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Dataflow Analysis for Software Product Lines. Claus Brabrand IT University of Copenhagen Universidade Federal de Pernambuco [ brabrand@itu.dk ]. Márcio Ribeiro Universidade Federal de Alagoas Universidade Federal de Pernambuco [ mmr3@cin.ufpe.br ]. Paulo Borba - PowerPoint PPT Presentation
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Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Dataflow Analysis forSoftware Product Lines
Claus BrabrandIT University of Copenhagen
Universidade Federal de Pernambuco[ brabrand@itu.dk ]
Márcio RibeiroUniversidade Federal de Alagoas
Universidade Federal de Pernambuco[ mmr3@cin.ufpe.br ]
Paulo BorbaUniversidade Federal de Pernambuco
[ phmb@cin.ufpe.br ]
Társis TolêdoUniversidade Federal de Pernambuco
[ twt@cin.ufpe.br ]
[ 3 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Outline >
Introduction
Software Product Lines
Dataflow Analysis (recap)Dataflow Analyses for Software Product Lines:
feature in-sensitive (A1) vs feature sensitive (A2, A3, A4)
Results:A1 vs A2 vs A3 vs A4 (in theory and practice)
Related Work
Conclusion
[ 4 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Introduction
1x CAR
=
1x CELL PHONE
=
1x APPLICATION
=
CARS CELL PHONES APPLICATIONS
Traditional Software Development:One program = One product
Product Line:A ”family” of products (of N ”similar” products):
customize
SPL:(Family ofPrograms)
[ 5 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Software Product Line
SPL:
Feature Model: (e.g.: ψFM ≡ VIDEO COLOR)
Family ofPrograms:
COLOR
VIDEO
COLORVIDEO
VID
EO
Ø
{ Video }
{ Color, Video }
Configurations:Ø, {Color}, {Video}, {Color,Video}VALID
{ Color }
customize
2F
Set of Features:F = { COLOR, VIDEO }
2F
[ 6 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Software Product Line
SPL:Family of s:
COLOR
VIDEO
COLORVIDEO
VID
EO
Program
Conditional compilation:
#ifdef ( )
...
#endif
Alternatively,via Aspects(as in AOSD)
Logo logo;...
...logo.use();
#ifdef (VIDEO) logo = new Logo();#endif
Exam
ple
(SPL
fragm
ent)
Similarly for; e.g.:■ uninitialized vars■ unused variables■ ...
*** null-pointer exception!in configurations: {Ø, {COLOR}}
: fF | |
[ 8 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
resultresult
0100101111011010100111110111
0100101111011010100111110111
Analysis of SPLs
The Compilation Process:
...and for Software Product Lines:
0100101111011010100111110111
resultcompile run
ERROR!
customize 0100101111011010100111110111
result
run
ERROR!
ANALYZE!
ANALYZE!
Feature-sensitive data-flow analysis !
runruncompilecompilecompile
ANALYZE!ANALYZE! ERROR!ERROR!
2F
[ 9 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Outline >
Introduction
Software Product Lines
Dataflow Analysis (recap)Dataflow Analyses for Software Product Lines:
feature in-sensitive (A1) vs feature sensitive (A2, A3, A4)
Results:A1 vs A2 vs A3 vs A4 (in theory and practice)
Related Work
Conclusion
[ 10 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Dataflow Analysis
Dataflow Analysis:1) Control-flow graph
2) Lattice (finite height)
3) Transfer functions (monotone)
L
Example:"sign-of-x analysis"
[ 11 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Analyzing a Program1) Program 2) Build CFG 3) Make Equations
4) Solve equations: fixed-point computation (iteration)
5) SOLUTION (least fixed point):
Annotated with program points
[ 12 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Outline >
Introduction
Software Product Lines
Dataflow Analysis (recap)Dataflow Analyses for Software Product Lines:
feature in-sensitive (A1) vs feature sensitive (A2, A3, A4)
Results:A1 vs A2 vs A3 vs A4 (in theory and practice)
Related Work
Conclusion
[ 13 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
A1 (brute force)
A1 (feature in-sensitive):N = 2F compilations!
