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Logical Abstract Interpretation Sumit Gulwani Microsoft Research, Redmond.

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1 Logical Abstract Interpretation Sumit Gulwani Microsoft Research, Redmond

2 Final Goal of the class Automatically verify partial correctness of programs like the following using abstract interpretation. Void Init(int* A, int n) { for (i := 0; i { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/250598/1/slides/slide_1.jpg", "name": "Final Goal of the class Automatically verify partial correctness of programs like the following using abstract interpretation.", "description": "Void Init(int* A, int n) { for (i := 0; i

3 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

4 Decision Procedures DP T ( ) = Yes, if is satisfiable = No, if is unsatisfiable Without loss of generality, we can assume that is a conjunction of atomic facts. Why? –DP( 1 Ç 2 ) is sat iff DP( 1 ) is sat or DP( 2 ) is sat What is the trade-off? –Converting into DNF may incur exponential blow-up

5 Outline Decision Procedures Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

6 Linear Arithmetic Expressions e := y | c | e 1 § e 2 | c £ e Atomic facts g := e ¸ 0 | e 0 Note that e=0 can be represented as e ¸ 0 Æ e · 0 e>0 can be represented as e-1 ¸ 0 (over integer LA) The decision problem for integer LA is NP-hard. The decision problem for rational LA is PTime. –PTime algorithms are complicated to implement. Popular choice is an exponential algorithm called Simplex –We will study a PTime algorithm for a special case.

7 Difference Constraints A special case of Linear Arithmetic Constraints of the form x · c and x-y · c –We can represent x · c by x-u · c, where u is a special zero variable. Wlog, we will assume henceforth that we only have constraints x-y · c Reasoning required: x-y · c 1 Æ y-z · c 2 ) x-z · c 1 +c 2 O(n 3 ) (saturation-based) decision procedure –Represent contraints by a matrix M n £ n where M[i][j] = c represents x i –x j · c –Perform transitive closure of M M[i][j] = min { M[i][j], M[i][k]+M[k][j] } – is unsat iff 9 i: M[i][i] < 0

8 Outline Decision Procedures –Linear Arithmetic Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

9 Uninterpreted Functions Expressions e := x | F(e 1,e 2 ) Atomic fact g := e 1 =e 2 | e 1 e 2 Axiom 8 e 1,e 2,e 1,e 2 : e 1 =e 1 Æ e 2 =e 2 ) F(e 1,e 2 )=F(e 1,e 2 ) (called congruence axiom) (saturation-based) Decision Procedure Represent equalities e 1 =e 2 2 G in Equivalence DAG (EDAG) –Nodes of an EDAG represent congruence classes of expressions that are known to be equal. Saturate equalities in the EDAG by following rule: –If C(e 1 )=C(e 1 ) Æ C(e 2 )=C(e 2 ), Merge C(F(e 1,e 2 )), C(F(e 1,e 2 )) where C(e) denotes congruence class of expression e Declare unsatisfiability iff 9 e 1 e 2 in G s.t. C(e 1 ) = C(e 2 )

10 Uninterpreted Functions: Example y F F F F F y=F 3 (y) F(y)=F 4 (y) F 2 (y)=F 5 (y) y=F 2 (y) y=F 5 (y)y F(y) F(y)=F 3 (y) y=F(y) ? : unsat Æ Æ

11 Uninterpreted Functions: Complexity Complexity of congruence closure : O(n log n), where n is the size of the input formula –In each step, we merge 2 congruence classes. The total number of steps required is thus n, where n is a bound on the original number of congruence classes. –The complexity of each step can be O(log n) by using union-find data structure

12 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

13 Combination of Linear Arithmetic and Uninterpreted Functions Expressions e := y | c | e 1 § e 2 | c £ e | F(e 1,e 2 ) Atomic Facts g := e ¸ 0 | e 0 Axioms: Combined axioms of linear arithmetic + uninterpreted fns. Decision Procedure: Nelson-Oppen methodology for combining decision procedures

14 Combining Decision Procedures Nelson-Oppen gave an algorithm in 1979 to combine decision procedures for theories T 1 and T 2, where: –T 1 and T 2 have disjoint signatures except equality –T 1, T 2 are stably infinite Complexity is O(2 n 2 £ (W 1 (n)+W 2 (n)). If T 1, T 2 are convex, complexity is O(n 3 £ (W 1 (n)+W 2 (n)). The theories of linear arithmetic and uninterpreted functions satisfy all of the above criterions.

