1. 1 Applications of TVLA Mooly Sagiv Tel Aviv University http://www.cs.tau.ac.il/~rumster/TVLA/ Shape Analysis with Applications

2. 2 Outline u Issues –Complexity of TVLA –Weak vs. Strong Updates u Cleanness –Null derefernces –Memory leaks –Freed storage (homework) –The concurrent modification problem u Partial correctness –Sorting –GC u Total Correctenss u Flow dependences u Multithreading u Other

3. 3 Complexity of Shape Analysis x = malloc() if (…) y 1 = x if (…) y 2 = x if (…) y 3 = x  if (…) y n =x

4. 4 Complexity of TVLA analysis u Maximal number of Nodes in Blurred Structures –3 |A | u Size of 3-valued structure representation u Action cost –Focus –Precondition –Coerce –New –Update –Coerce –Blur

5. 5 Weak vs. Strong Updates if (…) x = y else x = z x  n = NULL

6. 6 Detecting Incorrect Library Usages (J. Field, D. Goyal. G. Ramalingam, A. Warshavski) u Java provides libraries for manipulating data structures u Collections –Lists –Hashset –…–… u Iterators over collections allows sequential accesses u Statically detect incorrect library usages Set s = worklist.unprocessedItems(); for (Iterator i = s.iterator(); i.hasNext()){ Object item = i.next(); if (...) processItem(item);

7. 7 The Concurrent Modification Problem u Static analysis of Java programs manipulating Java 2 collections u Inconsistent usages of iterators –An Iterator object i defined on a collection object c –No use of i may be preceded by update to the contents of c, unless the update was also made via I –Guarantees order independence

8. 8 Artificial Example Set v = new Set(); Iterator i1 = v.iterator(); Iterator i2 = v.iterator(); Iterator i3 = i1; i1.next(); i1.remove(); if (...) { i2.next(); } if (...) { i3.next(); } v.add("..."); if (...) { i1.next();}

9. 9 class Make { private Worklist worklist; public static void main (String[] args) { Make m = new Make(); m.initializeWorklist(args); m.processWorklist(); } void initializeWorklist(String[] args) {...; worklist = new Worklist();... // add some items to worklist} void processWorklist() { Set s = worklist.unprocessedItems(); for (Iterator i = s.iterator(); i.hasNext()){ Object item = i.next(); if (...) processItem(item); } } void processItem(Object i){...; doSubproblem(...);} void doSubproblem(...) {... worklist.addItem(newitem);... } } public class Worklist { Set s; public Worklist() {...; s = new HashSet();... } public void addItem(Object item) { s.add(item); } public Set unprocessedItems() { return s; } } return rev; }

10. 10 Static Detection of Concurrent Modifications u Statically Check for CME exceptions u Warn against potential CME u Sound (conservative) solution u Not too many false alarms u Coding in TVLA –Operational Semantics –Vanilla solution is Imprecise (and inefficient) –Derive instrumentation predicates u Java to TVP front-end –Extract potentially relevant client code

11. 11 CME specification in Java’ class Version { /* represents distinct versions of a Set */ } class Collection { Version version; Collection() { version = new Version(); } boolean add(Object o) { version = new Version(); } Iterator iterator() { return new Iterator(this); } } class Iterator { Collection set; Version definingVersion; Iterator (Collection s) { definingVersion = s.version; set = s; } void remove() { requires (definingVersion == set.version); set.ver = new Version(); definingVersion = set.version; } Object next() { requires (definingVersion == set.version); } }

12. 12 Vanilla TVLA Encoding u Local iterators are pointers –Unary predicates u Relevant fields are pointer selectors –Binary predicates

13. 13 Artificial Example Set v = new Set(); Iterator i1 = v.iterator(); Iterator i2 = v.iterator(); Iterator i3 = i1; i1.next(); i1.remove(); if (...) { i2.next(); } if (...) { i3.next(); } v.add("..."); if (...) { i1.next();}

14. 14 Improved TVLA Encodings Use reachability Explicitly maintain relevant information valid[i] = i.defVersion == i.set.Version iterOf[i, v] = i.set == v mutex[i, j] = i.set ==j.set && i != j same[v, w] == v == w Can be automatically derived from the specification Polynomial complexity in programs where iterators are not stored in the client heap Meet over all path solution Adaptive to programs with client heap

