research @ gsd lab, university of waterloo yingfei xiong, 2011

Research @ GSD Lab, University of Waterloo

Yingfei Xiong, 2011

About me

• 2004~2006， Ph.D. Student@SEI, PKU– SA Group

• 2006~2009， Ph.D. Student@U Tokyo– Advisors: Zhenjiang Hu, Masato Takeichi

• 2009~， PostDoc@U Waterloo– Advisor： Krzysztof Czarnecki

Generative Software Development Laboratory

• Leaded by Krzysztof Czarnecki– Generative programming– Cardinarity-based feature model– Staged configuration– 12 papers cited 100+ times– Ph.D Thesis cited 2000+ times

• “Probably one of the best groups in MDE”– Jean Bezivin, University of Nantes

Generative Software Development Laboratory

• 1 faculty• 1 research associate• 2 postdocs• 7+1 Ph.D. Students• 2 MASc. Students• 2 Visitors• 2010 publications

– ICSE, ASE, SPLC, ICSM, ICMT, SLE, RE industry track, MDI, VaMoS...

– With only 1 rejection

Funding

Industry + Government match

Application-oriented, data-centric research

Application-oriented

• Real industry applications > publications in top venues

• Usefulness verified on real data > intelligent solution

• Most needed problem > interesting / promising problem

Data Centric

Data-Centric

Collect data

Analyze data

Discover problem

Discover solution

Evaluate

Apply to industry / write paper

Idea-centric

Idea

Develop into a solution

Refine problem

Collect data

Evaluate

Write paper

Problem-centric

Problem

Analyze Problem

Propose solution

Collect data

Evaluate

Write paper

Rafael's Bug Report Assignment

Thiago's API Migration

RehanUse Case Discovery

Steven / Thorsten / RafaelReal World Feature Model

Publication

• No publication in 2008• No publication in 2009• 2010

– [VaMoS] Variability Model of the Linux Kernel– [SPLC] Evolution of the Linux Kernel Variabilit

y Model– [ASE] Variability Modeling in the Real: A Pers

pective from the Operating Systems Domain– [ICSE] Reverse Engineering Feature Models

Moises, YingfeiProcess Model Refinement

Discussion

• Advantages:– Solving real world problem– No false assumption– Direct application to industry

• Disadvantages:– Fewer papers

• collecting data requires a lot of work• problem may not be easily solvable• work may not be easily appreciated

– Potentially short-sighted

Fix Generation for Feature Model Configuration

• CDL: a configuration language for embeded operation system

• eCos: graphical configurator for CDL

• Both developed by RedHat

• FODA feature model with attribute

CDL Examplecdl_option DEBUG {

display "Enable Debug"}

cdl_option LOG {display "Enable Log"

}

cdl_option LOG_LEVEL {display "Log Level"active_if LOGflavor datadefault_value 5

}

cdl_option TRACE {

display "Enable Trace"

requires DEBUG & LOG

}

cdl_option TRACE_LEVEL {

display "Trace Level"

active_if TRACE

flavor data

default_value 3

requires TRACE_LEVEL < LOG_LEVEL

}

Demo

Problem: No complete fix list

• "if CYGPKG_MYPKG_OP1 is active, make sure that the list of tests for that package is a substring of CYGDAT_MYPKG_ACTIVE_TESTS. This works 50% of the time. Problem is the other 50% of the time, rather than fiddling with the substrings, it enables / disables my subpackage!"

Is it possible to give complete solution list?

Will it be too long to use?

Will it be too long to generate?

Boolean Features

• Features: A,B,C,D,E,F

• Constraints– A -> B \/ C– B -> D \/ E– E -> F \/ G

• Configuration:– A=true– B,C,D,E,F=false

• Fixes– A->false– B->true, D->true– B->true , E-> true, F->

true– B->true, E->true, G->tr

ue– C->true

Factors affecting the number of fix

• How many features are inter-related

• How many disjunction are used in constraints

• Observation: Both factors are of low values in real CDL models

Non-Boolean Features

• Features: A, B

• Constraints: A > B

• Configuration: A=10, B=11

Abstract Fixes

• Features: A, B

• Constraints: A > B

• Configuration: A=10, B=11

• Fixes:– Change A { A>11 }– Change B { B<10 }

Minimal Fixes

• Changing as few variables as possible– Change A { A>11 }– is better than– Change A, B {A>B}

• Constraints as loose as possible– Change A {A>11}– is better than– Change A {A>12}

Factors affecting abstract fixes

• How many variables are inter-constrained

• How complex the constraints are

• Observation: Both factors are low

Fix Generation for Boolean Features:

1. Statically analyze the violated constraint

2. Statically analyze interactions

3. Remove unnecessary fixes based on dynamic fixes

An Example

• Variables: A, B, C, D

• Constraints:– A->(B/\C)– B->(!C\/D)

• Configuration: A=true, B=false, C=true, D=false

• A->(B/\C) is violated

Step 1. Analyze the Violated Constraint

fix[A->(B/\C)]

=fix[!A] fix[B/\C]⋃={<A=false>} (fix[B] × ⋃

fix[C])

={<A=false>} {<B=tru⋃e, C=true>}

={<A=false>, <B=true, C=true>}

• Constraints:– A->(B/\C)– B->(!C\/D)

• Configuration: – A=true, B=false, C=tru

e, D=false

Step 2: Analyze Interactions

{<B=true, C=true>} changes B and C

fix[true->(!true\/D)]=fix[false] fix[false\/D]⋃={} fix[false] fix[D]⋃ ⋃={<D=true>}

FixSet={<A=false>} ({<B=true,C⋃

=true>}×{<D=true>}={<A=false>, <B=true, C=tru

e, D=true>}

• Constraints:– A->(B/\C)– B->(!C\/D)

• Configuration: – A=true, B=false, C=tru

e, D=false

Step 3: Remove unnecessary fix

{<A=false>, <B=true, C=true, D=true>}

becomes

{<A>, <B, D>}

• Constraints:– A->(B/\C)– B->(!C\/D)

• Configuration: – A=true, B=false, C=tru

e, D=false

Non-Boolean Features

• Represent non-boolean constraints as boolean variables

• Statically analyze boolean constraints

• Convert the fix back to abstract ones

An Example

• Features:– M, N:Boolean– A, B:Int

• Constraints:– N -> A>10– A<B -> M

• Configuration:– A=8, B=2, M=false, N=true

Step1: Represent as boolean variables

• Features:– M, N:Boolean– A, B:Int

• Constraints:– N -> A>10– A<B -> M

• Configuration:– A=8, B=2, M=false, N=

true

• Boolean Variables:– X: A>10– Y: A<B

• Converted Constraints:– N -> X– Y -> M

• Converted Configuration:– X=false– Y=true– M=false, N=true

Step 2.1: Analyze Single Constraint

• Features:– M, N:Boolean– A, B:Int

• Configuration:– A=8, B=2, M=false, N=true

• Boolean Variables:– X: A>10– Y: A<B

• Converted Constraints:– N -> X– Y -> M

• fix[N->X]={<N=false>, <X=true>}

Step 2.2: Analyze Interaction

• Features:– M, N:Boolean– A, B:Int

• Configuration:– A=8, B=2, M=false, N=true

• Boolean Variables:– X: A>10– Y: A<B

• Converted Constraints:– N -> X– Y -> M

• <X=true> relates to Y• fix[Y->M]={<Y=false>,

<M=true>}

• FixSet={<N=false>} ({X=tr⋃ue}×{<Y=false>, <M=true>}={<N=false>, <X=true, Y=fasle>, <X=true, M=true>}

Step 3.1 Produce Variable Set

• Features:– M, N:Boolean– A, B:Int

• Configuration:– A=8, B=2, M=false, N=true

• Boolean Variables:– X: A>10– Y: A<B

• Converted Constraints:– N -> X– Y -> M

• <X=true, Y=false>• All variables in X and

Y– {A, B}

• All non-empty subsets– {A}– {B}– {A,B}

Step 3.2 Build a partial order

• Features:– M, N:Boolean– A, B:Int

• Configuration:– A=8, B=2, M=false, N=true

• Boolean Variables:– X: A>10– Y: A<B

• Converted Constraints:– N -> X– Y -> M

{A} {B}

{A,B}

• <X=true, Y=false>

Step 3.3 Test Satisfiability using SMT solvers

• Features:– M, N:Boolean– A, B:Int

• Configuration:– A=8, B=2, M=false, N=true

• Boolean Variables:– X: A>10– Y: A<B

• Converted Constraints:– N -> X– Y -> M

• <X=true, Y=false>• Change A {A>10, A<

2}– unsatisfiable

• Change B {8>10, 8<B}– unsatisfiable

• Change A, B {A>10, A<B}– satisfiable

Evaluation

• Verification of all observation– Is the number of fixes always small?– Is the generation from non-boolean constraint

s always fast?

Summary

• Application-oriented, data-centric research method

• "Data-centric" is probably important to software engineering research