Unit Testing & Test-Driven Development
This chapter has not had any significant translation yet. Should introduce and compare the various common test systems.
One of the important recent realizations is the dramatic value of unit testing.
This is the process of building integrated tests into all the code that you create, and running those tests every time you do a build. It's as if you are extending the compiler, telling it more about what your program is supposed to do. That way, the build process can check for more than just syntax errors, since you teach it how to check for semantic errors as well.
C-style programming languages, and C++ in particular, have typically valued performance over programming safety. The reason that developing programs in Java is so much faster than in C++ (roughly twice as fast, by most accounts) is because of Java's safety net: features like better type checking, enforced exceptions and garbage collection. By integrating unit testing into your build process, you are extending this safety net, and the result is that you can develop faster. You can also be bolder in the changes that you make, and more easily refactor your code when you discover design or implementation flaws, and in general produce a better product, faster.
Unit testing is not generally considered a design pattern; in fact, it might be considered a "development pattern," but perhaps there are enough "pattern" phrases in the world already. Its effect on development is so significant that it will be used throughout this book, and thus will be introduced here.
My own experience with unit testing began when I realized that every program in a book must be automatically extracted and organized into a source tree, along with appropriate makefiles (or some equivalent technology) so that you could just type make to build the whole tree. The effect of this process on the code quality of the book was so immediate and dramatic that it soon became (in my mind) a requisite for any programming book-how can you trust code that you didn't compile? I also discovered that if I wanted to make sweeping changes, I could do so using search-and-replace throughout the book, and also bashing the code around at will. I knew that if I introduced a flaw, the code extractor and the makefiles would flush it out.
As programs became more complex, however, I also found that there was a serious hole in my system. Being able to successfully compile programs is clearly an important first step, and for a published book it seemed a fairly revolutionary one-usually due to the pressures of publishing, it's quite typical to randomly open a programming book and discover a coding flaw. However, I kept getting messages from readers reporting semantic problems in my code (in Thinking in Java). These problems could only be discovered by running the code. Naturally, I understood this and had taken some early faltering steps towards implementing a system that would perform automatic execution tests, but I had succumbed to the pressures of publishing, all the while knowing that there was definitely something wrong with my process and that it would come back to bite me in the form of embarrassing bug reports (in the open source world, embarrassment is one of the prime motivating factors towards increasing the quality of one's code!).
The other problem was that I was lacking a structure for the testing system. Eventually, I started hearing about unit testing and Junit , which provided a basis for a testing structure. However, even though JUnit is intended to make the creation of test code easy, I wanted to see if I could make it even easier, applying the Extreme Programming principle of "do the simplest thing that could possibly work" as a starting point, and then evolving the system as usage demands (In addition, I wanted to try to reduce the amount of test code, in an attempt to fit more functionality in less code for screen presentations). This chapter is the result.
Write Tests First
As I mentioned, one of the problems that I encountered-that most people encounter, it turns out-was submitting to the pressures of publishing and as a result letting tests fall by the wayside. This is easy to do if you forge ahead and write your program code because there's a little voice that tells you that, after all, you've got it working now, and wouldn't it be more interesting/useful/expedient to just go on and write that other part (we can always go back and write the tests later). As a result, the tests take on less importance, as they often do in a development project.
The answer to this problem, which I first found described in Extreme Programming Explained, is to write the tests before you write the code. This may seem to artificially force testing to the forefront of the development process, but what it actually does is to give testing enough additional value to make it essential. If you write the tests first, you:
- Describe what the code is supposed to do, not with some external graphical tool but with code that actually lays the specification down in concrete, verifiable terms.
- Provide an example of how the code should be used; again, this is a working, tested example, normally showing all the important method calls, rather than just an academic description of a library.
- Provide a way to verify when the code is finished (when all the tests run correctly).
Thus, if you write the tests first then testing becomes a development tool, not just a verification step that can be skipped if you happen to feel comfortable about the code that you just wrote (a comfort, I have found, that is usually wrong).
