1 / 101

# Infinite State Model Checking with Presburger Arithmetic Constraints - PowerPoint PPT Presentation

Infinite State Model Checking with Presburger Arithmetic Constraints. Tevfik Bultan Department of Computer Science University of California, Santa Barbara. Joint Work with My Students. Action Language Verifier Tuba Yavuz-Kahveci (PhD 2004) Constantinos Bartzis (PhD 2004)

Related searches for Infinite State Model Checking with Presburger Arithmetic Constraints

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

## PowerPoint Slideshow about 'Infinite State Model Checking with Presburger Arithmetic Constraints' - johana

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Infinite State Model Checking with Presburger Arithmetic Constraints

Tevfik Bultan

Department of Computer Science

University of California, Santa Barbara

Joint Work with My Students Constraints

• Action Language Verifier

• Tuba Yavuz-Kahveci (PhD 2004)

• Constantinos Bartzis (PhD 2004)

• Design for verification

• Aysu Betin-Can (PhD 2005)

Infinite State Model Checking? Constraints

• Model checking started as a finite state verification technique

• Advantages of finite state systems:

• Exhaustive state enumeration is possible for finite state systems

• Disadvantages of infinite state systems:

• Verification problems that are computable for finite state systems are uncomputable for infinite state systems

Why Care About Infinity? Constraints

• Computer systems do not have infinite memory or infinite time

• So why care about infinity?

• Infinity is an abstraction

• Abstraction is at the core of computer science

• Computers are built with layers of abstractions

• Abstraction is necessary for design

• Abstraction is necessary for analysis

Why Care About Infinity? Constraints

• Reason 1:

• Enables us to check a specification with respect to an arbitrarily large number of components or memory

• For example, arbitrary number of threads

• Reason 2:

• Rather than developing verification techniques that rely on the bound of the state space to terminate

• Enables us to develop infinite state verification techniques that terminate independent of the bound

• A technique which is guaranteed to terminate is not helpful if it runs out of memory

An Example Constraints

• A simple example that demonstrates limitations of (finite state) model checkers

• Property P can be verified with an infinite state model checker that uses standard backward fixpoint computations

• Fixpoint computation for some properties

• for example, AG(State1  x 6)

may not converge but we can use conservative approximations

x’=x+1

State0

State1

x’=x+1

Initial: x=0  State0

P: AG(State1  x is odd)

P: AG(State1  ( . x =2+1))

Outline Constraints

• Model checking with arithmetic constraints

• Conservative approximations

• Automata representation for arithmetic constraints

• Composite representation

• Action Language Verifier (ALV)

• Checking synchronization in concurrent programs with ALV

Symbolic Model Checking Constraints[McMillan et al. LICS 1990]

• Represent sets of states and the transition relation as Boolean logic formulas

• Forward and backward fixpoints can be computed by iteratively manipulating these formulas

• Forward, backward image: Existential variable elimination

• Conjunction (intersection), disjunction (union) and negation (set difference), and equivalence check

• Use an efficient data structure for manipulation of Boolean logic formulas

• BDDs

Symbolic Model Checking Constraints

• What do you need to compute fixpoints?

Symbolic Conjunction(Symbolic,Symbolic)

Symbolic Disjunction(Symbolic,Symbolic)

Symbolic Negation(Symbolic)

Boolean EquivalenceCheck(Symbolic,Symbolic)

Symbolic Precondition(Symbolic)

• Precondition (i.e., EX) computation is handled by:

• variable renaming, followed by conjunction, followed by existential variable elimination

• Infinite state model checking: Use a symbolic representation that is capable of representing infinite sets and supports the above functionality

Linear Arithmetic Constraints Constraints

• Linear arithmetic formulas can represent (infinite) sets of valuations of unbounded integers

• Linear integer arithmetic formulas on can be stored as a set of polyhedra

where each is a linear equality or inequality constraint

and each is a polyhedron

xi integer variable, ai coefficient, c constant

A Linear Arithmetic Constraint Manipulator Constraints

• Omega Library [Pugh et al.]

