Software product line with IBEA

Constraint Solver for Product LinesProject 1 Presentation

Jianfeng Chen

[email protected]

April 9, 2015

Jianfeng Chen ([email protected]) Constraint Solver for Product Lines April 9, 2015 1 / 18

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, emailconnection, etc.

An operating system have thousands of modules and features.

Among them, which features should be implemented?

Software features

Software engineering is becoming more and more complex.

More and more features.

Software features

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.

Feature model

Feature tree:

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-

Feature model

Feature tree:

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-

Feature model

Feature tree:

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-

Feature model

Feature tree:

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 ([email protected]) Constraint Solver for Product Lines April 9, 2015 3 / 18

Feature model

open repository

more than 600 feature models

SXFM language; parser is available

INPUT:

feature model(including constraints)

some important feature attributes:

costfamiliarity(used before?)defects

OUTPUT: configurations (select which features?)

GOALS:

⇓ constraint violations

⇑ feature richness

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

familiarity(used before?)defects

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

costfamiliarity(used before?)

defects

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

INPUT:

GOALS:

⇑ used features

⇓ defects

⇓ total cost

Genetic algorithms

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

Domination

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

A 3 7 4 2 7

B 4 7 6 2 7A dominates B

A 4 7 6 2 7

B 4 4 6 1 5B dominates A

A 3 7 4 2 7

B 4 4 6 1 7A is indifferent from B

Domination

A 3 7 4 2 7

A 4 7 6 2 7

A 3 7 4 2 7

Domination

A 3 7 4 2 7

B 4 7 6 2 7

A dominates B

A 4 7 6 2 7

A 3 7 4 2 7

Domination

A 3 7 4 2 7

A 4 7 6 2 7

A 3 7 4 2 7

Domination

A 3 7 4 2 7

A 4 7 6 2 7

B 4 4 6 1 5

B dominates A

A 3 7 4 2 7

Domination

A 3 7 4 2 7

A 4 7 6 2 7

A 3 7 4 2 7

Domination

A 3 7 4 2 7

A 4 7 6 2 7

A 3 7 4 2 7

B 4 4 6 1 7

A is indifferent from B

Domination

A 3 7 4 2 7

A 4 7 6 2 7

A 3 7 4 2 7

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?

[Deb, Kalyanmoy, et al. 2002]

What if indifferent?add to the population pool!

How to prune?

Method 2: Indicator-based search

Binary quality indicatorFitness F (x) =

∑i∈P\x −e−I (i ,x)/κ

Binary quality indicator

Fitness F (x) =∑

i∈P\x −e−I (i ,x)/κ

Initialization

Get fitness

Eliminate the individuals with smallest fitness

Mating selection

Variation

Terminate or back to second step

Initialization

Get fitness

Mating selection

Variation

Initialization

Get fitness

Mating selection

Variation

Initialization

Get fitness

Mating selection

Variation

Initialization

Get fitness

Mating selection

Variation

Initialization

Get fitness

Mating selection

Variation

Initialization

Get fitness

Mating selection

Variation

Results

FM test (medium size)166 features112 leaves46 cross-tree constraints

Results

IBEA.FM.

Results

DE vs IBEA.FM.

Figure: Features NOT provided

Results

DE vs IBEA.FM.

Figure: Features NOT providedJianfeng Chen ([email protected]) Constraint Solver for Product Lines April 9, 2015 14 / 18

Results

DE vs IBEA.FM.

Figure: DefectsJianfeng Chen ([email protected]) Constraint Solver for Product Lines April 9, 2015 15 / 18

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

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Further discussion

Stopping criterion

customable weight

Running time

DE-FM-500 gens-54s

Front Quality

Future work

pruning in differential evolution (flocking behaviors)

mutate with reservations

reconstruct the feature tree (reduce the search space)

chaff algorithm

Satz/Z3

Future work

chaff algorithm

Satz/Z3

Future work

chaff algorithm

Satz/Z3

Future work

chaff algorithm

Satz/Z3

Future work

chaff algorithm

Satz/Z3

Future work

chaff algorithm

Satz/Z3

Thank you!

Software product line with IBEA

Engineering

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