means of recording the expected result in the third step. The result and the cached invocation are then saved together, ready to be retrieved. During this process, the invocation call is “consumed” and removed from the list of recorded invocations.



In the line five the method get() is called again. Since the result for it has been defined, MockHandlerImpl returns the retrieved result to the caller. The call is recorded and stored for for further use.



Verification also consists of multiple steps. The call to verify() marks the end of stubbing and sets the verification mode. In the following call to get() on the basis of set verification mode MockHandlerImpl is able to differentiate between the phases and passes the invocations recorded to the verification code.

Final thoughts

The developers of Mockito achieved much with simple constructs like singletons and shared state. The stuff behind the syntax sugar is sometimes even considered magic. I hope that, after reading this article, you no longer believe in magic but use your knowledge to create similar great frameworks.

Another point: Since Mockito uses ThreadLocal as storage for its state, is it possible to confuse it by using multiple threads? What do you think?

C/C++ pitfalls for Java developers

Java and C/C++ have concepts that are similar enough to get an inexperienced Java developer confused. Here I want to show you some mistakes I found or done myself.

Java and C/C++ have concepts that are similar enough to get an inexperienced Java developer confused. Here I want to show you some mistakes I found or done myself.

Type conversion rules

A well known and often used pattern is simultaneous assignment of an expression to a variable and its comparison with another value.

if((a = b) != c) {
  // do something

In both Java and C would this code would have the same behaviour. The problem arises when a parenthesis is misplaced, resulting in an assignment of a boolean expression to a:

if((a = b != c)) {
  // do something

Since a boolean expression can be converted to an integer and the assignment expression is contained in a parenthesis, the compiler may even not ensue a warning. For Java this code isn’t legal anymore while perfectly fine in C. The error strikes most hard when the result of the comparison, namely 0 or 1, is a valid value. A good example is a call to socket(), that may return 0 as a file descriptor for stdin. The probably simplest solution to this problem is separating the assignment from comparison – even at the cost of a temporary variable.

Memory management

The behaviour of standard containers is sometimes combined with incomplete/misunderstood behaviour of pointers. An example:

class A {}
class B
  void foo()
    std::vector<A*> theContainer;
    for(int i = 0; i < 100; i++) {
      theContainer.push_back(new A());

Every call to foo() would result in a memory leak due to not deleted A’s. When the vector is destructed, a destructor of each contained item is called. For pointers and other scalar types this is a no-op, resulting in missing call to the destructor of pointed to class. A solution to this problem could be the use of smart pointers wrapping the actual pointers or an explicit destruction of pointed to objects before the vector goes out of scope.

Deterministic destruction

Coming from language with automatic memory management there is some uncertainty when it comes to the order of destruction when multiple objects leave the scope. Consider this example:

void foo()
  std::lock_guard<std::mutex> lock(mutex);
  std::ifstream input ....

  //some operations


In this case the stream is destructed before the lock, guaranteeing that the stream is destructed before the execution reaches the destructor of the lock. This pattern is exploited by the RAII.

Exception handling

This is my personal favourite. Here is a little quiz: what is printed to the screen?

try {
  throw new SomeException();
} catch (SomeException& e) {
  std::cout << "first" << std::endl;
} catch (...) {
  std::cout << "second" << std::endl;

As some may already have guessed from the question: the answer is “second”. To make the code work, the reference in the catch block has to be replaced by the pointer. Another, and probably better alternative is to create the exception on the stack. The reason behind this mistake is that in java any thrown object is constructed with new. Explicit hints or experience are required to avoid such flawed exception handling.

Thoughts on Design

There is a book I am currently reading (and recommend): “The design of everyday things” by Donald A. Norman. The author describes common design errors in an easy readable way and shows or outlines the solutions for them. Despite not beeing a book about software engineering, it covers pretty well one of its greatest problems: interaction with a human. What are the points a software developer should consider when creating new or changing old features?

Natural mapping

Natural mappings are the clues that we can map to known patterns and instinctively use to interpret new unknown things. These mostly date back to prehistoric times and address the animal in us to catch our attention. To animals big things, moving things, things that are different because of color, shape or some other distinctive property are important, because they must decide based on them whether to flee or to attack. Natural mappings require near zero conscious processing power and make the user to pay attention to crucial information instantly. If you have two buttons “cancel order” and “submit order” and you want the user to click on submit, you better make the submit button big and flashy and the cancel button normal size and color it in standard grey (Have a look at the publish button and the preview button of wordpress).


