Tools for Automated Verification of Concurrent Software Tevfik Bultan Department of Computer Science University of California, Santa Barbara

Tools for Automated Verification of Concurrent Software Tevfik Bultan Department of Computer Science University of California, Santa Barbara bultan@cs.ucsb.edu http://www.cs.ucsb.edu/~bultan/ http://www.cs.ucsb.edu/~bultan/composite/

Summary Goal: Building reliable concurrent software Sub-goals: –Developing reliable concurrency controllers in Java –Developing reliable concurrent linked lists Approach: Model Checking Specification Language: Action Language Tools: –Composite Symbolic Library –Action Language Verifier

Students Joint work with my students: Tuba Yavuz-Kahveci Constantinos Bartzis

Outline Difficulties in concurrent programming Action Language Composite Symbolic Library Application to concurrency controllers Application to concurrent linked lists Related work Current and future work

Difficulties in Concurrent Programming Concurrent programming is difficult and error prone –In sequential programming you only worry about the states of the variables –In concurrent programming you also have to worry about the states of the threads State space increases exponentially with the number of threads

Concurrent Programming in Java Java uses a variant of monitor programming Synchronization using locks –Each object has a lock synchronized(o) {... } Coordination using condition variables –Objects can be used as condition variables synchronized (condVar){ while (!condExp) wait(condVar);... notifyAll(condVar); }

Dangers in Java Concurrency Nested locks synchronized m(other) { other.m(); } Thread1: run() { o1.m(o2); } Thread2: run() { o2.m(o1); } o1 lock o2 lock Thread1Thread2

Dangers in Java Concurrency Missed notification notify(condVar); Forgotten condition check if(!condExp) wait(condVar); Dependency among multiple condition variables can be complicated –Conservative notification and condition check Inefficient –Optimizing the notification and condition checks Error prone

A simplified model of Seattle Tacoma International Airport from [Zhong 97] Example: Airport Ground Traffic Control Simulation

Control Logic An airplane can land using 16R only if no airplane is using 16R at the moment An airplane can takeoff using 16L only if no airplane is using 16L at the moment An airplane taxiing on one of the exits C3-C8 can cross runway 16L only if no airplane is taking off at the moment An airplane can start using 16L for taking off only if none of the crossing exits C3-C8 is occupied at the moment (arriving airplanes have higher priority) Only one airplane can use a taxiway at a time

Java Implementation Simulate behavior of each airplane with a thread Use a monitor (a Java class) –private variables for number of airplanes on each runway and each taxiway –methods of the monitor enforce the control logic Each thread calls the methods of the monitor based on the airport layout to move from one point to the next

Example Implementation public synchronized void C8_To_B11A() { while (!((numRW16L == 0) && (numB11A == 0))) wait(); numC8 = numC8 - 1; numB11A = numB11A + 1; notifyAll(); } This code is not efficient since every thread wakes up every other thread Using separate condition variables complicates the synchronization –nested locks

Difficulties In Implementing Concurrent Linked Lists Linked list manipulation is difficult and error prone –State of the heap: unbounded State space: –Sequential programming states of the variables –Concurrent programming states of the variables states of the threads –Concurrent linked lists states of the variables states of the threads state of the heap

Examples singly linked lists doubly linked lists stack queue single lock double lock –allows concurrent inserts and deletes next next n1n2 prev next n1n2 next prev next next n1n2top next next n1n2first last

Action Language Tool Set Action Language Parser Verifier Code Generator OmegaLibraryCUDDPackage Verified code (Java monitor classes) MONA Composite Symbolic Library PresburgerArithmeticManipulatorBDDManipulatorAutomataManipulator Action Language Specification

Action Language [Bultan, ICSE 00], [Bultan, Yavuz-Kahveci, ASE 01] A state based language –Actions correspond to state changes States correspond to valuations of variables –boolean –enumerated –integer (possibly unbounded) –heap variables (i.e., pointers) Parameterized constants –specifications are verified for every possible value of the constant

Action Language Transition relation is defined using actions –Atomic actions: Predicates on current and next state variables –Action composition: asynchronous (|) or synchronous (&) Modular –Modules can have submodules –A module is defined as asynchronous and/or synchronous compositions of its actions and submodules

Readers Writers Example module main() integer nr; boolean busy; 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 S : Cartesian product of variable domains defines variable domains defines the set of states the set of states I : Predicates defining the initial states the initial states R : Atomic actions of the Reader 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