• A tool for manipulating Presburger arithmetic formulas: First order theory of integers without multiplication

• Equality and inequality constraints are not enough

• Divisibility constraints are also needed

• which means:is divisible by

Integers are Complicated Constraints

y

x – 5  2y

2y  x – 1

x 3y

3y  x + 7

3

29

x

dark shadow

real shadow

Presburger Arithmetic Model Checking Constraints[Bultan et al. CAV’97, TOPLAS’99]

• Use linear arithmetic constraints as a symbolic representation

• Use a Presburger arithmetic manipulator as the symbolic engine (Omega library)

• Compute fixpoints to verify or falsify CTL properties

• Use conservative approximations to achieve convergence

• Arithmetic constraints on boundedinteger variables can be represented using BDDs

• Use a binary encoding

• represent integer x as x0x1x2... xk

• where x0, x1, x2, ... , xk are binary variables

• You have to be careful about the variable ordering!

• BDDs and constraint representations are both applicable

• Which one is better?

Arithmetic Constraints vs. BDDs Constraints[Bultan TACAS’00]

Arithmetic Constraints vs. BDDs Constraints

• Constraint based verification can be more efficient than BDDs for integers with large domains

• Constraint based verification can be used to automatically verify infinite state systems

• cannot be done using BDDs

• However, BDD-based verification is more robust and the arithmetic constraint representation has two problems:

• Problem 1: Constraint based verification does not scale well when there are boolean or enumerated variables in the specification

• Problem 2: Price of infinity

• CTL model checking becomes undecidable for infinite domains

Outline Constraints

• Model checking with arithmetic constraints

• Conservative approximations

• Automata representation for arithmetic constraints

• Composite representation

• Action Language Verifier (ALV)

• Checking synchronization in concurrent programs with ALV

Conservative Approximations Constraints

• Compute a lower ( p ) or an upper ( p+ ) approximation to the truth set of the property ( p )

Conservative Approximations Constraints

• Compute a lower ( p ) or an upper ( p+ ) approximation to the truth set of the property ( p )

• There are three possible outcomes:

p

p

I

1) “The property is satisfied”

Conservative Approximations Constraints

• Compute a lower ( p ) or an upper ( p+ ) approximation to the truth set of the property ( p )

• There are three possible outcomes:

I

p

p

p

I

p

1) “The property is satisfied”

3) “I don’t know”

sates which

violate the

property

I

p

p+

 p

2) “The property is false and here is a counter-example”

Conservative Approximations Constraints

• Compute a lower ( p ) or an upper ( p+ ) approximation to the truth set of the property ( p )

• There are three possible outcomes:

I

p

p

p

I

p

1) “The property is satisfied”

3) “I don’t know”

sates which

violate the

property

I

p

p+

 p

2) “The property is false and here is a counter-example”

Conservative Approximations Constraints

• Truncated fixpoint computations

• To compute a lower bound for a least-fixpoint computation

• Stop after a fixed number of iterations

• Widening

• To compute an upper bound for the least-fixpoint computation

• We use a generalization of the polyhedra widening operator by [Cousot and Halbwachs POPL’78]

Polyhedra Widening Constraints

y

x  y

y  5

1  y

x

x  4

Ai:x  y  x  4  y  5  1  y

Polyhedra Widening Constraints

x  y

y

x  y

y  5

y  5

1  y

1  y

x

x  5

x  4

Ai:x  y  x  4  y  5  1  y

Ai+1: x  y  x  5  y  5  1  y

Polyhedra Widening Constraints

y

x  y

y  5

1  y

x

x  5

x  4

Ai:x  y  x  4  y  5  1  y

Ai+1: x  y  x  5  y  5  1  y

AiAi+1: x  y  y  5  1  y

Polyhedra Widening Constraints

x  y

y

x  y

y  5

y  5

1  y

1  y

x

x  5

x  4

Ai:x  y  x  4  y  5  1  y

Ai+1: x  y  x  5  y  5  1  y

AiAi+1: x  y  y  5  1  y

• Ai Ai+1 is defined as:

• all the constraints in Ai

• that are also satisfied by Ai+1

Outline Constraints

• Model checking with arithmetic constraints

• Conservative approximations

• Automata representation for arithmetic constraints

• Composite representation

• Action Language Verifier (ALV)

• Checking synchronization in concurrent programs with ALV

Automata Representation for Arithmetic Constraints Constraints[Bartzis, Bultan CIAA’02, IJFCS ’02]

• Given an atomic linear arithmetic constraint in one of the following two forms

we can construct an FA which accepts all the solutions to the given constraint

• By combining such automata one can handle full Presburger arithmetic

Basic Construction Constraints

0

1

2

• We first construct a basic state machine which

• Reads one bit of each variable at each step, starting from the least significant bits

• and executes bitwise binary addition and stores the carry in each step in its state

In my figures alphabet symbols are written vertically!

