tdd/tdd.rst
author Madhusudan.C.S <madhusudancs@gmail.com>
Tue, 31 Aug 2010 20:14:18 +0530
changeset 114 83b3e357ed08
parent 112 0b01bb6ea6b8
child 115 7455326cac40
permissions -rw-r--r--
Manipulated the python gcd script to accommodate new way of testing.
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Fundamentals
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============
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Test Driven Development, abbreviated as TDD is a method of software
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development which banks on the idea of writing test cases that fail for the
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code that doesn't even exist yet. The actual code is written later to pass
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the test and then refactored.
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First "Test"
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============
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Writing a test is simple. Writing a failing test? It is much more simple.
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Let us consider a very simple program which returns the Greatest Common
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Divisor (GCD) of two numbers. Since the test cases for the code is written
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prior to the code itself, it is necessary to have a clear idea of the code
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units that our program will contain. Let us attempt to clearly define the
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code units in our case of a GCD program. Let our program contain one and
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only one function called gcd() which takes in two arguments as parameters.
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These arguments are the numbers for which GCD must be computed. The gcd()
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function returns a single value which is the GCD of the two arguments
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passed. So if we want to find out GCD of 44, 23, I will call my code unit
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as c = gcd(44, 23) where c will contain the GCD of those two numbers.
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Now we have defined our code units, how will we write tests? Before writing
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the test, a very fundamental question arises in our minds. How do tests
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look like? So let us answer this question first. Tests are nothing but a
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series of assertions which are either True or False depending on the
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expected behaviour of the code. We tell our tests whether our code unit
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asserts True or asserts False based on the expected behaviour of the code
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units. If we happen to run the tests now we are sure to get errors. Oh! But
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why? We don't even have the function gcd to call. The test code doesn't
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even compile! So what should we do now? So the idea is to first write the
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stubs for the code units before we start writing tests. This is necessary
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for two reasons. Firstly, by writing the stubs for the code units we will
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be able to correctly decide and fix on to the code units that we have
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planned to include in our program. We have a clear cut idea as to how our
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program is structured, how the tests must be written among other
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things. Secondly, the tests must at least compile and then fail! If the
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tests don't even compile, that doesn't mean the tests failed. It means
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it was a failure on the programmer's part. Let us define our stub::
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  def gcd(a, b):
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      pass
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This stub does nothing other than defining a new function called gcd
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which takes two parameters a and b for which the GCD must be
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calculated. The body of the function just contains Python's **pass**
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statement which means it does nothing, i.e. empty. We have our stub
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ready. One important thing we need to keep in mind when we adopt TDD
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methodology is that we need to have a clear set of results defined for
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our code units. To put it more clearly, for every given set of inputs
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as test case we must have, before hand, the exact outputs that are
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expected for those input test cases. If we don't have that we have
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failed in the first step of the TDD methodology itself. We must never
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run looking for outputs for our test cases after we have the code
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ready or even while writing tests. The expected outputs/behaviour must
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be in our hands before we start writing tests. Therefore let us define
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our test cases and the expected output for those inputs. Let one of
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our test cases be 48 and 64 as *a* and *b* respectively. For this test
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case we know that the GCD is 16. So that is the expected output. Let
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our second test case be 44 and 19 as *a* and *b* respectively. We know
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that their GCD is 1 by simple paper and pen calculation.
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Now we know what a test is? What are the ingredients required to write
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tests? So what else should we wait for? Let us write our first test!::
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  tc1 = gcd(48, 64)
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  if tc1 != 16:
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      print "Test failed for the case a=48 and b=64. Expected 16. Obtained %d instead." % tc1
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      exit(1)
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  tc2 = gcd(44, 19)
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  if tc2 != 1:
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      print "Test failed for the case a=44 and b=19. Expected 1. Obtained %d instead." % tc2
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      exit(1)
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  print "All tests passed!"
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Let us put all these in a file and call this file **gcd.py**::
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  def gcd(a, b):
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      pass
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  if __name__ == '__main__':
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      tc1 = gcd(48, 64)
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      if tc1 != 16:
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          print "Test failed for the case a=48 and b=64. Expected 16. Obtained %d instead." % tc1
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          exit(1)
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      tc2 = gcd(44, 19)
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      if tc2 != 1:
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          print "Test failed for the case a=44 and b=19. Expected 1. Obtained %d instead." % tc2
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          exit(1)
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      print "All tests passed!"
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Note that we have introduced a new semantic which uses two new magic names
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in Python *__name__* and *__main__*. This is a very common idiom used in
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Python. Every Python code in a file can be run in two ways: Either as an
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independent stand-alone script or as a Python module which can be imported
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by other Python scripts or modules. When the idiom::
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  if __name__ == '__main__':
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is used, the code within this if block is executed first when we run the
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Python file as a stand-alone script. In other words, when we run this
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python file as a stand-alone script the control of the program first starts
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from the code that is within this if block from which the control is
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transferred to other parts of the program or to other modules from
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here. This comes as an extremely handy feature especially when we want to
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test our modules individually. Now let us run our code as a stand-alone
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script.::
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  madhu@madhu:~/Desktop$ python gcd.py
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  Traceback (most recent call last):
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    File "gcd.py", line 7, in <module> print "Test failed for the case a=48 and b=64. Expected 16. Obtained %d instead." % tc1
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  TypeError: %d format: a number is required, not NoneType
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Now we have our tests, the test cases and the code unit stub at
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hand. We also have the failing test. So we know for sure that we have
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cleared the first check point of TDD where the tests have failed. The
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failing tests also give a green signal for us to go ahead to our next
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check point i.e. to write the actual code in our code unit and make
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the test pass. So let us write the code for the gcd function by
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removing the **pass** control statement which had just created a gcd
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function stub for us.