Which Symbolic Representation to Use? BDDs canonical and efficient representation for Boolean logic formulas can only encode finite sets Linear Arithmetic Constraints can encode infinite sets two representations –polyhedral representation –automata representation mapping booleans to integers is not an efficient encoding F F F T T x  y  {(T,T), (T,F), (F,T)} a > 0  b = a+1  {(1,2), (2,3), (3,4),...} T x y

Composite Model Checking [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 symbolic rep. 1 symbolic rep. 2 symbolic rep. t composite atom Example: x: integer, y: boolean x>0 and x´  x-1 and y´ or x<=0 and x´  x and y´  y arithmetic constraint representation BDD arithmetic constraint representation BDD

Composite Symbolic Library [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 CUDD LibraryOMEGA Library Symbolic +intersect() +union() +complement() +isSatisfiable() +isSubset() +pre() +post() CompSym –representation: list of comAtom +intersect() + union() BoolSym –representation: BDD +intersect() +union() IntSym –representation: Polyhedra +intersect() +union() compAtom –atom: *Symbolic

Pre and Post-condition Computation Variables: x: integer, y: boolean Transition relation: R: x>0 and x´  x-1 and y´ or x<=0 and x´  x and y´  y Set of states: s: x=2 and !y or x=0 and !y Compute post(s,R)

Pre and Post-condition Distribute R: x>0 and x´  x-1 and y´ or x<=0 and x´  x and y´  y s: x=2 and !y or x=0 and y 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´ ) false y  post ( x=0, x<=0 and x´  x )  post ( y, y´  y ) x=0 y = x=1 and y or x=0 and y

Temporal Properties  Fixpoints [Emerson and Clarke 80] pppp Initialstates initial states that satisfy EF(  p) initial states that violate AG(p)  initial states that violate AG(p) EF(  p)states that can reach  p  p Pre(  p) Pre(Pre(  p))... EF(  p)  states that can reach  p   p  Pre(  p)  Pre(Pre(  p)) ... EG(  p) Initialstates initial states that satisfy EG(  p) initial states that violate AF(p)  initial states that violate AF(p) EG(  p) states that can avoid reaching p  p Pre(  p) Pre(Pre(  p))... EG(  p)  states that can avoid reaching p   p  Pre(  p)  Pre(Pre(  p)) ... EF(  p)

Polymorphic Verifier Symbolic TranSys::check(Node *f) { Symbolic s = check(f.left) case EX: s.pre(transRelation) case EF: 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

Fixpoints May Not Converge Integer variables can increase without a bound –state space is infinite Model checking is undecidable for systems with unbounded integer variables We use conservative approximations

Conservative Approximations Compute a lower ( p  ) or an upper ( p + ) approximation to the truth set of the property ( p ) Action Language Verifier can give three answers: I p pppp 1) “The property is satisfied” I p 3) “I don’t know” 2) “The property is false and here is a counter-example” I p  p p p p sates which violate the property p+p+p+p+ pppp

Conservative Approximations 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’77]

Widening Widening operation with composite representation: –Given two composite atoms c 1 and c 2 in consecutive fixpoint iterates, assume that c 1 = b 1  i 1 c 2 = b 2  i 2 where b 1 = b 2 and i 1  i 2 Assume that i 1 is a single polyhedron and i 2 is also a single polyhedron We find pairs of composite atoms which satisfy this criteria

Widening Assuming that i 1 and i 2 are conjunctions of atomic constraints (i.e., polyhedra), then i 1  i 2 is defined as: all the constraints in i 1 which are also satisfied by i 2 Example: i 1 = 0  count  count  2 i 2 = 0  count  count  3 i 1  i 2 = 0  count Replace i 2 with i 1  i 2 in c 2 This generates an upper approximation for the least fixpoint computation This constraint is not satisfied by i 2 so we drop it

Composite Symbolic Library with Automata Encoding OMEGA Library Symbolic +union() +isSatisfiable() +isSubset() +forwardImage() CompSym –representation: list of comAtom + union() compAtom –atom: *Symbolic IntSymAuto –representation: automaton +union() IntSym –representation: list of Polyhedra +union() CUDD Library BoolSym –representation: BDD +union() MONA IntBoolSymAuto –representation: automaton +union()

