JUnit test and Project 3 simulation

1. JUnittest and Project 3 simulation

2. Midterm Exam Wednesday, March 18, 2009 • Content: Week 1 to Week 9 • Guideline: posted on D2L. • Format: • Multiple choices • Simple problem solving questions • Writing code

3. JUnit • The testing problems • The framework of JUnit • A case study Acknowledgement: using some materials from JUNIT tutorial by Hong Qing Yu (www.cs.le.ac.uk/people/hqy1)

4. The Testing Problems Should write Do programmers few Why? I am so busy It is difficult

5. The Testing Problems Programmers need such kind of tool: “Writing a few lines of code, then a test that should run, or even better, to write a test that won't run, then write the code that will make it run.” JUnit is that kind of tool!

6. JUnit • The testing problems • The framework of JUnit • A case study

7. The Framework of JUnit

8. JUnit • The testing problems • The framework of JUnit • A case study

9. A Case Study • Lab3Queue: • enQueue method • deQueue method

10. Include junit library in eclipse

11. How to Write A TestCase using Junit (available in Eclipse 3.1 or later) • Step 1:Create a JUNIT test case (File -> New -> Junit Test Case

12. Create a test case

13. Create a test case import junit.framework.*; public class Lab3QueueTest { public void setUp() throws Exception { } public void tearDown() throws Exception { } }

14. Create a test case import junit.framework.*; public class Lab3QueueTest extends TestCase { Lab3Queue testQueue; intqueueSize; public void setUp() throws Exception { testQueue = new Lab3Queue(); queueSize = testQueue.getSize(); } public void tearDown() throws Exception { } }

15. Create a test case • For each method that you are going to test: • Write a corresponding test method named: test<method name> in the test case

16. Create a test case public void testenQueue() { intnewItem = 1; queueSize = testQueue.getSize(); testQueue.enQueue(newItem); Assert.assertEquals(queueSize+1, testQueue.getSize()); intactualItem = ((Integer) testQueue.getLastNode()).intValue(); Assert.assertEquals(newItem, actualItem); }

17. Assert • assertEquals(expected, actual) • assertEquals(message, expected, actual) • assertEquals(expected, actual, delta) • assertEquals(message, expected, actual, delta) • assertFalse(condition) • assertFalse(message, condition) • Assert(Not)Null(object) • Assert(Not)Null(message, object) • Assert(Not)Same(expected, actual) • Assert(Not)Same(message, expected, actual) • assertTrue(condition) • assertTrue(message, condition)

18. Structure • setUp() Storing the fixture's objects in instance variables of your TestCase subclass and initialize them by overriding the setUp method • tearDown() Releasing the fixture’s

19. Writing a test suite Step 2: Create a test suite by choosing

20. Writing a test suite

21. Writing a test suite import junit.framework.Test; import junit.framework.TestSuite; public class AllTests { public static Test suite() { TestSuite suite = new TestSuite("Test for AiportSimulation"); //$JUnit-BEGIN$ suite.addTestSuite(Lab3QueueTest.class); //$JUnit-END$ return suite; } }

22. Running a test AllTests -> choose Run -> Run As -> Junit Test

23. Running a test

24. Design Test Cases • The real world scenarios • The number boundaries

25. Tips Testcases must extend TestCase All ‘test’ methods must include at least one call to an assert method or to fail: assertEquals (String message, ...) assertNotNull (String message, Object obj) assertNull (String message, Object obj) assertSame (String message, Obj exp, Obj actual) assertTrue (String message, boolean condition) fail (String message) Remove System.out.println after test cases are working and rely on Junit assert methods to determine success/failure.

26. Dynamic Run Since JUnit 2.0 there is an even simpler dynamic way. You only pass the class with the tests to a TestSuite and it extracts the test methods automatically. suite.addTestSuite(Lab3QueueTest.class);

27. Project 3 - Algorithm

28. Recursion

29. Recursive Thinking • Recursion is a problem-solving approach that can be used to generate simple solutions to certain kinds of problems that would be difficult to solve in other ways • Recursion splits a problem into one or more simpler versions of itself

30. Recursive Thinking • Recursion is a problem-solving approach that can be used to generate simple solutions to certain kinds of problems that would be difficult to solve in other ways • Recursion splits a problem into one or more simpler versions of itself

31. Steps to Design a Recursive Algorithm • Step 1: • There must be at least one case (the base case), for a small value of n, that can be solved directly • Step 2: • A problem of a given size n can be split into one or more smaller versions of the same problem (recursive case) • Step 3: • Recognize the base case and provide a solution to it • Step 4: • Devise a strategy to split the problem into smaller versions of itself while making progress toward the base case • Step 5: • Combine the solutions of the smaller problems in such a way as to solve the larger problem Chapter 7: Recursion

32. Steps to Design a Recursive Algorithm • Step 1: • There must be at least one case (the base case), for a small value of n, that can be solved directly • Step 2: • A problem of a given size n can be split into one or more smaller versions of the same problem (recursive case) • Step 3: • Recognize the base case and provide a solution to it • Step 4: • Devise a strategy to split the problem into smaller versions of itself while making progress toward the base case • Step 5: • Combine the solutions of the smaller problems in such a way as to solve the larger problem Chapter 7: Recursion