0 1

0 0

/ /

0 1

0

1

/

1

1

0

/

0

0 1

1 1

/ /

0 1

Example

x + 2y

1

1

/

1

0

1

/

0

1

1

/

0

010

+ 2  001

0

0

/

1

0 1

0 0

/ /

0 1

1

0

0

Number of states:

Automaton Construction Constraints

• Equality With 0

• All transitions writing 1 go to a sink state

• State labeled 0 is the only accepting state

• For disequations (), state labeled 0 is the only rejecting state

• Inequality (<0)

• States with negative carries are accepting

• No sink state

• Non-zero Constant Term c

• Same as before, but now -c is the initial state

• If there is no such state, create one (and possibly some intermediate states which can increase the size by |c|)

Conjunction and Disjunction Constraints

0 0 1

0,1,1

0 1

0,1

1

0

Automaton for x-y<1

1

0

1

0

-1

0

1

0

1

0 0 1

0,1,1

0 0

0,1

0 0

0,1

0,-1

0

0

1

1

1

0

1

1 1

0,1

0

1

Automaton for x-y<1  2x-y>0

1

0

0 1

0,1

Automaton

for 2x-y>0

0 1

0,1

0 1

0,1

-1,-1

-1,0

-1

0

0

1

0

0

0

0

0

1

0

1

0

1

0

0 1

1,1

0 1

1,1

1

0

0 1 1

1,0,1

-2,-1

-2,0

-2,1

-2

0

1

1

0

1

1

• Conjunction and disjunction is handled by generating the product automaton

Other Extensions Constraints

• Existential quantification (necessary for pre and post)

• Project the quantified variables away

• The resulting FA is non-deterministic

• Determinization may result in exponential blowup of the FA size but we do not observe this in practice

• For universal quantification use negation

• Constraints on all integers

• Use 2’s complement arithmetic

• The basic construction is the same

• In the worst case the size doubles

Experiments Constraints

• We implemented these algorithms using MONA [Klarlund et al]

• We integrated them to our infinite state model checker

• We compared our automata representation against

• the polyhedral representation used in the Omega library

• the automata representation used in LASH [Boigelot and Wolper]

• we also integrated LASH to our model checker by writing a wrapper around it

Efficient Pre- and Post-condition Computations Constraints[Bartzis, Bultan CAV’03]

• Pre and post condition computations can cause an exponential blow-up in the size of the automaton in the worst case

• We do not observe this blow-up in the experiments

• We proved that for a common class of systems this blow up does not occur

Assumptions About the Transition Relation Constraints

• We assume that the transition relation of the input system is a disjunction of formulas in the following form

guard(R)  update(R)

where

• guard(R) is a Presburger formula on current state variables and

• update(R) is of the form

xi’=f(x1, …, xv)   xj’= xj

• In asynchronous concurrent systems the transition relation is usually in the above form

ji

Three Classes of Updates Constraints

• xi’ = c

• xi’ = xi + c

• xi’ = j=1aj· xj + c

We proved that

• Computation of pre is polynomial for all 3 cases

• Computation of post is polynomial for 2 and for 3, whenever ai is odd.

v

Other Results Related to Automata Encoding Constraints[Bartzis, Bultan TACAS’03, STTT]

• We developed efficient BDD construction algorithms and proved bounds for the sizes of the BDDs for bounded linear arithmetic constraints

• Given a linear arithmetic formula that contains n atomic constraints on v bounded integer variables represented with b-bits, the size of the BDD is:

• These results explain why all three versions of SMV (NuSMV, CMU SMV and Cadence SMV) are inefficient in handling linear arithmetic constraints

• In SMV the BDD size could be exponential in b

Other Results Related to Automata Encoding Constraints[CAV’04]

• We defined a widening operator for the automata representation of arithmetic constraints

• The widening operator looks for similar states in two consecutive iterations (Ai and Ai+1) and creates an equivalence relation

• then it merges the states in the same equivalence class

• We can prove that for some cases this widening operator computes the exact fixpoint