Automata Representation for Arithmetic Constraints [Bartzis, Bultan CIAA’02, IJFCS ’02] Given an atomic linear arithmetic constraint in one of the following two forms we construct an FA which accepts all the solutions to the given constraint By combining such automata one can handle full Presburger arithmetic

Basic Construction 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 012 0 1 0 / 0 1 01/101/1 1 / 0 1 0 / 0 1 1 / 1 11/011/0 00/100/1 Example x + 2y 010 + 2  001 100 01/001/0 1 0 / 0 Number of states:

Automaton Construction 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 0 0 1 0,1,1 0 1 0,1 1010 1010 1010 0101 0 0 1 0,1,1 Automaton for x-y<1 0 1 0 0,1 0 1 0,1 0 1 1 1,0,1 1 0,1 0101 1010 Automaton for 2x-y>0 0 -2 1111 0101 1010 0000 0 0,1 1111 1010 1010 0101 0 1 0,1 0 1 1,1 1010 0000 0000 0 1 0,1 1010 0101 0 1 1,1 1010 Automaton for x-y 0 -1,-1 0,-1 -2,-1 -1,0 -2,0-2,1 Conjunction and disjunction is handled by generating the product automaton

Other Extensions 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 We implemented these algorithms using MONA [Klarlund et al] Integrated them to the Action Language Verifier We verified a large number of specification examples We compared our representation against –the polyhedral representation used in the Omega library –the automata representation used in LASH we also integrated LASH to the Composite Symbolic Library using a wrapper around it

Experimental results

Efficient Pre- and Post-condition Computations [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 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 x i ’=f(x 1, …, x v )   x j ’= x j In asynchronous concurrent systems the transition relation is usually in the above form jiji

Three Classes of Updates 1.x i ’ = c 2.x i ’ = x i + c 3.x i ’ =  j=1 a j · x j + c We proved that Computation of pre is polynomial for all 3 cases Computation of post is polynomial for 2 and for 3, whenever a i is odd. v

Other Results Related to Automata Encoding [Bartzis, Bultan TACAS’03, STTT, CAV’04] We developed efficient algorithms for BDD construction for bounded linear arithmetic constraints –We showed that all three versions of SMV (NuSMV, CMU SMV and Cadence SMV) are inefficient in handling linear arithmetic constraints We defined a widening operator for the automata representation of arithmetic constraints –We can prove that for some cases this widening operator computes the exact fixpoint (for example for updates of the form x’=x+c)

SMV Is Inefficient for Linear Arithmetic Constraints

Application to Concurrency Controllers [ Yavuz-Kahveci, Bultan ISTTA 02] Outline of our approach: 1.Specify concurrency controllers and concurrent linked lists in Action Language 2.Verify their properties using composite model checking 3.Generate optimized Java classes from the specifications which preserve their properties

Readers-Writers Controller module main() integer nr; boolean busy; 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() boolean writing; initial: !writing; wEnter: !writing and nr=0 and !busy and busy’ and writing’; wExit: writing and !writing’ and !busy’; Writer: wEnter | wExit; endmodule main: Reader() | Reader() | Writer() | Writer(); spec: invariant([busy => nr=0]) endmodule

Arbitrary Number of Threads 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

Readers-Writers After Counting Abstraction module main() 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 Variables introduced by the counting abstractions Parameterized constants introduced by the counting abstractions

Verification of Readers-Writers Controller IntegersBooleansCons. Time (secs.) Ver. Time (secs.) Memory (Mbytes) RW-4150.040.016.6 RW-8190.080.017 RW-161170.190.028 RW-321330.530.0310.8 RW-641651.710.0620.6 RW-P710.050.019.1 SUN ULTRA 10 (768 Mbyte main memory)

What about the Java Implementation? We can automatically generate code from the controller specification –Generate a Java class –Make shared variables private variables –Use synchronization to restrict access Is the generated code efficient? –Yes! –We can synthesize the condition variables automatically –There is no unnecessary thread notification

Specific Notification Pattern [Cargill 96] public class ReadersWriters{ private int nr; private boolean busy; private Object rEnterCond, wEnterCond; private synchronized boolean Guard_rEnter() { if (!busy) { nr++; return true; } else return false; } public void rEnter() { synchronized(rEnterCond) { while(!Guard_rEnter()) rEnterCond.wait(); } public void rExit() { synchronized(this) { nr--; } synchronized(wEnterCond) { wEnterCond.notify(); } }... } All condition variables and wait and signal operations are generated automatically rEnter: !reading and !busy and nr’=nr+1 and reading’;