Not all users read the manual (if any exists) before they use their programs. Not all users that read the manual, can understand it or find there the steps necessary to accomplish their task. To be still able to succeed, at every step the user asks himself the following questions:

  • What is already done?
  • What is my current position in the process?
  • What is to do now?
  • How far I am away from my goal?

Consider a user who only has 15 minutes and has to fill out an order form consisting of 15 pages. A user who does not even get the total number of pages stops frustrated after very few pages, because the process seems endless. Provide him with the page count and the current page and he will be able to plan ahead and esimate the needed time. If the mandatory fields are marked, the user will concentrate on them and progress faster, incresing the probability to complete the order in time.


Every time a user has done something, he will want to ensure that everything happened as he wanted. A mute system will inspire confusion and fear (Want to try it out? Use ed). A status message is a great signal for the user that he accomplished some of the steps on the way to his goal. Without the message “Order submitted successfully” the user won’t know whether the system accepted his input or just jumped to an another page. Additional confirmations like emails allow the user to receive status information with the additional benefit of being persistent unlike a web page in a browser.


Like any human the users input bogus data or trigger unwanted actions. In this cases the user should get appropriate feedback. When the previous steps are considered, the user will know what field is affected, why the input is not accepted and what the steps are to correct the situation. Sometimes the errors are logical and not syntactical, making them hard to impossile to detect by the system. There is no way to tell whether the user wanted to buy one or ten books. The layout of elements can be adapted to minimize the risk of accidental use: the “Close Application” button is better not to be placed near the “Save” button. When the mistake has been done, the user should be offered an edit, or at least a withdrawal option. Not all systems allow reversal of actions, and present the user a confirmation dialog with an important choice, producing unnecessary stress. There are ways to conter that problem.


This are simple points that can be taken into consideration when carefully designing a system. Even if they are simple, we tend to forget them because we have deadlines or understand the system on such level that we cannot even imagine what steps an inexperienced user can take and what hints he need.

Testing Java with Grails 2.2

We have some projects that consist of both java and groovy classes. If you don’t pay attention, you can have a nice WTF moment.

Let us look at the following fictive example. You want to test a static method of a java class “BlogAction”. This method should tell you whether a user can trigger a delete action depending on a configuration property.

The classes under test:

public class BlogAction {
    public static boolean isDeletePossible() {
        return ConfigurationOption.allowsDeletion();
class ConfigurationOption {
    static boolean allowsDeletion() {
        // code of the Option class omitted, here it always returns false
        return Option.isEnabled('userCanDelete');

In our test we mock the method of ConfigurationOption to return some special value and test for it:

class BlogActionTest {
    void postCanBeDeletedWhenOptionIsSet() {
        def option = mockFor(ConfigurationOption)
        option.demand.static.allowsDeletion(0..(Integer.MAX_VALUE-1)) { -> true }


As result, the test runner greets us with a nice message:

    at junit.framework.Assert.fail(Assert.java:48)
    at junit.framework.Assert.assertTrue(Assert.java:20)
    at junit.framework.Assert.assertTrue(Assert.java:27)
    at junit.framework.Assert$assertTrue.callStatic(Unknown Source)
    at BlogActionTest.postCanBeDeletedWhenOptionIsSet(BlogActionTest.groovy:21)

Why? There is not much code to mock, what is missing? An additional assert statement adds clarity:


The static method still returns false! The metaclass “magic” provided by mockFor() is not used by my java class. BlogAction simply ignores it and calls the allowsDeletion method directly. But there is a solution: we can mock the call to the “Option” class.

void postCanBeDeletedWhenOptionIsSet() {
    def option = mockFor(Option)
    option.demand.static.isEnabled(0..(Integer.MAX_VALUE-1)) { String propertyName -> true }


Lessons learned: The more happens behind the scenes, the more important becomes the context of execution.