15 Convex Theory A theory is convex if the following holds. Let G = g 1 Æ … Æ g n If G ) e 1 =e 2 Ç e 3 =e 4, then G ) e 1 =e 2 or G ) e 3 =e 4 Examples of convex theory: -Rational Linear Arithmetic -Uninterpreted Functions

16 Examples of Non-convex Theory y=sel(upd(M,a,0),b) ) y=0 Ç y=sel(M,b) But y=sel(upd(M,a,0),b) ) y=0 and y=sel(upd(M,a,0),b) ) y=sel(M,b) / / Theory of Integer Linear Arithmetic Theory of Arrays 2 · y · 3 ) y=2 Ç y=3 But 2 · y · 3 ) y=2 and 2 · y · 3 ) y=3 / /

17 Stably Infinite Theory A theory T is stably infinite if for all quantifier-free formulas over T, the following holds: If is satisfiable, then is satisfiable over an infinite model. Examples of stably infinite theories –Linear arithmetic, Uninterpreted Functions Examples of non-stably infinite theories –A theory that enforces finite # of distinct elements. Eg., a theory with the axiom: 8 x,y,z (x=y Ç x=z Ç y=z). Consider the quantifier free formula : y 1 =y 2. is satisfiable but doesnt have an infinite model.

18 Nelson-Oppen Methodology Purification: Decompose into 1 Æ 2 such that i contains symbols from theory T i. –This can be done by introducing dummy variables. Exchange variable equalities between 1 and 2 until no more equalities can be deduced. –Sharing of disequalities is not required because of stably-infiniteness. –Sharing of disjunctions of equalities is not required because of convexity. is unsat iff 1 is unsat or 2 is unsat.

19 Combining Decision Procedures: Example a 1 =2y 2 -y 1 y 1 · 4y 3 · a 2 Æ y 1 4y 3 y 1 = y 2 y 1 = a 2 a 2 =F(a 1 ) y 1 =F(y 1 ) Æ y 2 =F(F(y 1 )) y 1 = a 1 y 1 · 4y 3 · F(2y 2 -y 1 ) Æ y 1 =F(y 1 ) Æ y 2 =F(F(y 1 )) Æ y 1 4y 3 Purification y 1 =y 2 y 1 =a 1 y 1 =a 2 ? : unsat Saturation

20 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

21 Logical Abstract Interpretation Abstract Interpretation of a program involves interpreting the program over abstract values from some abstract domain D equipped with a partial order ¹ Logical Abstract Interpretation refers to the case when –D = logical formulas over theory T – ¹ = logical implication relationship, i.e., E ¹ E iff E ) T E We will study following examples of logical interpretation –D consists of finite conjunctions of atomic facts over T. Linear Arithmetic Uninterpreted Functions Combination of Linear Arithmetic and Uninterpreted Functions –D consists of universally quantified formulas over T.

22 Transfer Functions for Logical Abstract Interpreter G =Join(G 1,G 2 ) = d G 1 Ç G 2 e G2G2 G1G1 Join Node G 2 = G Æ: g g True False G G 1 = G Æ g Conditional Node G =Postcondition(y := e, G) = d 9 y: G[y/y] Æ y=e[y/y] e y := e G Assignment Node An abstract interpreter computes abstract values or facts at each program point from facts at preceding program points using appropriate transfer fns. Transfer functions for a logical abstract interpreter thus involve providing operators for over-approximating disjunction and existential quantifier elimination.

23 Fixed-point Computation In presence of loops, fixed-point computation is required. The process is accelerated by using a widening operator, which takes the facts in the current and previous iteration (at some point inside a loop) and generates something weaker than the current fact. –A widening operator should guarantee convergence in a bounded number of steps. –Widening is typically applied at loop header points. Facts generated after fixed-point are invariants and can be used to validate assertions using decision procedures. assert(g) G Validate iff G Æ: g is unsat

24 Initialization The fact at program entry is initialized to >, which in our setting is the logical formula true. –This denotes that we make no assumptions about inputs, and whatever we prove will be valid for all inputs. The facts at all other program points are initialized to ?, which in our setting is the logical formula false. –This denotes our optimistic assumption of unreachability of program locations (unless we can prove them reachable in the process of fixed-point computation).

