This document summarizes a presentation on using genetic algorithms to solve constraint problems in product lines. It discusses using genetic algorithms to select optimal configurations of features while minimizing constraints violations, maximizing feature richness and usage, and minimizing defects and costs. Two methods are proposed: differential evolution and indicator-based search. The goals are to evolve towards configurations that satisfy constraints and optimize multiple objectives related to features.

1. Constraint Solver for Product Lines
Project 1 Presentation
Jianfeng Chen
jchen37@ncsu.edu
April 9, 2015
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 1 / 18

2. Software features
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 2 / 18

3. Software features
Software engineering is becoming more and more complex.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 2 / 18

4. Software features
Software engineering is becoming more and more complex.
More and more features.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 2 / 18

5. Software features
Software engineering is becoming more and more complex.
More and more features.
C2C online trading system: database, commercial data encryption, mil-
lisecond(microsecond) response, customable GUI, chatting module, email
connection, etc.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 2 / 18

6. Software features
Software engineering is becoming more and more complex.
More and more features.
C2C online trading system: database, commercial data encryption, mil-
lisecond(microsecond) response, customable GUI, chatting module, email
connection, etc.
An operating system have thousands of modules and features.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 2 / 18

7. Software features
Software engineering is becoming more and more complex.
More and more features.
C2C online trading system: database, commercial data encryption, mil-
lisecond(microsecond) response, customable GUI, chatting module, email
connection, etc.
An operating system have thousands of modules and features.
Among them, which features should be implemented?
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 2 / 18

8. Feature model
Feature tree:
http://www.digplanet.com/wiki/Feature_model
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 3 / 18

9. Feature model
Feature tree:
http://www.digplanet.com/wiki/Feature_model
Many features are related to others.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 3 / 18

10. Feature model
Feature tree:
http://www.digplanet.com/wiki/Feature_model
Many features are related to others.
Cross-tree constraints complex the problem.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 3 / 18

11. Feature model
Feature tree:
http://www.digplanet.com/wiki/Feature_model
Many features are related to others.
Cross-tree constraints complex the problem.
Cross-tree constraints are widespread in the software products.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 3 / 18

12. Feature model
Feature tree:
http://www.digplanet.com/wiki/Feature_model
Many features are related to others.
Cross-tree constraints complex the problem.
Cross-tree constraints are widespread in the software products.
Ex. more than three fourths features in eCos(an open source real-time oper-
ating system) are referred by some constraints.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 3 / 18

13. Feature model
SPLOT
open repository
more than 600 feature models
SXFM language; parser is available
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 4 / 18

14. Goals
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

15. Goals
INPUT:
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

16. Goals
INPUT:
feature model(including constraints)
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

17. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

18. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

19. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

20. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

21. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

22. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
GOALS:
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

23. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
GOALS:
⇓ constraint violations
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

24. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
GOALS:
⇓ constraint violations
⇑ feature richness
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

25. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
GOALS:
⇓ constraint violations
⇑ feature richness
⇑ used features
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

26. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
GOALS:
⇓ constraint violations
⇑ feature richness
⇑ used features
⇓ defects
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

27. Goals
INPUT:
feature model(including constraints)
some important feature attributes:
cost
familiarity(used before?)
defects
OUTPUT: configurations (select which features?)
GOALS:
⇓ constraint violations
⇑ feature richness
⇑ used features
⇓ defects
⇓ total cost
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 5 / 18

28. Genetic algorithms
http://www.abrandao.com/2015/01/21/simple-php-genetic-algorithm/
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 6 / 18

29. Domination
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

30. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

31. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
A 3 7 4 2 7
B 4 7 6 2 7
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

32. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
A 3 7 4 2 7
B 4 7 6 2 7
A dominates B
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

33. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
A 3 7 4 2 7
B 4 7 6 2 7
A dominates B
A 4 7 6 2 7
B 4 4 6 1 5
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

34. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
A 3 7 4 2 7
B 4 7 6 2 7
A dominates B
A 4 7 6 2 7
B 4 4 6 1 5
B dominates A
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

35. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
A 3 7 4 2 7
B 4 7 6 2 7
A dominates B
A 4 7 6 2 7
B 4 4 6 1 5
B dominates A
A 3 7 4 2 7
B 4 4 6 1 7
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

36. Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.
A 3 7 4 2 7
B 4 7 6 2 7
A dominates B
A 4 7 6 2 7
B 4 4 6 1 5
B dominates A
A 3 7 4 2 7
B 4 4 6 1 7
A is indifferent from B
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 7 / 18

37. Method 1: Differential Evolution
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 8 / 18

38. Method 1: Differential Evolution
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 8 / 18

39. Method 1: Differential Evolution
continuous: new = A + F ∗ (B − C)
binary: for each bit, new = A|B|C basing on some lottery.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 8 / 18

40. Method 1: Differential Evolution
What if indifferent?
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 9 / 18

41. Method 1: Differential Evolution
What if indifferent?
add to the population pool!
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 9 / 18

42. Method 1: Differential Evolution
What if indifferent?
add to the population pool!
How to prune?
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 9 / 18

43. Method 1: Differential Evolution
What if indifferent?
add to the population pool!
How to prune?
[Deb, Kalyanmoy, et al. 2002]
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 9 / 18

44. Method 2: Indicator-based search
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 10 / 18

45. Method 2: Indicator-based search
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 10 / 18

46. Method 2: Indicator-based search
Binary quality indicator
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 10 / 18

47. Method 2: Indicator-based search
Binary quality indicator
Fitness F(x) = i∈Px −e−I(i,x)/κ
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 10 / 18

48. Method 2: Indicator-based search
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

49. Method 2: Indicator-based search
Initialization
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

50. Method 2: Indicator-based search
Initialization
Get fitness
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

51. Method 2: Indicator-based search
Initialization
Get fitness
Eliminate the individuals with smallest fitness
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

52. Method 2: Indicator-based search
Initialization
Get fitness
Eliminate the individuals with smallest fitness
Mating selection
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

53. Method 2: Indicator-based search
Initialization
Get fitness
Eliminate the individuals with smallest fitness
Mating selection
Variation
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

54. Method 2: Indicator-based search
Initialization
Get fitness
Eliminate the individuals with smallest fitness
Mating selection
Variation
Terminate or back to second step
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 11 / 18

55. Results
FM test (medium size)
166 features
112 leaves
46 cross-tree constraints
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 12 / 18

56. Results
IBEA.FM.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 13 / 18

57. Results
DE vs IBEA.FM.
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 14 / 18

58. Results
DE vs IBEA.FM.
Figure: Features NOT provided
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 14 / 18

59. Results
DE vs IBEA.FM.
Figure: Defects
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 15 / 18

60. Further discussion
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

61. Further discussion
Stopping criterion
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

62. Further discussion
Stopping criterion
improvement/deterioration accumulation
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

63. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

64. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Running time
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

65. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Running time
DE-FM-500 gens-54s
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

66. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Running time
DE-FM-500 gens-54s
IBEA-FM-500 gens-83s
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

67. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Running time
DE-FM-500 gens-54s
IBEA-FM-500 gens-83s
In average, IBEA = 1.3*DE
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

68. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Running time
DE-FM-500 gens-54s
IBEA-FM-500 gens-83s
In average, IBEA = 1.3*DE
Front Quality
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

69. Further discussion
Stopping criterion
improvement/deterioration accumulation
customable weight
Running time
DE-FM-500 gens-54s
IBEA-FM-500 gens-83s
In average, IBEA = 1.3*DE
Front Quality
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 16 / 18

70. Future work
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 17 / 18

71. Future work
pruning in differential evolution (flocking behaviors)
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 17 / 18

72. Future work
pruning in differential evolution (flocking behaviors)
mutate with reservations
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 17 / 18

73. Future work
pruning in differential evolution (flocking behaviors)
mutate with reservations
reconstruct the feature tree (reduce the search space)
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 17 / 18

74. Future work
pruning in differential evolution (flocking behaviors)
mutate with reservations
reconstruct the feature tree (reduce the search space)
chaff algorithm
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 17 / 18

75. Future work
pruning in differential evolution (flocking behaviors)
mutate with reservations
reconstruct the feature tree (reduce the search space)
chaff algorithm
Satz/Z3
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 17 / 18

76. Thank you!
Jianfeng Chen (jchen37@ncsu.edu) Constraint Solver for Product Lines April 9, 2015 18 / 18