15. 15 Empirical Results BenchmarkLoc Err.FATime (sec) Space (MB) Structs. Kernel68315060194363 MapTest3351061204937 Iterator Test 126000.234208 JFE289611236499878

16. 16 Partial Correctness u {P} S {Q} u How to derive loop invariants u Abstract interpretation provides a sound solution u The abstract domain represents a class of program invariants

17. 17 Example Sorting of linked lists u dle(v1, v2) = v1.data  v2.data u inOrder[n, dle](v) =  v1: n(v, v1)  dle(v, v1) u inROrder[n, dle](v) =  v1: n(v, v1)  dle(v1, v) u Captures intermediate invariants as well typedef struct node { struct node *n; int data; } *Elements;

18. 18 typedef struct node { struct node *n; int data; } *Elements; L insert_sort(L x) { L r, pr, rn, l, pl;r = x; pr = NULL; while (r != NULL) { l = x;rn = r ->n; pl = NULL; while (l != r) { if (l->data > r->data) { pr->n = rn; r->n = l; if (pl == NULL) x = r; else pl->n = r; r = pr; break; } pl = l; l = l->n; } pr = r; r = rn; } return x; }

19. 19 L insert_sort(L x) { L r, pr, rn, l, pl; … return x; } n inOrder[dle,n]=½ r[n,x] dle inOrder[dle,n]=1/2 r[n,x] dle n x inOrder[dle,n] r[n,x] dle inOrder[dle,n] r[n,x] dle n x

20. 20 /*pred.tvp */ foreach (z in PVar) { %p z(v_1) unique box } %p n(v_1, v_2) function %i is[n](v) = E(v_1, v_2) ( v_1 != v_2 & n(v_1, v) & n(v_2, v)) foreach (z in PVar) { %i r[n,z](v) = E(v_1) (z(v_1) & n*(v_1, v)) } %i c[n](v) = n+(v, v) %p dle(v1, v2) reflexive transitive %i inOrder[dle,n](v) = A(v_1) n(v, v_1) -> dle(v, v_1) nonabs %i inROrder[dle,n](v) = A(v_1) n(v, v_1) -> dle(v_1, v) nonabs %r !dle(v_1, v_2) ==> dle(v_2, v_1)

21. 21 /* cond.tvp */ %action uninterpreted() { %t "uninterpreted" } %action Is_Not_Null_Var(x1) { %t x1 + " != NULL" %f { x1(v) } %p E(v) x1(v) } %action Is_Null_Var(x1) { %t x1 + " == NULL" %f { x1(v) } %p !(E(v) x1(v)) } %action Is_Eq_Var(x1, x2) { %t x1 + " == " + x2 %f { x1(v), x2(v) } %p A(v) x1(v) x2(v) } %action Is_Not_Eq_Var(x1, x2) { %t x1 + " != " + x2 %f { x1(v), x2(v) } %p !A(v) x1(v) x2(v) }

22. 22 %action Greater_Data_L(x1, x2) { %t x1 + "->data > " + x2 + "->data" %f { x1(v_1) & x2(v_2) & dle(v_1, v_2) } %p !E(v_1, v_2) x1(v_1) & x2(v_2) & dle(v_1, v_2) } %action Less_Equal_Data_L(x1, x2) { %t x1 + "->data data" %f { x1(v_1) & x2(v_2) & dle(v_1, v_2) } %p E(v_1, v_2) x1(v_1) & x2(v_2) & dle(v_1, v_2) }

23. 23 stat.tvp %action Set_Next_Null_L(x1) { %t x1 + "->" + n + " = null" %f { x1(v) } %message !(E(v) x1(v)) -> { n(v_1, v_2) =... is[n](v) =... r[n,x1](v) =... foreach (z in PVar –{x}) { r[n, x](v) =... } c[n](v) = inOrder[dle,n](v) = inOrder[dle,n](v) | x1(v) inROrder[dle,n](v) = inROrder[dle,n](v) | x1(v) }

24. 24 stat.tvp(more) %action Malloc_L(x1) { %t x1 + " = (L) malloc(sizeof(struct node)) " %new { x1(v) = isNew(v) inOrder[dle, n](v1, v2)=… inROrder[dle, n](v1, v2)=… }

25. 25 Abstract interpretation of if x->data <= y.data

26. 26 From Local Outlook to Global Outlook u (Safety) Every time control reaches a given point: –there are no garbage memory cells –the list is acyclic –each cell is locally ordered u (History) The list is a permutation of the original list

