Download - Static Analysis of Software Product Lines

Static Analysis ofSoftware Product Lines

Jan Midtgaard Andrzej Wąsowski

Claus Brabrand Paulo Borba

Mira MeziniEric Bodden

Márcio Ribeiro Társis Tolêdo

[ DFA-4-SPL ]( AOSD 2012 )

[ SPLLIFT ]( PLDI 2013 )

[ Var-Abs-Int ](in progress..)

[ 2 ]"Static Analysis of Software Product Lines" Aug 9, 2013MT Lab Meeting

< Outline >Introduction:

Software Product Lines (SPL)Dataflow Analysis (DFA)

DFA-4-SPL:A0 (brute force) (feature in-sensitive)A1 (consecutive)A2 (simultaneous)A3 (shared simultaneous)SPLLIFT (graph encoding)

Evaluation and ResultsSketch of Work in Progress: "Var-Abs-Int"

(feature sensitive)


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):

customizeSPL:

(Family ofPrograms)


Software Product LineSPL:

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


Software Product LineSPLs based on Conditional Compilation:

#ifdef ( )

...

#endif

Logo logo;...

...logo.use();

#ifdef (VIDEO) logo = new Logo();#endif

Exam

ple

(SPL

frag

men

t) *** null-pointer exception!in configurations: {Ø, {COLOR}}

: fF | |


resultresult

0100101111011010100111110111

0100101111011010100111110111

Analysis of SPLsThe Compilation Process:

...and for Software Product Lines:

0100101111011010100111110111

resultcompile run

ERROR!

generate 0100101111011010100111110111

resultrun

ERROR!

ANALYZE!

ANALYZE!

Feature-sensitive data-flow analysis !

runruncompilecompilecompile

ANALYZE!ANALYZE! ERROR!ERROR!

2F


Dataflow AnalysisDataflow Analysis:

1) Control-flow graph2) Lattice (finite height)3) Transfer functions (monotone)

L

Example:"sign-of-x analysis"


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






(feature sensitive)

Dataflow Analysis forSoftware Product LinesDFA-4-SPLClaus Brabrand

IT University of CopenhagenUniversidade Federal de Pernambuco

[ [email protected] ]

Márcio RibeiroUniversidade Federal de Alagoas

Universidade Federal de Pernambuco[ [email protected] ]

Paulo BorbaUniversidade Federal de Pernambuco


Társis ToledoUniversidade Federal de Pernambuco


AOSD 2012 and TAOSD 2013

"Intraprocedural Dataflow Analysis for Software Product Lines"

http://images.google.com/imgres?imgurl=http://supportdenmark.com/FlagBig.png&imgrefurl=http://supportdenmark.com/&h=413&w=579&sz=193&hl=en&start=2&um=1&tbnid=6F8YHc3c56TZOM:&tbnh=96&tbnw=134&prev=/images?q=danish+flag&svnum=10&um=1&hl=en&rls=com.microsoft:en-US


A0A0(brute force):

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∨

Lfeature in-sensitive!

N = O(2F) compilations!


int x = 0;

x++;

x--;

A:

B:

int x = 0;

x++;

x--;

A:

B:

int x = 0;

x++;

x--;

A:

B:

A1A1(consecutive):


c = {A}:

0

_|

+

✗

✓

✓

ψFM = A B∨

L

c = {B}: c = {A,B}:

0

_|

-

0

_|

0/+

+✗

✓ ✓

✓

✓ ✓

+

0

feature sensitive!

N = O(2F) fixp iterations!


x++;

+({A} = , {B} = , {A,B} = )

({A} = , {B} = , {A,B} = )

({A} = , {B} = , {A,B} = )

A2A2(simultaneous):


∀c ∈ {{A},{B},{A,B}}:

int x = 0;

x--;

0

_|

0

_|

-

0

_|

0/+

+

A:

B:

✓({A} = , {B} = , {A,B} = )✓✓

✓✓

✓✓

✗

✗

ψFM = A B∨

L

0

+

feature sensitive!LL × ×

{A} {B} {A,B}

L =


x--;

+

x++;

0

( [[ψ ¬A ]] = , [[∧ ψ A ]] = , [[∧ ψ ¬A ]] = , [[∧ ψ A ]] = )∧

( [[ψ ]] = , [[ψ ]] = )

A3A3(shared sim.):


ψFM = A B:∨

int x = 0;

A:

B:

_|( [[ψ]] = )

0( [[ψ]] = )

(A B) ¬A ¬B ≡ ∨ ∧ ∧ false

can use BDDrepresentation !(compact+efficient)

- 0/+

i.e., invalid given wrt.the feature model, ψ !

ψFM = A B∨

0∧¬A ∧A +

∧¬B ∧¬B ∧B ∧B

feature sensitive!LLL × ×

{A} {B} {A,B}

L =

Statically AnalyzingSoftware Product Linesin Minutes instead of YearsSPLLIFT

PLDI 2013

Eric BoddenTechnische Universität Darmstadt


Társis TolêdoUniversidade Federal de Pernambuco


Márcio RibeiroUniversidade Federal de Alagoas[ [email protected] ]

Mira MeziniTechnische Universität Darmstadt[ [email protected]]

Claus BrabrandIT University of Copenhagen[ [email protected] ]

Paulo BorbaUniversidade Federal de Pernambuco


"Statically Analyzing Software Product Lines in Minutes instead of Years"



true

[ (A B)∧ ¬A∧ ] ∨ [ true A∧ ]

= A

true A B∧

true ¬A∧ = ¬A

true

SPLLIFT

IFDS:A0:

λS . (S – {x}) {y}∪

{x}

{y}

SPLLIFT (IFDS ➞ IDE):A2:( {A} = {x} , {B} = {x} , {A,B} = {x,y} )

0 x y

0 x y

0 x y

0 x y

λS . (S – {x}) {y}∪A:#ifdef (A)

( {A} = {y} , {B} = {x} , {A,B} = {y} )

A ¬A¬A

LIFT:

■ Reps■ Horwitz■ Sagiv

fixed-pointiteration

graphreachability➔

¬A A

{x} {y}Ø

{x,y}






(feature sensitive)


ResultsResults for SPLLIFT (interprocedural analysis):

In fact, analyzing all valid configs is only slightly slower than analyzing one config !