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Most of us have learnt in high school math classes that the best and
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the easiest known algorithm to compute the gcd of two numbers was
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given to us 2300 years ago by a greek mathematician named Euclid. So
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let us use the Euclid's algorithm to compute the gcd of two numbers a
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and b::
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  def gcd(a, b):
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      if a == 0:
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          return b
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      while b != 0:
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          if a > b:
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              a = a - b
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          else:
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              b = b - a
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      return a
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**Note**: If you are unaware of Euclidean algorithm to compute the gcd
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of two numbers please refer to it on wikipedia. It has a very detailed
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explanation of the algorithm and its proof of validity among other
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things.
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Now let us run our script which already has the tests written in it
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and see what happens::
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  madhu@madhu:/media/python/sttp/tdd$ python gcd.py
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  All tests passed!
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Success! We managed to pass all the tests. But wasn't that code simple
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enough? Indeed it was. If you take a closer look at the code you will
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soon realize that the chain of subtraction operations can be replaced
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by a modulo operation i.e. taking remainders of the division between
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the two numbers since they are equivalent operations. Also modulo
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operation is far better than chain of subtractions because you will
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reduce much faster using modulo operation than the subtraction. For
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example if let us take 25, 5 as a and b in our example. If we write
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down the steps of the algorithm written above we have the following:
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Step 1: a = 25 b = 5: Since both a and b are not 0 and b is greater
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than a: b = 25 - 5 = 20
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Step 2: Since b is still not 0 and b is greater than a: b = 20 - 5 =
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15
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Step 3: Since b is still not 0 and b is greater than a: b = 15 - 5 =
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10
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Step 4: Since b is still not 0 and b is greater than a: b = 10 - 5 = 5
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Step 5: Since b is still not 0 and b is equal to a: b = 5 - 5 = 0
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Step 6: Since b is 0 the gcd is a = 5 which is returned
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If we adopt the modulo operation instead of subtraction and follow the
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steps:
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Step 1: a = 25 b = 5: Since both a and b are not 0 and b is greater
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than a: b = 25 % 5 = 0
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Step 2: Since b is 0 the gcd is a = 5 which is returned
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Wow! That was overwhelmingly lesser number of steps! So now we are
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convinced that if we replace the subtraction operation with the modulo
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operation our code performs much better. But if we think carefully we
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know that the modulo of a and b is less than b irrespective of how
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large the value of a is, including the case where a is already less
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than b. So we can eliminate that extra conditional **if** statement by
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just swapping the result of the modulo operation to the position of b
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and b to the position of a. This ensures that a is always greater than
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b and if not the swapping combined with modulo operation takes care of
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it. To exemplify it, if a = 5 and b = 25 then by swapping and
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performing modulo we have a = b = 25 and b = a % b = 5 % 25 = 5 and
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hence we proceed. So let us replace our original code with this new
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improved code we have come up with simple observations::
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  def gcd(a, b):
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      while b != 0:
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          a, b = b, a % b
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      return a
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Executing our script again we will see that all the tests pass. One
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final improvement we can think of which is not necessary in terms of
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efficiency but is certainly good to do keeping in mind the readability
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is that we can use the concept of recursion for the same
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algorithm. Without going into much detail this is how the code looks
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if we use a recursive approach::
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  def gcd(a, b):
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      if b == 0:
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          return a
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      return gcd(b, a%b)
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Much shorter and sweeter! And it passes all the tests!
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109
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More realistic "Tests"
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======================
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112
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Now we have successfully completed writing our first test, writing the
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relevant code and ensured the tests passed. We also refactored our
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code to perform better. With the knowledge of all these and some
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concepts and semantics like __main__ magic names we learnt we have
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come a long way with respect to writing tests. But our thirst is still
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unquenched! We want to do more and more tests! Not just write better
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code but also better tests! So let us keep building upon what we have
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learnt so far.
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Let us  start writing tests  for more realistic test  cases. Generally
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tests  are predetermined  as said  above, if  not the  code  itself is
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flawed. The predetermined tests are stored along with the test code in
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some  persistent way  like  in a  database,  a text  file,  a file  of
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specific format  like XML or  in other way.  Let us continue  with our
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example of  GCD function. We  will keep all  our test cases in  a text
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file,  which  is  indeed   persistent.  Let  our  file  have  multiple
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lines. Each line in this file  corresponds to a single test case. Each
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line consists of  three coloumns: first two coloumns  are the integers
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for  which the GCD  has to  be computed  and the  last coloumn  is the
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expected GCD  to the  preceding two  numbers. So how  do we  write our
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tests  to use  these  test cases?  Pretty  simple, let  us review  the
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machinery required first.
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  1. File reading: We already have learnt this in our chapters on
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     Basic Python.
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  2. Parsing read data from file: This just involves a for loop over
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     the data since we know that file contains lines which are
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     equivalent to file records and hence parse the data line by line
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     as strings as we iterate Over it and convert it to required data
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     type.
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Since we already have all the machinery required, let us proceed writing
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our test cases.
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%%%%%%%%% Much Later %%%%%%%%%%%%%%%%%
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The idea of placing the tests with in the Python scripts and to
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execute them when the script is run as a stand-alone script works well
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as long as we have our code in a single Python file or the tests for
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each script can be run separately. But in a more realistic software
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development scenario, often this is not the case. The code is spread
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around multiple Python scripts, each Python script also being called
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as a Python module, and across several Python packages. In such a
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scenario we may want to do more.