1. Integrating Arithmetic Constraint Based Verification and Shape Analysis Tevfik Bultan Joint work with Tuba Yavuz-Kahveci Department of Computer Science University of California, Santa Barbara bultan@cs.ucsb.edu http://www.cs.ucsb.edu/~bultan/composite

2. Motivation Concurrent programming is difficult and error prone –Sequential programming: states of the variables –Concurrent programming: states of the variables and the processes Linked list manipulation is difficult and error prone –States of the heap: possibly infinite We would like to guarantee properties of a concurrent linked list implementation

3. More Specific Problem There has been work on verification of concurrent systems with integer variables (and linear constraints) –[Boigelot 98], [Bultan, Gerber and Pugh, TOPLAS 99], [Delzanno and Podelski, STTT 01] There has been work on verification of (concurrent) linked lists –[Sagiv,Reps, Wilhelm TOPLAS 98], [Yahav POPL 01] What can we do for concurrent systems: –where both integer and heap variables influence the control flow –or the properties we wish to verify involve both integer and heap variables?

4. Our Approach Use symbolic verification techniques –Use polyhedra to represent the states of the integer variables –Use BDDs to represent the states of the boolean and enumerated variables –Use shape graphs to represent the states of the heap –Use a composite representation to combine them Use forward-fixpoint computations to compute reachable states –Truncated fixpoint computations can be used to detect errors –Over-approximation techniques can be used to prove properties Polyhedra widening Summarization in shape graphs

5. Action Language Tool Set Guarded Commands Action Language Parser Verifier Code Generator OmegaLibraryCUDDPackage Verified code (Java monitor classes) MONA Composite Symbolic Library Translator to Action Language Students who work with me on this project: – – Tuba Yavuz-Kahveci – – Constantinos Bartzis – – Aysu Betin-Can PresburgerArithmeticManipulatorBDDManipulatorAutomataManipulator ActionLanguageSpecification OR

6. Related Publications Composite Symbolic Library, Integration of polyhedra representation with BDDs –[Yavuz-Kahveci, Tuncer, Bultan, TACAS 01], [Yavuz-Kahveci, Bultan, STTT] Action Language Verifier –[Bultan ICSE 00], [Bultan, Yavuz-Kahveci ASE 01] Verification of Concurrency Control Components using Action Language Verifier –[Yavuz-Kahveci, Bultan ISSTA 02] Using automata representation for Presburger arithmetic in Composite Symbolic Library –[Bartzis and Bultan, CIAA 02], [Bartzis and Bultan, IJFCS] [Bartzis, Bultan CAV 03]

7. Outline Specification of concurrent linked lists –Action Language Symbolic verification –Composite representation Approximation techniques –Summarization –Widening Counting abstraction Experimental results Related Work Conclusions

8. Action Language [Bultan ICSE 00] [Yavuz-Kahveci, Bultan ASE 01] A state based language –Actions correspond to state changes States correspond to valuations of variables –Integer (possibly unbounded), heap, boolean and enumerated variables –Parameterized constants are allowed Transition relation is defined using actions –Atomic actions: Predicates on current and next state variables –Action composition: synchronous (&) or asynchronous (|) Modular –Modules can have submodules Properties to be verified –Invariant(p) : p always holds

9. Composite Formulas: State Formulas We use state formulas to express the properties we need to check –No primed variables in state formulas –State formulas are boolean combination ( , , , ,  ) of integer, boolean and heap formulas numItems > 2 => top.next != null integer formula heap formula

10. State formulas Boolean formulas –Boolean variables and constants (true, false) –Relational operators: =,  –Boolean connectives ( , , , ,  ) Integer formulas (linear arithmetic) –Integer variables and constants –Arithmetic operators: +, , and * with a constant –Relational operators: =, , >, <, ,  –Boolean connectives ( , , , ,  ) Heap formulas –Heap variable, heap-variable.selector, heap constant null –Relational operators: =,  –Boolean connectives ( , , , ,  )

11. Composite Formulas: Transition Formulas We use transition formulas to express the actions –In transition formulas primed-variables denote the next-state values, unprimed-variables denote the current-sate values pc=l2 and numItems=0 and top’=add and numItems’=1 and pc’=l3; current state variables next state variables

12. Transition Formulas Transition formulas are in the form: –boolean-formula  integer-formula  heap-transition-formula Heap transition formulas are in the form: –guard-formula  update-formula

13. Heap Transition Formulas A guard formula is a boolean combination of terms in the form: id 1 = id 2 id 1  id 2 id 1.f = id 2 id 1.f  id 2 id 1.f = id 2.f id 1.f  id 2.f id 1 = null id 1  null id 1.f = null id 1.f  null An update formula is a term in the form: id’ 1 = id 2 id’ 1 = id 2.f id’ 1.f = id 2 id’ 1.f = id 2.f id’ 1 = null id’ 1.f = null id’ 1 = new id’ 1.f = new