You can find convincing arguments in Extreme Programming Explained, as "write tests first" is a fundamental principle of XP. If you aren't convinced you need to adopt any of the changes suggested by XP, note that according to Software Engineering Institute (SEI) studies, nearly 70% of software organizations are stuck in the first two levels of SEI's scale of sophistication: chaos, and slightly better than chaos. If you change nothing else, add automated testing.
Simple Python Testing
Sanity check for a quick test of the programs in this book, and to append the output of each program (as a string) to its listing:
# SanityCheck.py #! /usr/bin/env python import string, glob, os # Do not include the following in the automatic # tests: exclude = ("SanityCheck.py", "BoxObserver.py",) def visitor(arg, dirname, names): dir = os.getcwd() os.chdir(dirname) try: pyprogs = [p for p in glob.glob('*.py') if p not in exclude ] if not pyprogs: return print('[' + os.getcwd() + ']') for program in pyprogs: print('\t', program) os.system("python %s > tmp" % program) file = open(program).read() output = open('tmp').read() # Append output if it's not already there: if file.find("output = '''") == -1 and \ len(output) > 0: divider = '#' * 50 + '\n' file = file.replace('#' + ':~', '#<hr>\n') file += "output = '''\n" + \ open('tmp').read() + "'''\n" open(program,'w').write(file) finally: os.chdir(dir) if __name__ == "__main__": os.path.walk('.', visitor, None)
Just run this from the root directory of the code listings for the book; it will descend into each subdirectory and run the program there. An easy way to check things is to redirect standard output to a file, then if there are any errors they will be the only thing that appears at the console during program execution.
A Very Simple Framework
As mentioned, a primary goal of this code is to make the writing of unit testing code very simple, even simpler than with JUnit. As further needs are discovered during the use of this system, then that functionality can be added, but to start with the framework will just provide a way to easily create and run tests, and report failure if something breaks (success will produce no results other than normal output that may occur during the running of the test). My intended use of this framework is in makefiles, and make aborts if there is a non- zero return value from the execution of a command. The build process will consist of compilation of the programs and execution of unit tests, and if make gets all the way through successfully then the system will be validated, otherwise it will abort at the place of failure. The error messages will report the test that failed but not much else, so that you can provide whatever granularity that you need by writing as many tests as you want, each one covering as much or as little as you find necessary.
In some sense, this framework provides an alternative place for all those "print" statements I've written and later erased over the years.
To create a set of tests, you start by making a static inner class inside the class you wish to test (your test code may also test other classes; it's up to you). This test code is distinguished by inheriting from UnitTest:
# UnitTesting/UnitTest.py # The basic unit testing class class UnitTest: testID = "" static List errors = ArrayList() # Override cleanup() if test object # creation allocates non-memory # resources that must be cleaned up: def cleanup(self): # Verify the truth of a condition: def affirm(boolean condition): if(!condition) errors.add("failed: " + testID)
The only testing method [[ So far ]] is affirm( ) , which is protected so that it can be used from the inheriting class. All this method does is verify that something is true. If not, it adds an error to the list, reporting that the current test (established by the static testID, which is set by the test-running program that you shall see shortly) has failed. Although this is not a lot of information-you might also wish to have the line number, which could be extracted from an exception-it may be enough for most situations.
Unlike JUnit (which uses setUp( ) and tearDown( ) methods), test objects will be built using ordinary Python construction. You define the test objects by creating them as ordinary class members of the test class, and a new test class object will be created for each test method (thus preventing any problems that might occur from side effects between tests). Occasionally, the creation of a test object will allocate non-memory resources, in which case you must override cleanup( ) to release those resources.
Writing tests becomes very simple. Here's an example that creates the necessary static inner class and performs trivial tests:
# UnitTesting/TestDemo.py # Creating a test class TestDemo: objCounter = 0 id = ++objCounter def TestDemo(String s): print(s + ": count = " + id) def close(self): print("Cleaning up: " + id) def someCondition(self): return True class Test(UnitTest): TestDemo test1 = TestDemo("test1") TestDemo test2 = TestDemo("test2") def cleanup(self): test2.close() test1.close() def testA(self): print("TestDemo.testA") affirm(test1.someCondition()) def testB(self): print("TestDemo.testB") affirm(test2.someCondition()) affirm(TestDemo.objCounter != 0) # Causes the build to halt: #! def test3(): affirm(0)
The test3( ) method is commented out because, as you'll see, it causes the automatic build of this book's source-code tree to stop.