Impressions from Java Forum Stuttgart 2013

Java Forum Stuttgart(JFS) is a yearly java focused conference primarily visited by developers. The conference lasts for a day, offering 45 minute long talks plus some time in between for discussions. This was my second visit and I am happy to tell you about my impressions.

vert.x: Polyglot – modular – Asynchron

Speaker: Eberhard Wolff from adesso AG

This was my first stop. The topic seemed interesting, because at Softwareschneiderei we are using a mix of different languages and frameworks for our projects. To learn about a new Framework was a nice thing. vert.x runs on a Java VM and can be written in a mixable variety of languages like Java, JavaScript or Groovy. The main points of the presentation were the examples in Java and JavaScript showing the asynchronous features and communication between different components. Judging by the function set and the questions asked, this seems to be a framework that provides java developers a smooth transition from the synchronous world to the event based asynchronous world. Compared to NodeJS, vert.x is currently a small project containing only a handful of modules.

Java 8 innovations

Speaker: Michael Wiedeking from MATHEMA Software GmbH

This one is somewhat special. After thousands of blog entries, presentations and whatever there is only a marginal chance to get fresh news about java features. The speaker did know this and spiced the presentation up with some jokes, while showing ever increasing complex code samples. Exactly what I hoped for: reading code and having fun.

Statical code analysis as a quality measurement?

Speaker: Dr. Karl-Heinz Wichert from iteratec GmbH

We are using grails in some of our projects. As any other highly dynamic language, grails suffers from its strength: weak type system. Without acceptance test support it is hard to verify whether a given code piece is correct or not. My hope was to hear something about new trends in statical analysis that allow me to detect simple errors faster, without firing up the system. Biggest mismatch between imagination and reality that day. The speaker presented the reasons why to use statical code analysys and its current shortcomings like the inability to verify that comments match the code commented or the inability to detect complex implementations of a simple algorithm. An interesting statement was that statical analysis fails if not every aspect is checked, the reason beeing the developer trying to optimize the code against the measured criteria while neglecting other aspects. From my point of view this is not a shortcoming of a statical analysis, but of the way the people use it. It is measurable that a maintainable product has proportionally more readable variable names than an unmaintainable one, but is not necessarily true that your product gets maintainable when you rename all your variables. All in one: the speaker managed to motivate me to look for holes in his argumentation and thus to actively think about the topic.

Enterprise portals with grails. Does it work?

Speakers: Tobias Kraft and Manuel Breitfeld from exentio GmbH

Like the previous presentation, this one attracted me because of the grails context. Additionally, because of the title, I was hoping for a nice description on pitfalls they encountered while building their portals. One part of the presentation was the description of the portal they built and the requirements it has to fullfill. Another part was a description of the grails platform. They use grails to deliver snippets for their portal that is organized as a collection of such snippets. Very valuable was the part about the problems one can encounter when using grails, where they honestly admitted that the migration from Grails 1.3.7 to 2.x did cost them some time. To detect regressions during platform upgrades they recommended to put extra effort in tests.

Car2Car systems – Java and Peer2Peer move into the car

Speaker: Adam Kovacs from msg-systems

After the impressive lunch break my brain waves almost reached zero. The program brochure missed any interesting titles for the next round so I went for the least common topic. The presentation turned out a lucky find. The speaker managed to keep the right level of detail, without diving too deep or scratching the surface. He described how Chord, an implementation of a distributed hash table, can be used to share locally relevant traffic data like traffic jams or accidents. To increase the stability and the security of the network he introduced the use of existing transmitting stations and certificate authorities.


Speaker: Dr Ralph Guderlei from eXXcellent Solutions

There were two reasons I wanted to visit this presentation. The first was: My colleague already showed me some features of this language. The second: the language is developed by the company that also develops the IntelliJ IDEA. Good IDE support is practically built in, isn’t it? The presentation covered syntax, lambdas, type inference extension methods and how kotlin handles null references. It looks like kotlin is going to become something like an improved java. I hope for the best.

Enterprise Integration Patterns

Speaker: Alexander Heusingfeld from First Point IT Solutions

Another Presentation that gets selected because of the least boring sounding title and another success. In the first minutes I expected an endless enumeration of common well known patterns. This was only true for the first minutes. The topic quickly shifted to asynchronous messaging and increasingly complex patterns to handle it. As two frameworks with similar range of functions he presented Apache Camel and Spring  Integration

Bottom line

The event was as always fun. Unfortunately it was not possible to visit more presentations due to their “parallel execution”. Have you been an JFS too and want to share your impressions about same or other presentations too? Post a comment!