27. 27 Bugs Found u Pointer manipulations –null dereferences –memory leaks u Forget to sort the first element u Swap equal elements in bubble sort (non-termination)

28. 28 L insert_sort_b2(L x) { L r, pr, rn, l, pl; if (x == NULL) return NULL; pr = x; r = x->n; while (r != NULL) { pl = x; rn = r->n; l = x->n; while (l != r) { if (l->d > r->d) { pr->n = rn; r->n = l ; pl->n = r; r = pr; break } pl = l;l = l->n; } pr = r; r = rn;} return x; } dle n inOrder[dle,n]=½ dle inOrder[dle,n]=1 dle n x

29. 29 Running Times

30. 30 Properties Not Proved u (Liveness) Termination u Stability

31. 31 Example: Mark and Sweep void Sweep() { unexplored = Universe collected =  while (unexplored   ) { x = SelectAndRemove(unexplored) if (x  marked) collected = collected  {x} } assert(collected = = Universe – Reachset(root) ) } void Mark(Node root) { if (root != NULL) { pending =  pending = pending  {root} marked =  while (pending   ) { x = SelectAndRemove(pending) marked = marked  {x} t = x  left if (t  NULL) if (t  marked) pending = pending  {t} t = x  right if (t  NULL) if (t  marked) pending = pending  {t} } assert(marked = = Reachset(root)) } Run Demo

32. 32 Total Correctness u Usually more complicated u Need to show that something good eventually happens u Difficult for programs with unbounded concrete states u Example linked lists –Show decreased set of reachable locations

33. 33 Program Dependences u A statement s1 depends on s2 if –s2 writes into a location l –s1 reads from location l –There is no intervening write in between u Useful for –Parallelization –Scheduling –Program Slicing u How to compute –Scalars –Stack pointers –Heap allocated pointers

34. 34 int y; List p, q; q = (List) malloc(); p = q; p->d = 5; … y = q->d; Flow Dependences vs. May-Aliases int y; List p, q; q = (List) malloc(); p = q; t=p; p->d = 5; t->d = 7; y = q->d; int y; List p, q; q = (List) malloc(); p = q; t=p; p->d = 5; p=(List) malloc(); y = q->d;

35. 35 void append() { List head, tail, temp; l1: head = (List) malloc(); l2: scanf("%c", &head->d); l3: head->n = NULL; l4: tail = head; l5: if (tail->d == `x') goto l12; l6: temp = (List) malloc(); l7: scanf("%c", &temp->d); l8: temp->n = NULL; l9: tail->n = temp; l10: tail = tail->n l11: goto l_5; l12: printf("%c", head->d); l13: printf("%c", tail->d); exit

36. 36 /*pred.tvp */ foreach (z in PVar) { %p z(v_1) unique box } %p n(v_1, v_2) function %i is[n](v) = E(v_1, v_2) ( v_1 != v_2 & n(v_1, v) & n(v_2, v)) foreach (z in PVar) { %i r[n,z](v) = E(v_1) (z(v_1) & n*(v_1, v)) foreach (l in Label) { %p lst_w_v[l,z]() // l is the last write to into the variable z } foreach (l in Label) { %p lst_w_f[l,n](v_1) box // l is the last write to into the v_1.n %p lst_w_f[l,d](v_1) box// l is the last write to into v_1.data } %i c[n](v) = n+(v, v)

37. 37 Operational Semantics for Statements stupdate l: x=rhslst_w_v[l,x]’ = 1 lst_w_v[l’, x] = 0 l: x->d = rhslst_w_f[l, d](v) = (x(v) ? 1 : lst_w_f[l, d](v)) lst_w_f[l’, d](v) = (x(v)? 0 : lst_w_f[l’, d](v)) l: x->n = rhslst_w_f[l, n](v) = (x(v) ? 1 : lst_w_f[l, n](v)) lst_w_f[l’, n](v) = (x(v)? 0 : lst_w_f[l’, n](v))

38. 38 “Read” Formulae for Statememts expformula l’: x := NULL0 l’: x := ylst_w_v[l,y] l’: x->n =NULLlst_w_v[l, x] l’: x->n= y lst_w_v[l, x]  lst_w_v[l, y] l’: x = y->n lst_w_v[l, y](v)   v: y(v)  lst_w_f[l, n](v)