• for example for updates of the form x’=x+c

Example Constraints

• The sequence y=x, y=x  y=x+1, y=x  y=x+1  y=x+2, … does not converge

• However we know that a fixpoint exists (yx) and is representable as an arithmetic constraint

module incr_1

integer y;

parameterized integer x;

initial: y=x;

incr_1: y'=y+1;

spec: AG(y>=x)

endmodule

Widening Constraints

• Instead of computing a sequence A1, A2, … where

Ai+1=Aipost(Ai)

compute A’1, A’2, … where

A’i+1=A’i(A’ipost(A’i))

• By definition AB  AB

• The goal is to find a widening operator  such that:

• The sequence A’1, A’2, … converges

• It converges fast

• The computed fixpoint is as close as possible to the exact set of reachable states

Widening Arithmetic Automata Constraints

• Given automata A and A’ we want to compute AA’

• We say that states k and k’ are equivalent (kk’) if either

• k and k’ can be reached from either initial state with the same string (unless k or k’ is a sink state)

• or, the languages accepted from k and k’ are equal

• or, for some state k’’, kk’’ and k’k’’

• The states of AA’ are the equivalence classes of 

Example Constraints

0 1

0,1

X

X

y=x

1 0

0,1

0

0

1

0

1

1

3

0 1

0,1

0

y=x y=x+1

1 0

0,1

0 X

0,1

1

1

X

X

0

3

0

1

2

1

0

2

0 1

0,1

3

1

0

Example Constraints

0 1

0,1

0 1

0,1

X

X

1 0

0,1

0 X

0,1

1

X

X

1 0

0,1

0

1

0

3

0

1

1

0

2

0 1

0,1

1

0

0 X

0,1

1

0

X

X

=

1

0

0 1

0,1

0,1

2

3

0

1

Example Constraints

0

0,

X

1

1

0

X

X

1

0

0 1

0,1

0

2

0

0

1

2

0

1

1

1

3

2

4

0 X

0,1

0

X

1

1

0

1

0

X

X

0

0

2

1

0 1

0,1

0

3

4

0

1

2

0

1

3

1

0

1

0

1

4

X

0

Example Constraints

0 X

0,1

0

0,

X

1

1

0

X

X

1

0

X

X

X

1

1

0

0

0

2

1

0

0 1

0,1

0 1

0,1

0

1

2

0

3

4

0

1

0

1

0

1

1

0

1

0

1

X

0

0 X

0,1

X 1

0,0

=

1

0

Represents:

yx

0,2

1,3

0

1

Outline Constraints

• Model checking with arithmetic constraints

• Conservative approximations

• Automata representation for arithmetic constraints

• Composite representation

• Action Language Verifier (ALV)

• Checking synchronization in concurrent programs with ALV

Composite Model Checking Constraints[Bultan, Gerber, League ISSTA 98, TOSEM 00]

• Map each variable type to a symbolic representation

• Map boolean and enumerated types to BDD representation

• Map integer type to a linear arithmetic constraint representation

• Use a disjunctive representation to combine different symbolic representations: composite representation

• Each disjunct is a conjunction of formulas represented by different symbolic representations

• we call each disjunct a composite atom

Composite Representation Constraints

composite atom

symbolic

rep. 1

symbolic

rep. 2

symbolic

rep. t

Example:

x: integer, y: boolean

x>0 and x´x-1andy´orx<=0 and x´xandy´y

arithmetic constraint

representation

arithmetic constraint

representation

BDD

BDD

Composite Symbolic Library Constraints[Yavuz-Kahveci, Tuncer, Bultan TACAS01], [Yavuz-Kahveci, Bultan FroCos 02, STTT 03]

• Uses a common interface for each symbolic representation

• Easy to extend with new symbolic representations

• Enables polymorphic verification

• Multiple symbolic representations:

• As a BDD library we use Colorado University Decision Diagram Package (CUDD) [Somenzi et al]

• As an integer constraint manipulator we use Omega Library [Pugh et al]

Composite Symbolic Library Class Diagram Constraints

BoolSym

CompSym

IntSym

–representation: BDD

–representation: Polyhedra

–representation: list

of comAtom

+intersect()

+union()

+intersect()

+union()

+intersect()

+ union()

compAtom

–atom: *Symbolic

Symbolic

+intersect()

