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Unit Testing: Strategies and Methods

Learn the role of unit testing, testing strategies, black box and white box methods, limits of testing, and various coverage methods. Explore example programs and understand mutation testing.

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Unit Testing: Strategies and Methods

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  1. Unit Testing CSSE 376, Software Quality Assurance Rose-Hulman Institute of Technology March 27, 2007

  2. Outline • Role of unit testing • Testing strategies • Black box methods • White box methods

  3. Role of Unit Testing • Assure minimum quality of units before integration into system • Focus attention on relatively small units • Marks end of development step

  4. Testing versus Debugging

  5. Limits of Testing • Testing can never demonstrate the absence of errors • Exhaustive testing is infeasible • Testing may be done imperfectly

  6. Strategies for Unit Testing • Black box • use only specification of program • test implementation against its specification • White box • use structure or other properties of a program to generate tests

  7. Black Box Methods • Equivalence partitioning • Boundary value analysis

  8. White Box Methods

  9. White Box Methods • Coverage • statement • branch • path • Cyclomatic • Dataflow • Mutation • Error Based

  10. Coverage Methods • Statement • execute each statement • Branch • execute each branch • Path • execute each path

  11. Statement Coverage • Execute each statement in the program • Considered minimum criterion for most unit testing • May be difficult to achieve for error cases

  12. Example Program 1: if (a < 0) { 2: return 0 } 3: r = 0; 4: c = a; 5: while (c > 0) { 6: r = r + b; 7: c = c - 1; } 8: return r;

  13. Statement Tests a = 3, b = 4 executes 1, 3, 4, 5, 6, 7, 5, 6, 7, 5, 6, 7, 5, 8 a = -3, b = 2 executes 1, 2

  14. Branch Coverage • Execute each branch of the program at least once • Differs from statement coverage only for "if" statements without "else"s and case statements without default cases.

  15. Dataflow Testing • More than branch coverage, but less than path coverage • Uses information about references to variables to select paths

  16. Definitions and Uses • Defining node • input statement • lhs of assignment • Usage node • output statement • rhs of assignment • control statement

  17. Suspicious Paths • Variable is defined (set to a new value) but never referenced • Variable is referenced but never defined • Variable is defined twice before it is used

  18. DU Paths • Definition-use (du) path wrt V: a path containing a defining node for V at the beginning a usage node for V at the end, and no def's or use's in between • DU testing: execute each du-path of each variable

  19. Example Program 1: if (a < 0) { 2: return 0 } 3: r = 0; 4: c = a; 5: while (c > 0) { 6: r = r + b; 7: c = c - 1; } 8: return r;

  20. Example DU Paths(corrected 3/28/07) Def (c) = {4, 7} Use (c) = {5, 7} Def (r) = {3, 6} Use (r) = {6, 8} du-paths for c: 4 - 5, 7 – 5 du-paths for r: 3 - 4 - 5 - 6, 6 - 7 - 5 - 6, 3 - 4 - 5 - 8,6 - 7 - 5 - 8

  21. Test Cases for DU Paths (corrected 3/28/07) a = 2 1 - 3 - 4 - 5 - 6 - 7 - 5 - 6 - 7 - 5 - 8 Covers du-paths: 4 - 5, 7 - 5, 3 - 4 - 5 - 6, 6 - 7 - 5 - 6

  22. Cartoon of the Day

  23. Mutation Testing(As Applied to White-Box) • Path testing exercises the control of a program, not the computations along the paths • Most programs under test are "almost" right

  24. Mutation Method • Pre-process program to generate mutants • Execute all the mutants and the original program • If a mutant differs from the original, then it is "killed" • If all mutants are killed, then test set is adequate.

  25. Mutation Metaphor

  26. Example Program function square( x: integer): integer; begin square := x * x end

  27. Example Mutant function square( x: integer): integer; begin square := x + x end

  28. Executing Mutants • Test set {2} does not kill the mutant • Test set {2, 4} does kill the mutant, and it reveals the flaw in the program

  29. Which Mutants? • Examples: • Off by one errors (i+1, i, i-1) • Different variable (i, j, k)

  30. Assumptions ofMutation Testing • Competent Programmer: The perfect program is very close to the program under test • Oracle Hypothesis: You know the output for any input • Continuity: Every complicated mistake can be caught by looking for simple mistakes.

  31. Problems with Mutation • Very expensive • each test runs the whole program • many mutants for every program • Some mutants fail to halt • May be difficult to detect when a mutant is really just an equivalent program • Not reliable: may not try the right mutants

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