Software product line with IBEA

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

Software features

Software features
Software engineering is becoming more and more complex.

Software features
More and more features.

Software features
C2C online trading system: database, commercial data encryption, mil-
lisecond(microsecond) response, customable GUI, chatting module, email
connection, etc.

Software features
connection, etc.
An operating system have thousands of modules and features.

Software features
connection, etc.
An operating system have thousands of modules and features.
Among them, which features should be implemented?

Feature model
Feature tree:
http://www.digplanet.com/wiki/Feature_model

Feature model
Feature tree:
Many features are related to others.

Feature model
Feature tree:
Cross-tree constraints complex the problem.

Feature model
Feature tree:
Cross-tree constraints are widespread in the software products.

Feature model
Feature tree:
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.

Feature model
SPLOT
open repository
more than 600 feature models
SXFM language; parser is available

Goals

Goals
INPUT:

Goals
INPUT:
feature model(including constraints)

Goals
INPUT:
some important feature attributes:

Goals
INPUT:
cost

Goals
INPUT:
cost
familiarity(used before?)

Goals
INPUT:
cost
defects

Goals
INPUT:
cost
defects
OUTPUT: conﬁgurations (select which features?)

Goals
INPUT:
cost
defects
GOALS:

Goals
INPUT:
cost
defects
GOALS:
⇓ constraint violations

Goals
INPUT:
cost
defects
GOALS:
⇑ feature richness

Goals
INPUT:
cost
defects
GOALS:
⇑ used features

Goals
INPUT:
cost
defects
GOALS:
⇑ used features
⇓ defects

Goals
INPUT:
cost
defects
GOALS:
⇑ used features
⇓ defects
⇓ total cost

Genetic algorithms
http://www.abrandao.com/2015/01/21/simple-php-genetic-algorithm/

Domination

Domination
Reverse the uprise objectives ⇒ all objectives are the less the better.

Domination
A 3 7 4 2 7
B 4 7 6 2 7

Domination
A 3 7 4 2 7
B 4 7 6 2 7
A dominates B

Domination
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

Domination
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

Domination
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

Domination
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

Method 1: Diﬀerential Evolution

continuous: new = A + F ∗ (B − C)
binary: for each bit, new = A|B|C basing on some lottery.

What if indiﬀerent?

add to the population pool!

How to prune?

How to prune?
[Deb, Kalyanmoy, et al. 2002]

Method 2: Indicator-based search

Binary quality indicator

Binary quality indicator
Fitness F(x) = i∈Px −e−I(i,x)/κ

Initialization

Initialization
Get ﬁtness

Initialization
Get ﬁtness
Eliminate the individuals with smallest ﬁtness

Initialization
Get ﬁtness
Mating selection

Initialization
Get ﬁtness
Mating selection
Variation

Initialization
Get ﬁtness
Mating selection
Variation
Terminate or back to second step

Results
FM test (medium size)
166 features
112 leaves
46 cross-tree constraints

Results
IBEA.FM.

Results
DE vs IBEA.FM.

Results
DE vs IBEA.FM.
Figure: Features NOT provided

Results
DE vs IBEA.FM.
Figure: Defects

Further discussion

Further discussion
Stopping criterion

Further discussion
Stopping criterion
improvement/deterioration accumulation

Further discussion
Stopping criterion
customable weight

Further discussion
Stopping criterion
customable weight
Running time

Further discussion
Stopping criterion
customable weight
Running time
DE-FM-500 gens-54s

Further discussion
Stopping criterion
customable weight
Running time
DE-FM-500 gens-54s
IBEA-FM-500 gens-83s

Further discussion
Stopping criterion
customable weight
Running time
DE-FM-500 gens-54s
In average, IBEA = 1.3*DE

Further discussion
Stopping criterion
customable weight
Running time
DE-FM-500 gens-54s
In average, IBEA = 1.3*DE
Front Quality

Future work

Future work
pruning in diﬀerential evolution (ﬂocking behaviors)

Future work
mutate with reservations

Future work
reconstruct the feature tree (reduce the search space)

Future work
chaﬀ algorithm

Future work
chaﬀ algorithm
Satz/Z3

Thank you!

Software product line with IBEA

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