+union()

+complement()

+isSatisfiable()

+isSubset()

+pre()

+post()

CUDD Library

OMEGA Library

Pre and Post-condition Computation Constraints

Variables:

x: integer, y: boolean

Transition relation:

R:x>0 and x´x-1andy´orx<=0 and x´xandy´y

Set of states:

s:x=2and!yorx=0and!y

Compute post(s,R)

Pre and Post-condition Distribute Constraints

R:x>0 and x´x-1andy´orx<=0 and x´xandy´y

s:x=2and!yorx=0andy

post(s,R) =post(x=2 , x>0 and x´x-1)  post(!y , y´)

x=1 y

• post(x=2 , x<=0 and x´x)  post (!y , y´y)

false!y

• post(x=0 , x>0 and x´x-1)  post(y , y´)

falsey

• post (x=0 , x<=0 and x´x)  post (y, y´y )

x=0y

=x=1andy orx=0andy

Polymorphic Verifier Constraints

SymbolicTranSys::check(Node *f) {

Symbolics = check(f.left)

caseEX:

s.pre(transRelation)

caseEF:

do

sold = s

s.pre(transRelation)

s.union(sold)

while not sold.isEqual(s)

}

 Action Language Verifier

is polymorphic

 It becomes a BDD based model

checker when there or no integer variables

Composite Representation + Shape Graphs Constraints[Yavuz-Kahveci, Bultan SAS 02]

• Shape graphs represent the states of the heap

• Each node in the shape graph represents a dynamically allocated memory location

• Heap variables point to nodes of the shape graph

• The edges between the nodes show the locations pointed by the fields of the nodes

heap variables add and top

point to node n1

add.next is node n2

top.next is also node n2

add.next.next is null

add

top

next

n1

n2

next

Composite Symbolic Library: Further Extended Constraints

CompSym

–representation:

list of comAtom

+ union()

compAtom

ShapeGraph

–atom: *Symbolic

–atom: *Symbolic

Symbolic

+union()

+isSatisfiable()

+isSubset()

+forwardImage()

BoolSym

HeapSym

IntSym

–representation:

BDD

–representation:

list of ShapeGraph

–representation:

list of Polyhedra

+union()

+union()

+union()

CUDD Library

OMEGA Library

Forward Fixpoint Constraints

add

top

pc=l2  mutex

• numItems=2

add

top

pc=l4  mutex

• numItems=2

add

top

pc=l1  mutex

• numItems=3

arithmetic constraint

representation

A set of shape graphs

BDD

add

top

pc=l1  mutex

• numItems=2

Post-condition Computation: Example Constraints

top’=add

add

top

add

top

set of

states

pc=l4  mutex

• numItems=2

transition

relation

pc=l4 and mutex’

pc’=l1

numItems’=numItems+1

pc=l1  mutex

• numItems=3

Again: Fixpoints Do Not Converge Constraints

• We have two reasons for non-termination

• integer variables can increase without a bound

• the number of nodes in the shape graphs can increase without a bound

• As I mentioned earlier, we use widening on integer variables to achieve convergence

• For heap variables we use the summarization operation

Summarization Example Constraints

add

top

pc=l1  mutex

• numItems=3

summarized nodes

After summarization, it becomes:

add

top

pc=l1  mutex

• numItems=3  summarycount=2

a new integer variable

representing the number

of concrete nodes encoded

by the summary node

summary node

Simplification Constraints

add

top

• numItems=3

•  summaryCount=2

pc=l1  mutex

add

• numItems=4

•  summaryCount=3

top

pc=l1  mutex

=

add

• (numItems=4

• summaryCount=3

• numItems=3

 summarycount=2)

top

pc=l1  mutex

Simplification On the Integer Part Constraints

add

• (numItems=4

• summaryCount=3

• numItems=3

 summaryCount=2)

top

pc=l1  mutex

=

add

top

• numItems=summaryCount+1

• 3  numItems

• numItems  4

pc=l1  mutex

Then We Use Integer Widening Constraints

add

top

• numItems=summaryCount+1

• 3  numItems

• numItems  4

pc=l1  mutex

add

top

• numItems=summaryCount+1

• 3  numItems

• numItems  5

pc=l1  mutex

=

add

top

• numItems=summaryCount+1

• 3  numItems

pc=l1  mutex

Now, fixpoint converges

Verified Properties Constraints

Verifying Linked Lists with Multiple Fields Constraints

• Pattern-based summarization

• User provides a graph grammar rule to describe the summarization pattern

L x = next x y, prev y x, L y

• Represent any maximal sub-graph that matches the pattern with a summary node

• no node in the sub-graph pointed by a heap variable

Summarization Pattern Examples Constraints

...