A simplified model of Seattle Tacoma International Airport from [Zhong 97] Example: Airport Ground Traffic Control

Action Language Specification module main() integer numRW16R, numRW16L, numC3,...; initial: numRW16R=0 and numRW16L=0 and...; module Airplane() enumerated pc {arFlow, touchDown, parked, depFlow, taxiTo16LC3,..., taxiFr16LB2,..., takeoff}; initial: pc=arFlow or pc=parked; reqLand: pc=arFlow and numRW16R=0 and pc’=touchDown and numRW16R’=numRW16R+1; exitRW3: pc =touchDown and numC3=0 and numC3’=numC3+1 and numRW16R’=numRW16R-1 and pc’=taxiTo16LC3;... Airplane: reqLand | exitRW3 |...; endmodule main: AirPlane() | Airplane() | Airplane() |....; spec: AG(numRW16R  1 and numRW16L  1) spec: AG(numC3  1) spec: AG((numRW16L=0 and numC3+numC4+...+numC8>0) => AX(numRW16L=0)) endmodule

Airport Ground Traffic Control Action Language specification –Has 13 integer variables –Has 6 Boolean variables per airplane process to keep the local state of each airplane –20 actions Automatically generated Java monitor class –Has 13 integer variables –Has 14 condition variables –Has 34 methods

Experiments ProcessesConstruction(sec)Verify-P1(sec)Verify-P2(sec)Verify-P3(sec) 20.810.420.280.69 41.500.780.501.13 83.031.530.992.22 166.863.022.035.07 2A,PD1.020.640.430.83 4A,PD1.941.190.811.39 8A,PD3.952.281.542.59 16A,PD8.744.63.155.35 PA,2D1.671.310.883.94 PA,4D3.152.421.715.09 PA,8D6.404.643.327.35 PA,16D13.669.217.0212.01 PA,PD2.650.990.570.43 A: Arriving Airplane D: Departing Airplane P: Arbitrary number of threads

Efficient Java Implementation public class airport { private int numRW16R; private int numRW16L; private int numC3;.... private Object CondreqLand; private Object CondexitRW3;... public airport() { numRW16R = 0 ; numRW16L = 0 ;... } private synchronized boolean Guarded_reqLand(){ if(numRW16R == 0) { numRW16R = numRW16R + 1; return true; }else return false ; } public void reqLand(){ synchronized(CondreqLand){ while (! Guarded_reqLand()){ try{ CondreqLand.wait(); } catch(InterruptedException e){;} }

Heap Type [Yavuz-Kahveci, Bultan SAS 02] Heap type in Action Language heap {next} top; Heap type represents dynamically allocated storage top’=new; We need to add a symbolic representation for the heap type to the Composite Symbolic Library numItems > 2 => top.next != null

Concurrent Stack module main() heap {next} top, add, get, newTop; boolean mutex; integer numItems; initial: top=null and mutex and numItems=0; module push() enumerated pc {l1, l2, l3, l4}; initial: pc=l1 and add=null; push1: pc=l1 and mutex and !mutex’ and add’=new and pc’=l2; push2: pc=l2 and numItems=0 and top’=add and numItems’=1 and pc’=l3; push3: pc=l3 and top’.next =null and mutex’ and pc’=l1; push4: pc=l2 and numItems!=0 and add’.next=top and pc’=l4; push5: pc=l4 and top’=add and numItems’=numItems+1 and mutex’ and pc’=l1; push: push1 | push2 | push3 | push4 | push5; endmodule module pop()... endmodule main: pop() | pop() | push() | push() ; spec:AG(mutex =>(numItems=0 top=null)) spec: AG(mutex => (numItems>2 => top->next!=null)) endmodule

Shape Graphs 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 add top next next n1n2 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

Composite Symbolic Library: Further Extended CUDD LibraryOMEGA Library Symbolic +union() +isSatisfiable() +isSubset() +forwardImage() CompSym –representation: list of comAtom + union() BoolSym –representation: BDD +union() compAtom –atom: *Symbolic HeapSym –representation: list of ShapeGraph +union() IntSym –representation: list of Polyhedra +union() ShapeGraph –atom: *Symbolic