14. 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 top=null 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 top!=null 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 Variable declarations define the state space of the system Initial states Atomic actions: primed variables denote the next sate variables Transition relation of the push module is defined as the asynchronous composition of its atomic actions Stack Example

15. Stack (Cont’d) module pop() enumerated pc {l1, l2, l3}; initial: pc=l1 and get=null and newTop=null; pop1: pc=l1 and mutex and top!=null and newTop’=top.next and !mutex’ and pc’=l2; pop2: pc=l2 and get’=top and pc’=l3; pop3: pc=l3 and top’=newTop and mutex’ and numItems’=numItems-1 and pc’=l1; pop: pop1 | pop2 | pop3; endmodule main: pop() | pop() | push() | push(); spec: invariant([mutex =>(numItems=0 top=null)]) spec: invariant([mutex =>(numItems>2 => top->next!=null)]) endmodule Invariants to be verified Transition relation of main defined as asynchronous composition of two pop and two push processes

16. Stack (with integer guards) 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 add’.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

17. Outline Specification of concurrent linked lists –Action Language Symbolic verification –Composite representation Approximation techniques –Summarization –Widening Counting abstraction Experimental results Related Work Conclusions

18. Symbolic Verification: Forward Fixpoint Forward fixpoint for the reachable states can be computed by iteratively manipulating symbolic representations –We need forward-image (post-condition), union, and equivalence check computations ReachableStates(I: Set of initial states, T: Transition relation) { RS := I; repeat { RS old := RS; RS := RS old  forwardImage(RS old, T); } until (RS  RS old ) }

19. Symbolic Verification: Symbolic Representations We use symbolic representations for encoding sets of states Boolean logic formulas (stored as a BDDs) represent the sets of states of the boolean variables: pc=l1  mutex Presburger arithmetic formulas (stored as polyhedra) represent the sets of states of integer variables: numItems > 0

20. Symbolic Representation: Shape Graphs Sets of shape graphs represent the sates of the heap variables and the heap Each node in the shape graph represents a dynamically allocated memory location Heap variables point to nodes of the shape graph (if they are not null) 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

21. Composite Representation Each variable type is mapped to a symbolic representation type –Boolean and enumerated types  BDD representation –Integer variables  Polyhedra –Heap variables  Shape graphs Each conjunct in a transition formula operates on a single symbolic representation Composite representation: A disjunctive representation to combine different symbolic representations Union, subsumption check and forward-image computations are performed on this disjunctive representation

22. Composite Representation A composite representation A is a disjunction where –n is the number of composite atoms in A –t is the number of basic symbolic representations Each composite atom is a conjunction –Each conjunct corresponds to a different symbolic representation

23. Composite Representation: Example pc=l1  mutex numItems=2addtop    pc=l2   mutex  numItems=2  addtop BDD A set of polyhedra A set of shape graphs  pc=l4   mutex  numItems=2  addtop  pc=l1  mutex  numItems=3  addtop

24. Composite Symbolic Library [Yavuz-Kahveci, Tuncer, Bultan TACAS01], [Yavuz-Kahveci, Bultan STTT] Composite Library implements this approach using an object- oriented design An abstract class defines the common interface for symbolic representations –Easy to extend with new symbolic representations –Enables polymorphic verification 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] For encoding the states of the heap variables and the heap we use shape graphs encoded as BDDs (using CUDD)

25. Composite Symbolic Library: Class Diagram 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

26. Satisfiability Checking for the Composite Representation Given a composite representation We can check satisfiability as follows:

27. Forward Image Computation for the Composite Representation Given composite representations for a set of states and a transition relation: We can compute the forward image as follows:

28. Forward-Image 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

29. Forward–Fixpoint Computation (Repeatedly Applies Forward-Image) pc=l1  mutex numItems=0 addtop    pc=l1  mutex  numItems=1  addtop  pc=l2   mutex  numItems=0  addtop  pc=l3   mutex  numItems=1  addtop

30.  pc=l4   mutex  numItems=1  addtop  pc=l1  mutex  numItems=2  addtop  pc=l2   mutex  numItems=2  addtop  pc=l4   mutex  numItems=2  addtop pc=l2   mutex  numItems=1  addtop

31. pc=l4  mutex numItems=3 add top  ... 

32. Forward-Fixpoint does 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 The state space is infinite Even if we ignore the heap variables, reachability is undecidable when we have unbounded integer variables So, we use conservative approximations

33. Outline Specification of concurrent linked lists –Action Language Symbolic verification –Composite representation Approximation techniques –Summarization –Widening Counting Abstraction Experimental results Related Work Conclusions

34. Conservative Approximations To verify or falsify a property p Compute a lower ( RS  ) or an upper ( RS + ) approximation to the set of reachable states There are three possibilities: “The property is satisfied” RS  p p p p RS +

35. Conservative Approximations reachable sates which violate the property “The property is false” RS   p p p p RS “I don’t know” RS   p p p p RS RS +

36. Computing Upper and Lower Bounds for Reachable States Truncated fixpoint computation –To compute a lower bound for a least-fixpoint computation –Stops 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] Summarization –Generate summary nodes in the shape graphs which represent more than one concrete node –Materialization: we need to generate concrete nodes from the summary nodes when needed

37. 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...