void m() { int x=0; ifdef(A) x++; ifdef(B) x--;}
c = {A}: c = {B}: c = {A,B}:
int x = 0;
x++;
x--;
int x = 0;
x++;
x--;
int x = 0;
x++;
x--;
0
_|
+
0
_|
-
0
_|
0/+
+
ψFM = A B∨
L
[ 14 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
A2 (consecutive)
A2 (feature sensitive!):void m() { int x=0; ifdef(A) x++; ifdef(B) x--;}
c = {A}: c = {B}: c = {A,B}:
int x = 0;
x++;
x--;
int x = 0;
x++;
x--;
int x = 0;
x++;
x--;
0
_|
+
0
_|
-
0
_|
0/+
+
A:
B:
A:
B:
A:
B:
0+
✗
✓
✓ ✗
✓ ✓
✓
✓ ✓
ψFM = A B∨
L
[ 15 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
A3 (simultaneous)
A3 (feature sensitive!):void m() { int x=0; ifdef(A) x++; ifdef(B) x--;}
∀c ∈ {{A},{B},{A,B}}:
int x = 0;
x++;
x--;
0
_|
+
0
_|
-
0
_|
0/+
+
A:
B:
0+
✓({A} = , {B} = , {A,B} = )
({A} = , {B} = , {A,B} = )
({A} = , {B} = , {A,B} = )
({A} = , {B} = , {A,B} = )
✓✓
✓✓
✓✓
✗
✗
ψFM = A B∨
L
[ 16 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
A4 (shared)
A4 (feature sensitive!):void m() { int x=0; ifdef(A) x++; ifdef(B) x--;}
ψFM = A B:∨
int x = 0;
x++;
x--;
A:
B:
_|( [[ψ]] = )
0 +( [[ψ ¬A∧ ]] = , [[ψ A∧ ]] = )
0( [[ψ]] = )
(A B) ¬A ¬B ≡ ∨ ∧ ∧ false…using BDDrepresentation!(compact+efficient)
+ - 0/+( [[ψ ¬A∧ ¬B∧ ]] = , [[ψ A∧ ¬B∧ ]] = , [[ψ ¬A∧ B∧ ]] = , [[ψ A∧ B∧ ]] = )0
i.e., invalid given wrt.the feature model, ψ !
ψFM = A B∨
L
[ 17 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Specification: A1, A2, A3, A4
A1
A2
A3
A4
[ 18 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
A1, A2, A3, and A4A1 A2
A3 A4
[ 19 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Outline >
Introduction
Software Product Lines
Dataflow Analysis (recap)Dataflow Analyses for Software Product Lines:
feature in-sensitive (A1) vs feature sensitive (A2, A3, A4)
Results:A1 vs A2 vs A3 vs A4 (in theory and practice)
Related Work
Conclusion
[ 20 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Intraprocedural Evaluation
Four (qualitatively different) SPL benchmarks:Implementation: A1, A2, A3, A4 in SOOT + CIDEEvaluation: total time, analysis time, memory usage
[ 21 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Results (total time)
In theory:
In practice:
6x 8x 14x
3x5x
3x
1x 1x 1x
2x 2½x2x
A2 (3x), A3 (4x), A4 (5x)
Feature sensitive (avg. gain factor):
(Reaching Definitions)
2F 2F
2F
[ 22 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Results (analysis time)
In theory:
In practice: TIME(A4) : Depends ondegree of sharing in SPL !
(caching!)(Reaching Definitions) A3 (1.5x) faster
On average (A2 vs A3):
A2
A3
vs
2F
[ 23 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Results (memory usage)
In theory:
In practice:(Reaching Definitions) 6.3 : 1
Average
2F
A2
A3
vs
SPACE(A4) : Depends ondegree of sharing in SPL !
[ 24 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Outline >
Introduction
Software Product Lines
Dataflow Analysis (recap)Dataflow Analyses for Software Product Lines:
feature in-sensitive (A1) vs feature sensitive (A2, A3, A4)
Results:A1 vs A2 vs A3 vs A4 (in theory and practice)
Related Work
Conclusion
[ 25 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Related Work (DFA)
Path-sensitive DFA:
Idea of “conditionally executed statements”
Compute different analysis info along different paths (~ A2, A3, A4) to improve precision or to optimize “hot paths”
Predicated DFA:
Guard lattice values by propositional logic predicates (~ A4), yielding “optimistic dataflow values” that are kept distinct during analysis (~ A3 and A4)
“Constant Propagation with Conditional Branches”( Wegman and Zadeck ) TOPLAS 1991
“Predicated Array Data-Flow Analysis for Run-time Parallelization”( Moon, Hall, and Murphy ) ICS 1998
Our work: Automatically lift any DFA to SPLs (with ψFM) ⇒feature-sensitive analysis for analyzing entire program family
[ 26 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Related Work (Lifting for SPLs)
Model Checking:
Type Checking:
Parsing:
Testing:
Model Checking Lots of Systems: Efficient Verification of Temporal Properties in Software Product Lines”( Classen, Heymans, Schobbens, Legay, and Raskin ) ICSE 2010
Model checks all SPLs at the same time (3.5x faster) than one by one! (similar goal, diff techniques)
Type checking ↔ DFA (similar goal, diff techniques)Our: auto lift any DFA (uninit vars, null pointers, ...)