Minutes instead of Years ! :-)

Reaching Definitions Possible Types Uninitialized VariablesSPL

benchmark# validconfigs

A1 SPLLIFT A1 SPLLIFT A1 SPLLIFT

Lampiro 4 3m30s 42s 13s 4s 3m09s 1m25s

MM 08 26 24m29s 59s 2m06s 3s 27m39s 2m13s

GPL 1,872 days 8m48s 9h03m39s 42s days 7m09s

Berkeley DB unknown years 12m04s years 24s years 10m18s






(feature sensitive)

Systematic Derivation of Static Analyses for Software Product LinesVar-Abs-IntJan Midtgaard

Aarhus University[ [email protected] ]

Claus BrabrandIT University of Copenhagen[ [email protected] ]

Andrzej WąsowskiIT University of Copenhagen[ [email protected] ]

I n p r o g r e s s . . .

"Systematic Derivation of Static Analyses for Software Product Lines"





Lifted Lifted

Lifted Lifted

Var-Abs-Int"Systematic Derivation of Analyses for SPLs":

...

Lifted

SPL

Questions ?

Thx


AbstractSoftware Product Lines (SPLs) developed using annotative approaches such as conditional compilation come with an inherent risk of constructing erroneous products. For this reason, it is essential to be able to analyze such SPLs. However, as dataflow analysis techniques are not able to deal with SPLs, developers must generate and analyze all valid products individually, which is expensive for non-trivial SPLs.We demonstrate how to take any standard dataflow analysis and automatically turn it into a feature-sensitive dataflow analysis in several qualitatively different ways. All analyses are capable of analyzing all valid products of an SPL without having to generate all of them explicitly.


Example SPL

config = {G}(¬F G ¬H)∧ ∧

customize(instantiate)(preprocess)

Software Product Line: Conventional Program:


Exploded Super Graph...for Program:


Lifted Exploded Super Graph...for SPL:


independent options...

> Earth's Population (233)

[ C. K

ästn

er ]

33

[ 28 ]"Static Analysis of Software Product Lines" Aug 9, 2013MT Lab Meeting> Atoms in Universe (2320)

[ C. K

ästn

er ]

independent options...320


[ C. K

ästn

er ]

10 000 configurableoptions...


Specification: A1, A2, A3, A4

A1

A2

A3

A4


A1, A2, A3, and A4A1 A2

A3 A4


Intraprocedural EvaluationFour (qualitatively different) SPL benchmarks:

Implementation: A1, A2, A3, A4 in SOOT + CIDEEvaluation: total time, analysis time, memory usage


EvaluationFive (qualitatively different) SPL benchmarks:


Results (intra)In practice:

4x

(Reaching Definitions)(Total time, incl. compile)

7x

3x

1x1x

(no re-compile!)

Feature sensitive(A1, A2, and A3)all faster than A0


Overview

A0 (brute force)

A1 (consecutive)

A2 (simultaneous)

A3 (shared)

A* (combo)

FAST

ER

(intra-procedural)

no re-compile!

caching!

sharing!

combo!

AOSD2012

TAOSD 2013


Beyond the Sum of all MethodsFor a method with K valid configurations, which of analyses A1 vs A2 vs A3 is fastest?

Statistically significant differences between A1, A2, and A3 for all N,except between A2 and A3 for N=4 (underlined above).

K #


Combo Analysis Strategy: A*Intraprocedurally combinedanalysis strategy, A*:

A* consistently fastest(combo!)


Overview (cont'd)

A0 (brute force)

A1 (consecutive)

A2 (simultaneous)

A3 (shared)

A* (combo)

SPLLIFT

FAST

ER

(intra-procedural)

PLDI 2013

IFDS (graph repr)

A3+BDD (esp. interprocedural)

no re-compile!

caching!

sharing!

combo!

graphencoding!

repr!AOSD2012

TAOSD 2013


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


Results (analysis time)In theory:

In practice:TIME(A4) : Depends on

degree of sharing in SPL !(caching!)

(Reaching Definitions) A3 (1.5x) fasterOn average (A2 vs A3):

A2

A3vs

2F


Results (memory usage)In theory:

In practice:(Reaching Definitions) 6.3 : 1Average

2F

A2

A3vs

SPACE(A4) : Depends ondegree of sharing in SPL !


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


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


Emerging Interfaces


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:


Results (analysis time)In theory:

In practice:TIME(A4) : Depends on

degree of sharing in SPL !

Nx1 ≠ 1xN?!

(caching!)

(Reaching Definitions) A3 (1.5x) fasterOn average (A2 vs A3):

A2

A3vs

2F 2F


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


IFDEF normalizationRefactor "undisciplined" (lexical) ifdefs into "disciplined" (syntactic) ifdefs:

Normalize "ifdef"s (by transformation):


Example Bug from LampiroLampiro SPL (IM client for XMPP protocol):

*** uninitialized variable "logo"(if feature "GLIDER" is defined)

Similar problems with:undeclared variables, unused variables, null pointers, ...


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


Formula ~ Set of ConfigurationsDefinitions (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} }


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


Conditional CompilationThe '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) { ...}


Lexical #ifdef Syntactic ifdefSimple 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


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


Var-Abs-Int

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