39. 39 void append() { List head, tail, temp; l1: head = (List) malloc(); l2: scanf("%c", &head->d); l3: head->n = NULL; l4: tail = head; l5: if (tail->d == `x') goto l12; l6: temp = (List) malloc(); l7: scanf("%c", &temp->d); l8: temp->n = NULL; l9: tail->n = temp; l10: tail = tail->n l11: goto l5; l12: printf("%c", head->d); l13: printf("%c", tail->d); exit head temp tail lst_w_v[l1, head] lst_w_v[l10, tail] lst_w_v[l6, temp] lst_w_f[l2, d] lst_w_f[l9,n] lst_w_f[l7,d] lst_w_f[l8,n]

40. 40 Java Concurrency u Threads and locks are just dynamically allocated objects u synchronized implements mutual exclusion u wait, notify and notifyAll coordinate activities across threads

41. 41 l_0: while (true) { l_1:synchronized(sharedLock) { l_C:// critical actions l_2:} l_3: } Two threads: (pc 1,pc 2,lockAcquired 1,lockAcquired 2 ) Example - Mutual Exclusion u Allocate new lock ? u Allocate new thread ?

42. 42 Program Model u Interleaving model of concurrency u Program is modeled as a transition system

43. 43 Configurations u A program configuration encodes: –global store –program-location of every thread –status of locks and threads u First-order logical structures used to represent program configurations

44. 44 Configurations u Predicates model properties of interest –is_thread(t) –{ at[lab](t) : lab  Labels } –{ rval[fld](o 1,o 2 ) : fld  Fields } –held_by(l,t) –blocked(t,l) –waiting(t,l) u Can use the framework with different predicates

45. 45 Configurations is_thread at[l_C] rval[this] held_by blocked is_thread at[l_1] rval[this] is_thread at[l_0] is_thread at[l_0] is_thread at[l_1] rval[this] blocked

46. 46 Configurations u Program control-flow is not separately represented u Program location for each thread is encoded inside the configuration –{ at[lab](t) : lab  Labels }

47. 47 Structural Operational Semantics - actions u An action consists of: –precondition(when) formula –update formulae u Precondition formula may use a free variable t s for “currently scheduled” thread u Semantics is non-deterministic

48. 48 Structural Operational Semantics - actions lock(v)  t  t s : rval[v](t s,l)  held_by(l,t) held_by’(l 1,t 1 ) = held_by(l 1,t 1 )  (l 1 = l  t 1 = t s ) blocked’ (t 1,l 1 ) = blocked(t 1,l 1 )  ((l 1  l)  (t 1  t s )) precondition predicate update

49. 49 Safety Properties u Configuration-local property as logical formula u Example: no total deadlock  t,  l b : is_thread(t)   blocked(t, l b )  t 1,t 2 : (t 1  t 2 )   (at[l crit ](t 1 )  at[l crit ](t 2 )) u Example: mutual exclusion

50. 50 Concrete Configuration is_thread at[l_C] rval[this] held_by blocked is_thread at[l_1] rval[this] is_thread at[l_0] is_thread at[l_0] is_thread at[l_1] rval[this] blocked

51. 51 Abstract Configuration is_thread at[l_C] rval[this] held_by blocked is_thread at[l_1] rval[this] is_thread at[l_0]

52. 52 Safety Properties Revisited u RW interference u WW interference u Total deadlock u Nested monitors u Illegal thread interactions

53. 53 Interprocedural Analysis (Rinetzky) u Model the stack as a linked list (CC 2001) –Observe alias patterns –Handles recursion with pointers from the stack to the heap (but rather slow) u Exploit referential transparency –The part of the store modified by a procedure is limited –Summarize irrelevant calling contexts –Pre-analyze Abstract Data Types »Analyzed parts of LEDA linked lists

54. 54 Other TVLA Applications u Handling trees (G. Yorsh) u Allowing data structure specification (M. Rinard) u Refinement of implementations (A. Mulhren) u Mobile Ambients (F. Nielson & H.R. Nielson)

55. 55 TVLA Generalizations u ITVLA (F. Dimaio, N. Dor) –Support arithmetic operations –Import static domains for integer analysis »Intervals »Polyhedra –Derive Update Formula for Instrumentation Predicates (A. Loginov, T. Reps)

56. 56 Summary u TVLA supports design and prototyping of sophisticated static analyzers –“The heap is your friend” –First order logic specifications is natural –Powerful static domain u But scaling is an issue –Interprocedural analysis –User specification –Predictability –User interface u Debugability of Operational Semantics