Communication Through Code

In a previous post my colleague described our experiment on our ability to transfer the intention of the code by tests. The tests describe how the code behaves when called from the outside. Additional approach is to communicate through code.

To understand the code, at least the following two questions have to be answered:

  • How does the code work?
  • What is the reason behind the way the code is implemented?


As long as the code is readable, it is possible to deduce its meaning. Improving readability is a common technique to help the reader. This includes using descriptive names, reducing complexity or hiding implementation details until they are absolutely necessary to understand the problem.

On the other hand deducing the reason why exactly this implementation was chosen by somebody is an impossible task without the knowledge (or lack thereof) of all implementors combined. One of the missing parts are the assumptions. Our code is full of them. Consider the following example:

void print(char* text)
  printf("program says %s", text);

In this function the writer assumes that:

  • the text is a valid pointer
  • the text is zero terminated
  • this program can write to stdout, i.e. is a console app
  • the reader speaks english

Or something nastier:

void* allocateBuffer(size_t size)
  void* buffer = malloc(size);
  if (!buffer) {
    printf("expect a segmentation fault!");
  return buffer;

Here the writer assumes that malloc always returns either NULL or a pointer to dereferenceable memory. It is not always the case:

If size is zero, the return value depends on the particular library implementation (it may or may not be a null pointer), but the returned pointer shall not be dereferenced.

Assumptions not explicitly defined in the code lead sooner or later to hard to discover bugs.

Solution approaches

Comments are the quick and dirty way of writing down assumptions. They are easiest to read, but are never enforced and tend to diverge from the code with every edit made to it. However it is better to read “should never come here” and hear the alarm bells ringing than seeing nothing but whitespace.

Some of the assumptions can be documented and verified through tests, with varying level of detail. Unit tests will be most efficient on assumptions with little or no context, like verifying that only non-NULL-pointers are passed to a function. For more global assumptions integration or acceptance tests can be used. Together they ensure that no changes to the codebase break the assumptions made earlier. The drawback of unit tests is that they are locally decoupled from the code tested, forcing the reader to gather the information by searching for direct or indirect references to it.

When new code is written, assertions help to document how the API is meant to be used. Since they are executed not only during the test phase, they can capture wrong assumptions the authors made about the runtime environment. Writing down every possible assumption can quickly clutter the code with repeated statements like “assume pointer x is not NULL”, reducing readability and usefulness of this technique.


All of the shown approaches are not new. Each one has an aspect it excels at, so to get the most information out of the code they all have to be used. Their domains overlap partially, so it is possible to choose the approach depending on the situation, i.e. replacing assertions with unit tests for time critical code. One niche currently not filled by any of them is the description of global assumptions like the cultural background of the users.

Test your migrations

Do you trust your database migrations?

An evolving project that changes its persistent data structure can require a transformation of already existing content into the new form. To achieve this goal in our grails projects we use a grails database migration plugin. This plugin allows us to apply changesets to the database and keep track of its current state.

The syntax of the DSL for groovy database migrations is easy to read. This can trick you into the assumption that everything that looks good, compiles and runs without errors is OK. Of course it is not. Here is an example:

changeSet(author: 'vasili', id: 'copies messages to archive') {
  grailsChange {
    change {
                + "MESSAGES.DATE_SENT > to_date('2011-01-01 00:00', "
                + "'YYYY-MM-DD HH24:MI:SS')") { row ->
                  + "VALUES(${row.id}, ${row.content}, ${row.date_sent})")
    change {
                + "MESSAGES.DATE_SENT > to_date('2012-01-01 00:00', "
                + "'YYYY-MM-DD HH24:MI:SS')") { row ->
                + "VALUES(${row.id}, ${row.content}, ${row.date_sent})")

Here you see two change closures that differ only by the year in the SQL where clause. What do you think will happen with your database when this migration is applied? The answer is: only changes from the year 2012 will be found in the destination table. The assumption that when there is one change closure in the grailsChange block there can also be two changes in it is, while compilable and runnable, wrong. Loking at the documentation you will see that it shows only one change block in the example code. When you divide the migration into multiple parts, each of them working on their own change, everything will work as expected.

Currently there is no safety net like unit tests for database migrations. Every assumption you make must be tested manually with some dummy test data.