n

n

n

L x  x.n = y, L y

...

n

n

n

L x  x.n = y, y.p = x, L y

p

p

p

...

n

n

n

L x  x.n = y, x.d = z, L y

d

d

d

Outline Constraints

• Model checking with arithmetic constraints

• Conservative approximations

• Automata representation for arithmetic constraints

• Composite representation

• Action Language Verifier (ALV)

• Checking synchronization in concurrent programs with ALV

Action Language Tool Set Constraints

Action Language

Verifier (ALV)

Action Language

Specification

Action Language

Parser

Composite Symbolic Library

Omega

Library

CUDD

Package

MONA

Verified

Counter example

Presburger

Arithmetic

Manipulator

BDD

Manipulator

Automata

Manipulator

Action Language Constraints[Bultan, ICSE 00], [Bultan, Yavuz-Kahveci, ASE 01]

• Variables: boolean, enumerated, integer (unbounded)

• Parameterized constants

• specifications are verified for all possible values

• Transition relation is defined using actions and modules

• Atomic actions: Predicates on current and next state variables

• Action composition:

• asynchronous (|) or synchronous (&)

• A module is defined as asynchronous and/or synchronous compositions of its actions and submodules

module main() Constraints

integernr;

booleanbusy;

restrict:nr>=0;

initial: nr=0 and !busy;

module Reader()

boolean reading;

initial: !reading;

rEnter: !reading and !busy and

nr’=nr+1 and reading’;

rExit: reading and !reading’ and nr’=nr-1;

Reader: rEnter | rExit;

endmodule

module Writer()

...

endmodule

main: Reader() | Reader() | Writer() | Writer();

spec: invariant(busy => nr=0)

endmodule

Readers Writers Example: A Closer Look

S : Cartesian product of

variable domains defines

the set of states

I : Predicates defining

the initial states

R : Atomic actions of the

Reader

R : Transition relation of Reader defined as asynchronous composition of its atomic actions

R : Transition relation of main defined as asynchronous composition of two Reader and two Writer processes

Arbitrary Number of Threads Constraints

• Counting abstraction

• Create an integer variable for each local state of a thread

• Each variable will count the number of threads in a particular state

• Local states of the threads have to be finite

• Specify only the thread behavior that relates to the correctness of the controller

• Shared variables of the controller can be unbounded

• Counting abstraction can be automated

module main() Constraints

integer nr;

boolean busy;

parameterized integer numReader, numWriter;

restrict: nr>=0 and numReader>=0 and numWriter>=0;

initial: nr=0 and !busy;

module Reader()

integer readingF, readingT;

initial: readingF=numReader and readingT=0;

rEnter: readingF>0 and !busy and

nr’=nr+1 and readingF’=readingF-1 and

readingT’=readingT+1;

rExit: readingT>0 and nr’=nr-1 readingT’=readingT-1 and readingF’=readingF+1;

Reader: rEnter | rExit;

endmodule

module Writer()

...

endmodule

main: Reader() | Writer();

spec: invariant(busy => nr=0)

endmodule

Readers-Writers After Counting Abstraction

Parameterized constants introduced by the counting abstractions

Variables introduced by the counting abstractions

Outline Constraints

• Model checking with arithmetic constraints

• Conservative approximations

• Automata representation for arithmetic constraints

• Composite representation

• Action Language Verifier (ALV)

• Checking synchronization in concurrent programs with ALV

Design for Verification Constraints

Action

Language

Verifier

Design for

Verification

enables

uses

Verification of

Synchronization in

Concurrent

Programs

A Java Read-Write Lock Implementation Constraints

How do we translate this to

Action Language?