Forward Fixpoint pc=l1  mutex numItems=2 add top   pc=l2   mutex  numItems=2 addtopBDD arithmetic constraint representation A set of shape graphs  pc=l4   mutex  numItems=2  addtop  pc=l1  mutex  numItems=3  addtop

Post-condition Computation: Example pc=l4   mutex  numItems=2  addtop pc=l4 and mutex’ pc’=l1 pc=l1  mutex numItems’=numItems+1  numItems=3 top’=add   addtop set of states transitionrelation

Again: Fixpoints Do Not Converge 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 The nodes that form a chain are mapped to a summary node No heap variable points to any concrete node that is mapped to a summary node Each concrete node mapped to a summary node is only pointed by a concrete node which is also mapped to the same summary node During summarization, we also introduce an integer variable which counts the number of concrete nodes mapped to a summary node

Summarization Example pc=l1  mutex numItems=3 add top   pc=l1  mutex numItems=3  summarycount=2 add top  summary node a new integer variable representing the number of concrete nodes encoded by the summary node After summarization, it becomes: summarized nodes

Simplification pc=l1  mutex  numItems=3  summaryCount=2 addtop pc=l1  mutex add top   numItems=4  summaryCount=3  = pc=l1  mutex add top   (numItems=4  summaryCount=3  numItems=3  summarycount=2)

Simplification On the Integer Part pc=l1  mutex add top   (numItems=4  summaryCount=3  numItems=3  summaryCount=2) = pc=l1  mutex add top  numItems=summaryCount+1  3  numItems  numItems  4

Then We Use Integer Widening pc=l1  mutex 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 = Now, fixpoint converges

Verified Properties SpecificationVerified Invariants Stack top=null  numItems=0 top  null  numItems  0 numItems=2  top.next  null Single Lock Queue head=null  numItems=0 head  null  numItems  0 (head=tail  head  null)  numItems=1 head  tail  numItems  0 Two Lock Queue numItems>1  head  tail numItems>2  head.next  tail

Experimental Results Number of Threads Queue HC Queue IC Stack HC Stack IC 2Lock Queue HC 2Lock Queue IC 1P-1C10.1912.954.575.2160.558.13 2P-2C15.7421.646.738.2488.26122.47 4P-4C31.5546.512.7115.11  1P-PC12.8513.625.615.73  PP-1C18.2419.436.486.82  HC : heap control IC : integer control Verification times in secs

Verifying Linked Lists with Multiple Fields 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... nnn L x  x.n = y, L y... nnn L x  x.n = y, y.p = x, L y ppp L x  x.n = y, x.d = z, L y... nnn d d d

Shape Analysis [Sagiv,Reps, Wilhelm TOPLAS’98], [Dor, Rodeh, Sagiv SAS’00] –These papers on shape analysis directly influenced us [Yahav POPL’01] –Verification of concurrent linked lists with arbitrary number of processes [Sagiv,Reps, Wilhelm TOPLAS], [Lev-Ami, Reps, Sagiv, Wilhelm ISSTA 00] –3-valued logic and instrumentation predicates [Sagiv,Reps, Wilhelm ESOP 03] –Automatically generating instrumentation predicates [Deutch PLDI’94] –Deutch used integer constraint lattices to compute aliasing information using symbolic access paths [Fradet and Metayer POPL 97] –The idea of summarization patterns is based on the shape types introduced in the above paper

Model Checking Software Specifications [Atlee, Gannon 93] –Translating SCR mode transition tables to input language of explicit state model checker EMC [Clarke, Emerson, Sistla 86] [Chan et al. 98,00] –Translating RSML specifications to input language of SMV [Bharadwaj, Heitmeyer 99] –Translating SCR specifications to Promela, input language of automata-theoretic explicit state model checker SPIN

Specification Languages Specification languages for verification –[Milner 80] CCS –[Chandy and Misra 88] Unity –[Lamport 94] Temporal Logic of Actions (TLA) Specification languages for model checking –[Holzmann 98] Promela –[McMillan 93] SMV –[Alur and Henzinger 96, 99] Reactive Modules

Action Language TLA Connection Similarities: –Transition relation is defined using predicates on current (unprimed) and next state (primed) variables –Each predicate is defined using integer arithmetic, boolean logic, etc. Differences: In Action Language –Temporal operators are not used in defining the transition relation Dual language approach: temporal properties (in CTL) are redundant, they are used to check correctness –Synchronous and asynchronous composition operators are not equivalent to logical operators

