The document discusses the use of evolutionary algorithms, specifically genetic algorithms and genetic programming, in white-box software testing to automate the generation of test cases. It reviews several surveyed papers that explore different aspects of evolutionary testing, including factors affecting fitness functions and approaches to testing object-oriented software. The take-home messages highlight the challenges of generating test cases and the efficacy of evolutionary algorithms in addressing these challenges.

1. White-Box Software Testing
using Evolutionary Algorithms
Yaser Sulaiman
1

2. Time for a break!
2

3. Pex
3

4. White-box testing
4

5. photo by Simon Goldenberg 5

6. Test adequacy criteria
6

7. Statement Branch
Weakest
Strongest
Path Condition
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8. Coming up with test cases/data isn’t
as easy as it sounds
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9. photo by racheocity 10

10. Cover as much as you can!
11

11. photo by [ d i e g o ] 12

12. Looks like an optimization
problem.. sort of
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13. So how about automating it?
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14. Evolutionary algorithms
15

15. photo by ][/][ 16

16. Genetic
Algorithms
(GA)
Evolutionary
Algorithms
Genetic
Programming
(GP)
17

17. GA
18

18. Fitness
Selection
Function
Chromosomes
Mutation Crossover
19

19. GP
20

20. Specialization of GA
21

21. Where individuals represent
computer programs
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22. GP evolves programs!
23

23. stolen from Wikipedia 24

24. Mathematically speaking
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25. Given a set of inputs and outputs
, a program is sought which
satisfies
= ()
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26. Fitness of a candidate program?
27

27. Expected output
vs.
= () Actual output
28

28. 2
= −
=1
where ∈ and ∈
29

29. Moving on to the surveyed papers
30

30. Brendt* Tonella Wappler* Ahmed*
2002 2004 2006 2008
* et al. 31

31. Brendt* Tonella Wappler* Ahmed*
2002 2004 2006 2008
* et al. 32

32. “Breeding software test cases with
genetic algorithms”
33

33. “Breeding software test cases with
genetic algorithms”
34

34. Fossil record
35

35. Relative fitness function
36

36. Berndt et al. (2002) 37

37. 3 factors affect the fitness function
38

38. 1. Novelty
measure of uniqueness
39

39. 2. Proximity
measure of closeness
40

40. 3. Severity
measure of seriousness
41

41. “simple rules, complex behavior”
42

42. Explorers, prospectors, and miners
43

43. Brendt* Tonella Wappler* Ahmed*
2002 2004 2006 2008
* et al. 44

44. “Evolutionary testing of classes”
45

45. “Evolutionary testing of classes”
46

46. Object-oriented testing
47

47. Class Under Test (CUT)
&
Method Under Test (MUT)
48

48. Sequence of object constructions &
method calls
49

49. Tonella (2004) focuses on
representation and operators
50

50. A chromosome isn’t just a sequence
of input values anymore
51

51. $a=A():$b=B():$a.m(int, $b) @ 3
52

52. Brendt* Tonella Wappler* Ahmed*
2002 2004 2006 2008
* et al. 53

53. “Evolutionary unit testing of object-
oriented software using strongly-
typed genetic programming”
54

54. “Evolutionary unit testing of object-
oriented software using strongly-
typed genetic programming”
55

55. GP that is type-aware
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56. Wappler & Wegener (2006) focus
on call sequence feasibility
57

57. Wappler & Wegener (2006) 58

58. Method Call Dependence Graph
(MCDG)
59

59. Wappler & Wegener (2006) 60

60. A sequence is feasible if it
corresponds to an acyclic subgraph
of the MCDG
61

61. Wappler & Wegener (2006) 62

62. Brendt* Tonella Wappler* Ahmed*
2002 2004 2006 2008
* et al. 63

63. “GA-based multiple paths test data
generator”
64

64. “GA-based multiple paths test data
generator”
65

65. Ahmed & Hermadi (2008) focus on
the efficiency of test data generation
66

66. Generating multiple test data to
cover multiple target paths in one
GA run
67

67. Requires incorporating the paths
within the fitness calculation
68

68. // TODO: Redigest the paper.
69

69. Let’s compare the surveyed papers
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70. Evolutionary Object- Coverage
Paper
Algorithm Oriented Criterion
Berndt et al.
GA Branch
(2002)
Tonella
GA Branch
(2004)
Wappler &
Wegener Strongly-Typed GP Branch
(2006)
Ahmed &
Hermadi GA Path
(2008)
71

71. A direction for future work
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72. The reverse direction
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73. Evolutionary
Program Test Cases
Algorithm
74

74. Evolutionary
Program Test Cases
Algorithm
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75. Writing test cases first?
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76. It’s called TDD
77

77. photo by nyuhuhuu 78

78. photo by Magic Madzik 79

79. TDD + GP isn’t a new idea
80

80. 81

81. Take-home messages
82

82. Generating test cases can be
challenging
83

83. Testing object-oriented software
provides even more challenges
84

84. Evolutionary algorithms can be
used to solve [some of] these
problems
85

85. Even software developers turn to
Mother Nature for inspiration
86

86. …</presentation>
<questions>…
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