class ReadWriteLock {private Object lockObj;private int totalReadLocksGiven;private boolean writeLockIssued;private int threadsWaitingForWriteLock;public ReadWriteLock() {    lockObj = new Object();    writeLockIssued = false;  }  public void getReadLock() {synchronized (lockObj) {while ((writeLockIssued) || (threadsWaitingForWriteLock != 0)) {try {          lockObj.wait();        } catch (InterruptedException e) {        }      }      totalReadLocksGiven++;    }  }public void getWriteLock() {synchronized (lockObj) {      threadsWaitingForWriteLock++;while ((totalReadLocksGiven != 0) || (writeLockIssued)) {try {          lockObj.wait();        } catch (InterruptedException e) {          //        }      }      threadsWaitingForWriteLock--;      writeLockIssued = true;    }  }public void done() {synchronized (lockObj) {      //check for errorsif ((totalReadLocksGiven == 0) && (!writeLockIssued)) {        System.out.println(" Error: Invalid call to release the lock");return;      }if (writeLockIssued)        writeLockIssued = false;else        totalReadLocksGiven--;      lockObj.notifyAll();    }  }}

Action

Language

Verifier

Verification of

Synchronization in

Java

Programs

Design for Verification Constraints

• Abstraction and modularity are key both for successful designs and scalable verification techniques

• The question is:

• How can modularity and abstraction at the design level be better integrated with the verification techniques which depend on these principles?

• Our approach:

• Structure software in ways that facilitate verification

• Document the design decisions that can be useful for verification

• Improve the applicability and scalability of verification using this information

A Design for Verification Approach Constraints

We have been investigating a design for verification approach based on the following principles:

• Use of design patterns that facilitate automated verification

• Use of stateful, behavioral interfaces which isolate the behavior and enable modular verification

• An assume-guarantee style modular verification strategy that separates verification of the behavior from the verification of the conformance to the interface specifications

• A general model checking technique for interface verification

• Domain specific and specialized verification techniques for behavior verification

• Avoids usage of error-prone Java synchronization primitives:

• synchronize, wait, notify

• Separates controller behavior from the threads that use the controller:

• Supports a modular verification approach which exploits this modularity

• for scalable verification

class Action{ Constraints

protected final Object owner; …

private boolean GuardedExecute(){

boolean result=false;

for(int i=0; i<gcV.size(); i++) try{

if(((GuardedCommand)gcV.get(i)).guard()){

((GuardedCommand)gcV.get(i)).update();

result=true; break; }

}catch(Exception e){}

return result;

}

public void blocking(){

synchronized(owner) {

while(!GuardedExecute()) {

try{owner.wait();}

catch (Exception e){} }

owner.notifyAll(); }

}

public boolean nonblocking(){

synchronized(owner) {

boolean result=GuardedExecute();

if (result) owner.notifyAll();

return result; }

}

}

class RWController implements RWInterface{

int nR; boolean busy;

final Action act_r_enter, act_r_exit;

final Action act_w_enter, act_w_exit;

RWController() {

...

gcs = new Vector();

gcs.add(new GuardedCommand() {

public boolean guard(){

return (nR == 0 && !busy);}

public void update(){busy = true;}}

);

act_w_enter = new Action(this,gcs);

}

public void w_enter(){

act_w_enter.blocking();}

public boolean w_exit(){

return act_w_exit.nonblocking();}

public void r_enter(){

act_r_enter.blocking();}

public boolean r_exit(){

return act_r_exit.nonblocking();}

}

Reader-Writer Controller

This helper class is provided.

No need to rewrite it!

A controller interface defines the Constraintsacceptable call sequences for the threads that use the controller

Interfaces are specified using finite state machines

public class RWStateMachine implements RWInterface{

StateTable stateTable;

final static int idle=0,reading=1, writing=2;

public RWStateMachine(){ ... stateTable.insert("w_enter",idle,writing);

}

public void w_enter(){

stateTable.transition("w_enter");

}

...