Constraint-Based Verification [Cooper 71] –Used a decision procedure for Presburger arithmetic to verify sequential programs represented in a block form [Cousot and Halbwachs 78] –Used real arithmetic constraints to discover invariants of sequential programs [Halbwachs 93] –Constraint based delay analysis in synchronous programs [Halbwachs et al. 94] –Verification of linear hybrid systems using constraint representations [Alur et al. 96] –HyTech, a model checker for hybrid systems [Delzanno and Podelski 99] –Built a model checker using constraint logic programming framework

Automata-Based Representations [Klarlund et al.] –MONA, an automata manipulation tool for verification [Boudet and Comon CAAP ’96] –Translating linear arithmetic constraints to automata [Wolper and Boigelot TACAS ‘00] –verification using automata as a symbolic representation [Kukula et al. 98] –application of automata based verification to hardware verification [Bouajjani et al CAV ’00] –Regular Model Checking [Dams, Lakhnech and Steffen CAV ’01], [Boigelot, Legay and Wolper CAV ’03] –Iterating transducers

Combining Symbolic Representations [Chan et al. CAV’97] –both linear and non-linear constraints are mapped to BDDs –Only data-memoryless and data-invariant transitions are supported [Bharadwaj and Sims TACAS’00] –Combines automata based representations (for linear arithmetic constraints) with BDDs –Specialized for inductive invariant checking [Bensalem et al. 00] –Symbolic Analysis Laboratory –Designed a specification language that allows integration of different verification tools

Model Checking Programs Verisoft from Bell Labs [Godefroid POPL 97] –C programs, handles concurrency, bounded search, bounded recursion, stateless search Java Path Finder (JPF) at NASA Ames [Havelund, Visser] –Explicit state model checking for Java programs, bounded search, bounded recursion, handles concurrency SLAM project at Microsoft Research [Ball, Rajamani et al. SPIN 00, PLDI 01] –Symbolic model checking for C programs, unbounded recursion, no concurrency –Uses predicate abstraction [Saidi, Graf 97] and BDDs BANDERA: A tool for extracting finite state models from programs [Dwyer, Hatcliff et al ICSE 00, 01]

Concurrency Controllers and Interfaces [Betin-Can, Bultan SoftMC 03, ASE’04] Concurrency Controller – Behavior: How do the shared variables change – Interface: In which order are the methods invoked Separate Verification – Behavior verification Action Language Verifier – Interface verification Java PathFinder A modular approach – Build complex concurrency controllers by composing interfaces

Example Interface reqLand exitRW3crossRW3 park2 reqTakeOff leave exitRW4 exitRW5 exitRW6 exitRW7 exitRW8 crossRW5 crossRW6 crossRW7 crossRW8 crossRW4 park11 park10 park9 park7

Verification of Web Services [Fu, Bultan, Hull, Su TACAS 01, WES 02], [Bultan,Fu,Hull, Su WWW 03], [Fu, Bultan, Su CIAA 03, WWW 04,ISSTA 04,ICWS 04] Verification of Vortex workflows using SMV and Action Language Verifier A top-down approach to specification and verification of composite web services –Specify the composite web service as a conversation protocol –Generate peer specifications from the conversation protocol Realizability conditions Working on the application of this framework to BPEL –Bottom-up approach using synchronizability analysis

Conversation Protocol A  B:msg1 B  A:msg2 B  C:msg3C  B:msg4 B  C:msg5 G(msg1  F(msg3  msg5)) ? LTL property !msg1 ?msg2 Peer A ?msg1 !msg2 !msg5 !msg3 ?msg4 Peer B ?msg3 !msg4 Peer C Peer APeer BPeer C msg1 msg2, msg6 msg3, msg5 msg4 Conversation Schema Input Queue... Virtual Watcher ?msg6 B  A:msg6 !msg6 ?msg5

The End

Tools for Automated Verification of Concurrent Software Tevfik Bultan Department of Computer Science University of California, Santa Barbara

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Tools for Automated Verification of Concurrent Software Tevfik Bultan Department of Computer Science University of California, Santa Barbara

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Presentation on theme: "Tools for Automated Verification of Concurrent Software Tevfik Bultan Department of Computer Science University of California, Santa Barbara"— Presentation transcript:

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