38. 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

39. Summarization Summarization guarantees that the number of different shape graphs that can be generated are finite However, the summary-counts can still increase without a bound We use polyhedral widening operation to force the fixpoint computation to convergence

40. Let’s Continue the Forward-fixpoint pc=l1  mutex  numItems=3  summaryCount=2 addtop  pc=l2   mutex   addtop numItems=3  summaryCount=2  pc=l4   mutex   addtop numItems=3  summaryCount=2 pc=l1  mutex add top    numItems=4  summaryCount=2 We need to do summarization again 

41. Summarization pc=l1  mutex add top   numItems=4  summaryCount=2 After summarization, it becomes: pc=l1  mutex add top   numItems=4  summaryCount=3

42. Simplification After each fixpoint iteration we try to merge as many composite atoms as possible For example, following composite atoms can be merged pc=l1  mutex  numItems=3  summaryCount=2 addtop pc=l1  mutex add top   numItems=4  summaryCount=3

43. 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)

44. 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

45. Widening Forward-fixpoint computation still will not converge since numItems and summaryCount keep increasing without a bound We use the widening operation: –Given two composite atoms c 1 and c 2 in consecutive fixpoint iterates, assume that c 1 = b 1  i 1  h 1 c 2 = b 2  i 2  h 2 where b 1 = b 2 and h 1 = h 2 and i 1  i 2 –Also assume that i 1 is a single polyhedron (i.e. a conjunction of arithmetic constraints) and i 2 is also a single polyhedron

46. Widening Then –i 1  i 2 is defined as: all the constraints in i 1 which are also satisfied by i 2 Replace i 2 with i 1  i 2 in c 2 This generates an upper approximation to the forward-fixpoint computation

47. Widening Example 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, the forward-fixpoint converges

48. Dealing with Arbitrary Number of Processes Use counting abstraction [Delzanno CAV’00] –Create an integer variable for each local state of a process –Each variable will count the number of processes in a particular state Local states of the processes have to be finite –Shared variables of the monitor can be unbounded Counting abstraction can be automated

49. Stack After Counting Abstraction module main() heap top, add, get, newTop; boolean mutex; integer numItems; integer l1C, l2C, l2C, l4C; parameterized integer numProc; initial: top=null and mutex and numItems=0 and l1C=numProc and l2C=0 and l3C=0 and l4C=0; restrict: numProc>0; module push() //enumerated pc {l1, l2,l3,l4}; initial: add=null; push1: l1C>0 and mutex and !mutex' and add'=new and l1C'=l1C-1 and l2C'=l2C+1; push2: l2C>0 and top=null and top'=add numItems'=1 and l2C'=l2C-1 and l3C'=l3C+1;... push: push1 | push2 | push3 | push4 | push5; endmodule Parameterized constant representing the number of processes Variables for counting the number of processes in each state When local state changes, decrement current local state counter and increment next local state counter Initialize initial state counter to the number of processes. Initialize other states to 0.

50. 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

51. Experimental Results - Verification Times Number of Processes 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 

52. Generalization to Linked Lists with Multiple Fields We need a summarization operation that can be used to define more than just singly linked lists We use restricted graph grammar rules to define summarization patterns The nodes which match to the summarization pattern are represented with a single node We still keep a summary count for each summary node

53. 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

54. Summarization Using Summarization Patterns Find the (maximal) set of nodes that match to the pattern We are looking at linear linked lists –there will be one entry node –and one exit node (exit node is not included in the summary node) Other than the entry and the exit nodes, the set of nodes which match the pattern do not have any incoming or outgoing edges to outside nodes

55. Summarization Patterns Using summarization patterns we can handle a larger class of linked lists Summarization and materialization operations can be done automatically based on the summarization pattern Verification is still completely automatic but the user has to give the summarization pattern

56. Related Work There is a lot of work on Shape analysis, I will just mention the ones which directly influenced us: –[Sagiv,Reps, Wilhelm TOPLAS’98] –[Dor, Rodeh, Sagiv SAS’00] Verification of concurrent linked lists with arbitrary number of processes in [Yahav POPL’01] [Sagiv,Reps, Wilhelm TOPLAS], [Lev-Ami, Reps, Sagiv, Wilhelm ISSTA 00] use 3-valued logic and instrumentation predicates to verify properties that cannot be expressed directly in our framework such as sorted linked lists, however, our approach does not require instrumentation predicates [Sagiv,Reps, Wilhelm ESOP 03] recent results on automatically generating instrumentation predicates

57. Related Work Deutch used integer constraint lattices to compute aliasing information using symbolic access paths [Deutch PLDI’94] The idea of summarization patterns is based on the shape types introduced in [Fradet and Metayer POPL 97]

58. Future Work Liveness properties? –We would like to do full CTL model checking Backward image computation?