You can name your inner class anything you'd like; the only important factor is that it extends UnitTest. You can also include any necessary support code in other methods. Only public methods that take no arguments and return void will be treated as tests (the names of these methods are also not constrained).
The above test class creates two instances of TestDemo. The TestDemo constructor prints something, so that we can see it being called. You could also define a default constructor (the only kind that is used by the test framework), although none is necessary here. The TestDemo class has a close( ) method which suggests it is used as part of object cleanup, so this is called in the overridden cleanup( ) method in Test.
The testing methods use the affirm( ) method to validate expressions, and if there is a failure the information is stored and printed after all the tests are run. Of course, the affirm( ) arguments are usually more complicated than this; you'll see more examples throughout the rest of this book.
Notice that in testB( ), the private field objCounter is accessible to the testing code-this is because Test has the permissions of an inner class.
You can see that writing test code requires very little extra effort, and no knowledge other than that used for writing ordinary classes.
To run the tests, you use RunUnitTests.py (which will be introduced shortly). The command for the above code looks like this:
java com.bruceeckel.test.RunUnitTests TestDemo
It produces the following output:
test1: count = 1 test2: count = 2 TestDemo.testA Cleaning up: 2 Cleaning up: 1 test1: count = 3 test2: count = 4 TestDemo.testB Cleaning up: 4 Cleaning up: 3
All the output is noise as far as the success or failure of the unit testing is concerned. Only if one or more of the unit tests fail does the program returns a non-zero value to terminate the make process after the error messages are produced. Thus, you can choose to produce output or not, as it suits your needs, and the test class becomes a good place to put any printing code you might need- if you do this, you tend to keep such code around rather than putting it in and stripping it out as is typically done with tracing code.
If you need to add a test to a class derived from one that already has a test class, it's no problem, as you can see here:
# UnitTesting/TestDemo2.py # Inheriting from a class that # already has a test is no problem. class TestDemo2(TestDemo): def __init__(self, s): TestDemo.__init__(s) # You can even use the same name # as the test class in the base class: class Test(UnitTest): def testA(self): print("TestDemo2.testA") affirm(1 + 1 == 2) def testB(self): print("TestDemo2.testB") affirm(2 * 2 == 4)
Even the name of the inner class can be the same. In the above code, all the assertions are always true so the tests will never fail.
White-Box & Black-Box Tests
The unit test examples so far are what are traditionally called white-box tests. This means that the test code has complete access to the internals of the class that's being tested (so it might be more appropriately called "transparent box" testing). White-box testing happens automatically when you make the unit test class as an inner class of the class being tested, since inner classes automatically have access to all their outer class elements, even those that are private.
A possibly more common form of testing is black-box testing, which refers to treating the class under test as an impenetrable box. You can't see the internals; you can only access the public portions of the class. Thus, black-box testing corresponds more closely to functional testing, to verify the methods that the client programmer is going to use. In addition, black-box testing provides a minimal instruction sheet to the client programmer - in the absence of all other documentation, the black-box tests at least demonstrate how to make basic calls to the public class methods.
To perform black-box tests using the unit-testing framework presented in this book, all you need to do is create your test class as a global class instead of an inner class. All the other rules are the same (for example, the unit test class must be public, and derived from UnitTest).