33. Steps to Design a Recursive Algorithm • Step 1: • There must be at least one case (the base case), for a small value of n, that can be solved directly • Step 2: • A problem of a given size n can be split into one or more smaller versions of the same problem (recursive case) • Step 3: • Recognize the base case and provide a solution to it • Step 4: • Devise a strategy to split the problem into smaller versions of itself while making progress toward the base case • Step 5: • Combine the solutions of the smaller problems in such a way as to solve the larger problem Chapter 7: Recursion

34. Steps to Design a Recursive Algorithm • Step 1: • There must be at least one case (the base case), for a small value of n, that can be solved directly • Step 2: • A problem of a given size n can be split into one or more smaller versions of the same problem (recursive case) • Step 3: • Recognize the base case and provide a solution to it • Step 4: • Devise a strategy to split the problem into smaller versions of itself while making progress toward the base case • Step 5: • Combine the solutions of the smaller problems in such a way as to solve the larger problem Chapter 7: Recursion

35. Steps to Design a Recursive Algorithm • Step 1: • There must be at least one case (the base case), for a small value of n, that can be solved directly • Step 2: • A problem of a given size n can be split into one or more smaller versions of the same problem (recursive case) • Step 3: • Recognize the base case and provide a solution to it • Step 4: • Devise a strategy to split the problem into smaller versions of itself while making progress toward the base case • Step 5: • Combine the solutions of the smaller problems in such a way as to solve the larger problem Chapter 7: Recursion

36. Proving that a Recursive Method is Correct • Proof by induction • Prove the theorem is true for the base case • Show that if the theorem is assumed true for n, then it must be true for n+1 • Recursive proof is similar to induction • Verify the base case is recognized and solved correctly • Verify that each recursive case makes progress towards the base case • Verify that if all smaller problems are solved correctly, then the original problem is also solved correctly Chapter 7: Recursion

37. Recursive Definitions of Mathematical Formulas • Mathematicians often use recursive definitions of formulas that lead very naturally to recursive algorithms • Examples include: • Factorial • Powers • Greatest common divisor • If a recursive function never reaches its base case, a stack overflow error occurs Chapter 7: Recursion

38. Recursion Versus Iteration • There are similarities between recursion and iteration • In iteration, a loop repetition condition determines whether to repeat the loop body or exit from the loop • In recursion, the condition usually tests for a base case • You can always write an iterative solution to a problem that is solvable by recursion • Recursive code may be simpler than an iterative algorithm and thus easier to write, read, and debug Chapter 7: Recursion

39. Efficiency of Recursion • Recursive methods often have slower execution times when compared to their iterative counterparts • The overhead for loop repetition is smaller than the overhead for a method call and return • If it is easier to conceptualize an algorithm using recursion, then you should code it as a recursive method • The reduction in efficiency does not outweigh the advantage of readable code that is easy to debug Chapter 7: Recursion

40. Efficiency of Recursion (continued) Inefficient Efficient

41. Recursive Array Search • Searching an array can be accomplished using recursion • Simplest way to search is a linear search • Examine one element at a time starting with the first element and ending with the last • Base case for recursive search is an empty array • Result is negative one • Another base case would be when the array element being examined matches the target • Recursive step is to search the rest of the array, excluding the element just examined Chapter 7: Recursion

42. Algorithm for Recursive Linear Array Search Chapter 7: Recursion

43. Design of a Binary Search Algorithm • Binary search can be performed only on an array that has been sorted • Stop cases • The array is empty • Element being examined matches the target • Checks the middle element for a match with the target • Throw away the half of the array that the target cannot lie within Chapter 7: Recursion

44. Design of a Binary Search Algorithm (continued) Chapter 7: Recursion

45. Efficiency of Binary Search and the Comparable Interface • At each recursive call we eliminate half the array elements from consideration • O(log2 n) • Classes that implement the Comparable interface must define a compareTo method that enables its objects to be compared in a standard way • CompareTo allows one to define the ordering of elements for their own classes Chapter 7: Recursion

46. Method Arrays.binarySearch • Java API class Arrays contains a binarySearch method • Can be called with sorted arrays of primitive types or with sorted arrays of objects • If the objects in the array are not mutually comparable or if the array is not sorted, the results are undefined • If there are multiple copies of the target value in the array, there is no guarantee which one will be found • Throws ClassCastException if the target is not comparable to the array elements Chapter 7: Recursion

47. Method Arrays.binarySearch (continued) Chapter 7: Recursion

48. Recursive Data Structures • Computer scientists often encounter data structures that are defined recursively • Trees (Chapter 8) are defined recursively • Linked list can be described as a recursive data structure • Recursive methods provide a very natural mechanism for processing recursive data structures • The first language developed for artificial intelligence research was a recursive language called LISP Chapter 7: Recursion

49. Recursive Definition of a Linked List • A non-empty linked list is a collection of nodes such that each node references another linked list consisting of the nodes that follow it in the list • The last node references an empty list • A linked list is empty, or it contains a node, called the list head, that stores data and a reference to a linked list Chapter 7: Recursion

50. Problem Solving with Recursion • Will look at two problems • Towers of Hanoi • Counting cells in a blob Chapter 7: Recursion