“Type Safety for Feature-Oriented Product Lines”( Apel, Kastner, Grösslinger, and Lengauer ) ASE 2010
“Type-Checking Software Product Lines - A Formal Approach”( Kastner and Apel ) ASE 2008
“Variability-Aware Parsing in the Presence of Lexical Macros & C.C.”( Kastner, Giarrusso, Rendel, Erdweg, Ostermann, and Berger ) OOPSLA 2011
“Reducing Combinatorics in Testing Product Lines”( Hwan, Kim, Batory, and Khurshid ) AOSD 2011
Select relevant feature combinations for a given test caseUses (hardwired) DFA (w/o FM) to compute reachability
(similar techniques, diff goal):Split and merging parsing (~A4) and also uses instrumentation
[ 27 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Emerging Interfaces
[ 28 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Emerging Interfaces
"A Tool for Improving Maintainability of Preprocessor-based Product Lines"( Márcio Ribeiro, Társis Tolêdo, Paulo Borba, Claus Brabrand )
*** Best Tool Award ***CBSoft 2011:
[ 29 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Outline >
Introduction
Software Product Lines
Dataflow Analysis (recap)Dataflow Analyses for Software Product Lines:
feature in-sensitive (A1) vs feature sensitive (A2, A3, A4)
Results:A1 vs A2 vs A3 vs A4 (in theory and practice)
Related Work
Conclusion
[ 30 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Conclusion(s)
It is possible to analyze SPLs using DFAs
We can automatically "lift" any dataflow analysis and make it feature sensitive:
A2) Consecutive
A3) Simultaneous
A4) Shared Simultaneous
A2,A3,A4 much faster (3x,4x,5x) than naive A1
A3 is (1.5x) faster than A2 (caching!)
A4 saves lots of memory vs A3 (sharing!) 6.3 : 1
[ 31 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Future Work
Explore how all this scales to…:
In particular:…relative speed of A1 vs A2 vs A3 vs A4 ?
…which analyses are feasible vs in-feasible ?
INTER-proceduraldata-flow analysis
In progress...!
Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
< Obrigado* >
*) Thanks
Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
BONUS SLIDES
[ 34 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Results (analysis time)
In theory:
In practice: TIME(A4) : Depends ondegree of sharing in SPL !
Nx1 ≠ 1xN?!
(caching!)
(Reaching Definitions) A3 (1.5x) fasterOn average (A2 vs A3):
A2
A3
vs
2F
2F
[ 35 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
A2 vs A3 (caching)
Cache misses in A2 vs A3:
Normal cache:As expected, A2 incurs more cache misses ( slower!)⇒
Full/no cache*:As hypothesized, this indeed affects A2 more than A3
i.e., A3 has better cache properties than A2
*) we flush the L2 cache, by traversing an 8MB “bogus array” to invalidate cache!
A2
A3
vs
[ 36 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
IFDEF normalization
Refactor "undisciplined" (lexical) ifdefs into "disciplined" (syntactic) ifdefs:
Normalize "ifdef"s (by transformation):
[ 37 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Example Bug from Lampiro
Lampiro SPL (IM client for XMPP protocol):
*** uninitialized variable "logo"(if feature "GLIDER" is defined)
Similar problems with:undeclared variables, unused variables, null pointers, ...
[ 38 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
BDD (Binary Decision Diagram)
Compact and efficient representation forboolean functions (aka., set of set of names)
FAST: negation, conjunction, disjunction, equality !
= F(A,B,C) = A(BC)
A
C
minimized BDD
B
A
BB
C C C C
BDD
[ 39 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Formula ~ Set of Configurations
Definitions (given F, set of feature names):f F feature namec 2F configuration (set of feature names) c FX 22 set of config's (set of set of feature names) X 2F
Exampleifdefs:
F
[[ BA ]]
[[ A(BC) ]]
F = {A,B}
F = {A,B,C}
= { {A}, {B}, {A,B} }
= { {A,B}, {A,C}, {A,B,C} }
[ 40 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Feature Model (Example)
Feature Model:
Feature set:
Formula:
Set of configurations:FM Car Engine (1.01.4) Air1.4
{ {Car, Engine, 1.0}, {Car, Engine, 1.4}, {Car, Engine, 1.4, Air} }
F = {Car, Engine, 1.0, 1.4, Air}
Note:| [[FM]] | = 3 < 32 = |2F |
[[ ]] =
Engine
1.0
Air
Air
1.4
[ 41 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Conditional Compilation
The 'ifdef' construction:
Syntactic variant of lexical #ifdef
Propositional Logic:
where fF (finite set of feature names)
Example:
STM : 'ifdef' '(' ')' STM
: fF | |
status.print("you die");ifdef (DeluxeVersion && ColorDisplay) { player.redraw(Color.red); Audio.play("crash.wav");}lives = lives - 1;
A
ifdef (A) { ...
}
[ 42 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
Lexical #ifdef Syntactic ifdef
Simple transformation:
We do not handle non-syntactic '#ifdef's:
Fair assumption(also in CIDE)
Nested ifdef's also give rise to a conj.of formulas
[ 43 ]Dataflow Analysis for Software Product Lines May 24, 2012COPLAS, DIKU
CASE 1: "COPY"
A4: Lazy Splitting (using BDDs)CASE 2: "APPLY" CASE 3: "SPLIT"
: S
[ =l , ... ]
[ =l , ... ]
l ' = fS(l )
: S
[ =l , ... ]
[ =l ', ... ]
l ' = fS(l )
: S
[ =l , ... ]
[ =l, =l' ,...]
l ' = fS(l )
= Ø = Ø
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