25 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

26 Difference Constraints Abstract element: –conjunction of x i -x j · c ij –can be represented using matrix M, where M[i][j]=c ij Decide(M): 1.M := Saturate(M) 2.Declare unsat iff 9 i: M[i][i] < 0 Join(M 1, M 2 ): 1.M 1 := Saturate(M 1 ); M 2 := Saturate(M 2 ); 2.Let M 3 be s.t. M 3 [i][j] = Max { M 1 [i][j], M 2 [i][j] } 3.return M 3

27 Difference Constraints Eliminate(M, x i ): 1.M := Saturate(M); 2.Let M 1 be s.t. M 1 [j][k] = 1 (if j=i or k=i) = M[j][k] otherwise 3.return M 1 Widen(M 1, M 2 ): 1.M 1 := Saturate(M 1 ); M 2 := Saturate(M 2 ); 2.Let M 3 be s.t. M 3 [i][j] = M 1 [i][j] (if M 1 [i][j] = M 2 [i][j])) = 1 (otherwise) 3.return M 3

28 1 · y<51 Æ z=y+2 Difference Constraints: Example y < 50 y := 0; z := 2; y++; z++; y=0, z=2 y=1 Æ z=3 ? ? ? ? true y=0 Æ z=2 0 · y · 1 Æ z=y+2 1 · y · 2 Æ z=y+2 0 · y · 2 Æ z=y+2 0 · y Æ z=y+2 0 · y<50 Æ z=y+2 0 · y<51 Æ z=y+2 y=50 Æ z=y+2 False True Assert (z=52)

29 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

30 Uninterpreted Functions Abstract element: –conjunction of e 1 =e 2, where e := y | F(e 1,e 2 ) –can be represented using EDAGs Decide(G): 1.G := Saturate(G); 2.Declare unsat iff G contains e 1 e 2 and G has e 1, e 2 in the same congruence class. Eliminate(G, y): 1.G := Saturate(G); 2.Erase y; (might need to delete some dangling expressions) 3.return G

31 Uninterpreted Functions Join(G 1, G 2 ): 1.G 1 := Saturate(G 1 ); G 2 := Saturate(G 2 ); 2.G := Intersect(G 1, G 2 ); 3.return G; For each node n = in G 1 and node m = in G 2, G contains a node [n,m] =

32 Uninterpreted Functions: Example of Join G1G1 G2G2 F y 2, F y 1, F y 3,y 4 y 5 F y 6 y 7 F y 2, F y 1, F y 4,y 5 F y 6,y 7 y 3 G = Join(G 1,G 2 ) F y 2, F y 1, F y 4,y 5 F y 6 y 3 y 7

33 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

34 Combination: Decision Procedure DP(E 12 ): 1. := Purify&Saturate(E 12 ); 2.Return DP T 1 (E 1 ) Æ DP T 2 (E 2 );

35 Combination: Join Algorithm Join T 12 (L 12, R 12 ): 1. := Purify&Saturate(L 12 ); := Purify&Saturate(R 12 ); 2.D L := Æ {v i = | v i 2 Vars(L 1 Æ L 2 ), v j 2 Vars(R 1 Æ R 2 ) }; D R := Æ {v j = | v i 2 Vars(L 1 Æ L 2 ), v j 2 Vars(R 1 Æ R 2 ) }; 3.L 1 := L 1 Æ D L ; R 1 := R 1 Æ D R ; L 2 := L 2 Æ D L ; R 2 := R 2 Æ D R ; 4.A 1 := Join T 1 (L 1, R 1 ); A 2 := Join T 2 (L 2, R 2 ); 5. V := Vars(A 1 Æ A 2 ) – Program Variables; A 12 := Eliminate T 12 (A 1 Æ A 2, V); 6.Return A 12 ;

36 y=F(a) a= h a,b i z=a-1 a= h a,b i y=F(b) b= h a,b i z=b-1 b= h a,b i z=a-1 Æ y=F(a) z=b-1 Æ y=F(b) h a,b i =1+z y=F( h a,b i ) Join la Join uf Eliminate uf+la y=F(1+z) { h a,b i } Join uf+la Combination: Example of Join Algorithm

37 Combination: Existential Quantifier Elimination Elimintate T 12 (E 12, V): 1. := Purify&Saturate(E 12 ); 2. := DefSaturate(E 1, E 2, V [ Temp Variables); 3.V := V [ Temp Variables – D; E 1 := Eliminate T 1 (E 1, V); E 2 := Eliminate T 2 (E 2, V); 4.E := (E 1 Æ E 2 ) [Defs(y)/y]; 5.Return E; DefSaturate(E 1, E 2, U) returns the set of all variables D that have definitions Defs in terms of variables not in U as implied by E 1 Æ E 2.