There's one other caveat, which will also provide a little review of Java packages. If you want to be completely rigorous, you must put your black-box test class in a separate directory than the class it tests, otherwise it will have package access to the elements of the class being tested. That is, you'll be able to access protected and friendly elements of the class being tested. Here's an example:
# UnitTesting/Testable.py class Testable: def f1(): pass def f2(self): pass # "Friendly": package access def f3(self): pass # Also package access def f4(self): pass
Normally, the only method that should be directly accessible to the client programmer is f4( ). However, if you put your black-box test in the same directory, it automatically becomes part of the same package (in this case, the default package since none is specified) and then has inappropriate access:
# UnitTesting/TooMuchAccess.py class TooMuchAccess(UnitTest): Testable tst = Testable() def test1(self): tst.f2() # Oops! tst.f3() # Oops! tst.f4() # OK
You can solve the problem by moving TooMuchAccess.py into its own subdirectory, thereby putting it in its own default package (thus a different package from Testable.py). Of course, when you do this, then Testable must be in its own package, so that it can be imported (note that it is also possible to import a "package-less" class by giving the class name in the import statement and ensuring that the class is in your CLASSPATH):
# UnitTesting/testable/Testable.py package c02.testable class Testable: def f1(): pass def f2(self): # "Friendly": package access def f3(self): # Also package access def f4(self):
Here's the black-box test in its own package, showing how only public methods may be called:
# UnitTesting/BlackBoxTest.py class BlackBoxTest(UnitTest): Testable tst = Testable() def test1(self): #! tst.f2() # Nope! #! tst.f3() # Nope! tst.f4() # Only public methods available
Note that the above program is indeed very similar to the one that the client programmer would write to use your class, including the imports and available methods. So it does make a good programming example. Of course, it's easier from a coding standpoint to just make an inner class, and unless you're ardent about the need for specific black-box testing you may just want to go ahead and use the inner classes (with the knowledge that if you need to you can later extract the inner classes into separate black-box test classes, without too much effort).
The program that runs the tests makes significant use of reflection so that writing the tests can be simple for the client programmer:
# UnitTesting/RunUnitTests.py # Discovering the unit test # class and running each test. class RunUnitTests: def require(requirement, errmsg): if(!requirement): print(errmsg) sys.exit() def main(self, args): require(args.length == 1, "Usage: RunUnitTests qualified-class") try: Class c = Class.forName(args) # Only finds the inner classes # declared in the current class: Class classes = c.getDeclaredClasses() Class ut = null for(int j = 0 j < classes.length j++): # Skip inner classes that are # not derived from UnitTest: if(!UnitTest.class. isAssignableFrom(classes[j])) continue ut = classes[j] break # Finds the first test class only # If it found an inner class, # that class must be static: if(ut != null) require( Modifier.isStatic(ut.getModifiers()), "inner UnitTest class must be static") # If it couldn't find the inner class, # maybe it's a regular class (for black- # box testing: if(ut == null) if(UnitTest.class.isAssignableFrom(c)) ut = c require(ut != null, "No UnitTest class found") require( Modifier.isPublic(ut.getModifiers()), "UnitTest class must be public") Method methods = ut.getDeclaredMethods() for(int k = 0 k < methods.length k++): Method m = methods[k] # Ignore overridden UnitTest methods: if(m.getName().equals("cleanup")) continue # Only public methods with no # arguments and void return # types will be used as test code: if(m.getParameterTypes().length == 0 && m.getReturnType() == void.class && Modifier.isPublic(m.getModifiers())): # The name of the test is # used in error messages: UnitTest.testID = m.getName() # A instance of the # test object is created and # cleaned up for each test: Object test = ut.newInstance() m.invoke(test, Object) ((UnitTest)test).cleanup() except e: e.printStackTrace(System.err) # Any exception will return a nonzero # value to the console, so that # 'make' will abort: System.err.println("Aborting make") System.exit(1) # After all tests in this class are run, # display any results. If there were errors, # abort 'make' by returning a nonzero value. if(UnitTest.errors.size() != 0): it = UnitTest.errors.iterator() while(it.hasNext()): print(it.next()) sys.exit(1)
Automatically Executing Tests
- Install this book's source code tree and ensure that you have a make utility installed on your system (Gnu make is freely available on the internet at various locations). In TestDemo.py, un-comment test3( ), then type make and observe the results.
- Modify TestDemo.py by adding a new test that throws an exception. Type make and observe the results.
- Modify your solutions to the exercises in Chapter 1 by adding unit tests. Write makefiles that incorporate the unit tests.
|||I had originally called this assert(), but that word became reserved in JDK 1.4 when assertions were added to the language.|