}

Controller Interfaces

reading

r_enter

r_exit

idle

w_exit

writing

w_enter

Verification Framework Constraints

Behavior

Verification

Action

Language

Verifier

Controller

Behavior

Machine

Controller

Classes

Counting

Abstraction

Concurrent

Program

Controller

Interface

Machine

Interface

Verification

Java

Path Finder

Thread

Thread

Isolation

Thread

Thread

Class

Thread

Classes

Modular Design / Modular Verification Constraints

Thread Modular Interface Verification

Concurrent Program

Thread 1

Thread 2

Thread n

Thread 1

Thread 2

Thread n

Interface

Machine

Interface

Machine

Interface

Machine

Interface

Controller

Modular

Behavior

Verification

Shared

Data

Controller Behavior

Behavior Verification Constraints

• Analyzing properties (specified in CTL) of the synchronization policy encapsulated with a concurrency controller and its interface

• Verify the controller properties assuming that the user threads adhere to the controller interface

• Behavior verification with Action Language Verifier

• We wrote a translator which translates controller classes to Action Language

• Using counting abstraction we can check concurrency controller classes for arbitrary number of threads

Interface Verification Constraints

• A thread is correct with respect to an interface if all the call sequences generated by the thread can also be generated by the interface machine

• Checks if all the threads invoke controller methods in the order specified in the interfaces

• Checks if the threads access shared data only at the correct interface states

• Interface verification with Java PathFinder

• Verify Java implementations of threads

• Correctness criteria are specified as assertions

• Look for assertion violations

• Assertions are in the StateMachine and SharedStub

• Performance improvement with thread Isolation

Thread Isolation: Part 1 Constraints

• Interaction among threads

• Threads can interact with each other in only two ways:

• invoking controller actions

• invoking shared data methods

• To isolate the threads

• Replace concurrency controllers with controller interface state machines

• Replace shared data with shared stubs

Thread Isolation: Part 2 Constraints

• Interaction among a thread and its environment

• Modeling thread’s call to its environment with stubs

• File I/O, updating GUI components, socket operations, RMI call to another program

• Replace with pre-written or generated stubs

• Modeling the environment’s influence on threads with drivers

• Thread initialization, RMI events, GUI events

• Enclose with drivers that generate all possible events that influence controller access

Automated Airspace Concept Constraints

• Automated Airspace Concept by NASA researchers automates the decision making in air traffic control

• The most important challenge is achieving high dependability

• Automated Airspace Concept includes a failsafe short term conflict detection component called Tactical Separation Assisted Flight Environment (TSAFE)

• It is responsible for detecting conflicts in flight plans of the aircraft within 1 minute from the current time

• Dependability of this component is even more important than the dependability of the rest of the system

• It should be a smaller, isolated component compared to the rest of the system so that it can be verified

TSAFE Constraints

TSAFE functionality:

• Display aircraft position

• Display aircraft planned route

• Display aircraft future projected route trajectory

• Show conformance problems

TSAFE Architecture Constraints

Computation

Feed Parser

Timer

Flight

Database

Graphical

Client

<<TCP/IP>>

User

Radar feed

Server

Client

EventThread

<<RMI>>

21,057 lines of code with 87 classes

Behavior Verification Performance Constraints

P denotes parameterized verification for arbitrary number of threads

Fault Categories Constraints

• Concurrency controller faults

• initialization faults (2)

• guard faults (2)

• update faults (6)

• blocking/nonblocking faults (4)

• Interface faults

• modified-call faults (8)

• conditional-call faults

• conditions based on existing program variables (13)

• conditions on new variables declared during fault seeding (5)

Effectiveness in Finding Faults Constraints

• Created 40 faulty versions of TSAFE

• Each version had at most one interface fault and at most one behavior fault

• 14 behavior and 26 interface faults

• Among 14 behavior faults ALV identified 12 of them

• 2 uncaught faults were spurious

• Among 26 interface faults JPF identified 21 of them

• 2 of the uncaught faults were spurious

• 3 of the uncaught faults were real faults that were not caught by JPF

Conclusions Constraints

• Infinite state model checking is feasible

• Enables verification of specifications with unbounded variables

• Enables verification of parameterized systems

• Infinite state verification techniques can help us in identifying more efficient finite state model checking techniques

• Building extensible tools is important!

Conclusions Constraints

• Application of automated verification techniques to real-world systems leads to re-thinking the design

• Design for verification can lead to more effective verification

• Integrating verification tools lead to more effective verification

• In our case ALV and JPF

Conclusions Constraints

• We were able to use our design for verification approach based on design patterns and behavioral interfaces in different domains

• Use of domain specific behavior verification techniques has been very effective

• Interface verification was the bottleneck

• Model checking research resulted in various verification techniques and tools which can be customized for specific classes of software systems

• Automated verification techniques can scale to realistic software systems using design for verification approach

THE END Constraints