38 a=F 2 (b) Æ c=F(b) a · b · y Æ z=c+1 a · b · y Æ z=c+1 Æ a=F 2 (b) Æ c=F(b) { a, b, c } { b } a · y Æ z=c+1 Eliminate uf Substitute F(z-1) · y c z-1 a F(z-1) Eliminate uf+la Combination: Example of Existential Elimination c z-1 a F(z-1) Def la Def uf Eliminate la a = F(c)

39 1 · i, N(x)=N(y)+i struct List { struct List* next; } x, y; N(z) = 0, if z = null = 1 + N(z ! next) Abstract Interpretation over Combined Domain: Example y null y := x; i := 0; i := i+1; y := y ! next; y=x, i=0 y=x ! next, i=1, x null N(x)=N(x ! next)+1 ? ? ? ? true y=x, i=0 y=x, i=0, y null 0 · i · 1 N(x)=N(y)+i 0 · i · 1, y null N(x)=N(y)+i 1 · i · 2, N(x)=N(y)+i 0 · i · 2, N(x)=N(y)+i 0 · i, N(x)=N(y)+i 0 · i, y null N(x)=N(y)+i

40 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

41 Universally Quantified Abstract Domain Abstract element is of the form E Æ Æ i ( 8 X: A i ) B i ) –where E, A i, B i are from some underlying base domain(s) D. The partial order ¹ is a refinement of the more natural implication relationship. E Æ Æ i ( 8 X: A i ) B i ) ¹ E Æ Æ j ( 8 X: A j ) B j ) iff –E ) E – 8 j 9 i s.t. E Æ A j ) A i and E Æ B i ) B j Another way to state the above thing would be to say that the partial order is still the implication relationship but transfer functions are incomplete.

42 Quantified Abstract Domain: Join Algorithm Consider a simpler case first. Let (E Æ 8 X:A ) B) = Join(E 1 Æ 8 X:A 1 ) B 1, E 2 Æ 8 X:A 2 ) B 2 ). Then, –(E 1 Æ 8 X:A 1 ) B 1 ) ¹ (E Æ 8 X:A ) B) –(E 2 Æ 8 X:A 2 ) B 2 ) ¹ (E Æ 8 X:A ) B) Or, equivalently, –E 1 ) E and E 2 ) E. Thus, E = Join(E 1, E 2 ). –E 1 Æ A ) A 1 and E 2 Æ A ) A 2, i.e., A ) (E 1 ) A 1 Æ E 2 ) A 2 ). Thus, A = b E 1 ) A 1 Æ E 2 ) A 2 c. –E 1 Æ B 1 ) B and E 2 Æ B 2 ) B. Thus, B = Join(E 1 Æ B 1, E 2 Æ B 2 ). Join(E 1 Æ Æ i ( 8 X: A 1 i ) B 1 i ), E 2 Æ Æ i ( 8 X: A 2 i ) B 2 i )): 1.result := Join D (E 1, E 2 ); 2.Forall i,j: 3. A := b E 1 ) A 1 i Æ E 2 ) A 2 j c ; B := Join D (E 1 Æ B 1 i, E 2 Æ B 2 j ); 4. result := result Æ 8 X:A ) B 5.return result;

43 Quantified Abstract Domain: Example of Join Let G 1 = (i=0 Æ 8 k: k=0 ) F[k]=i) G 2 = (i=1 Æ 8 k: 0 · k · 1 ) F[k]=0) Then Join(G 1, G 2 ) = 0 · i · 1 Æ 8 k: A ) B, where A = b (i=0 ) k=0) Æ (i=1 ) 0 · k · 1) c = 0 · k · i B = Join D (i=0 Æ F[k]=i, i=1 Æ F[k]=0) = F[k]=0

44 Quantified Abstract Domain: Eliminate Let (E Æ 8 X:A ) B) = Eliminate(E Æ 8 X:A ) B, s). Then, (E Æ 8 X:A ) B) ¹ (E Æ 8 X:A ) B) among other things. For simplicity, assume that s doesnt affect terms in A,B involving X. Then, –E ) E and E doesnt contain any term affected by change to s. Thus, E = Eliminate D (E,s). –E Æ A ) A and A doesnt contain any term affected by change to s. Thus, A = b8 s:E ) A c. –E Æ B ) B and B doesnt contain any term affected by change to s. Thus, B = Eliminate D (E Æ B, s).

45 Quantified Abstract Domain: Eliminate Eliminate(G, s): 1.Let G be E Æ 8 X:A ) B –Psuedo-code can be easily extended for multiple 8 2.T := { e | e occurs in A or B; Vars(e) Å X ; } 3.A := A Æ Æ e 2 T NotEffect(, e); 4.E := Eliminate D (E,s); 5.B := Eliminate D (B Æ E,s); 6.A := b8 s:E ) A c ; 7.return (E Æ 8 X: A ) B) NotEffect(, e) denotes a constraint g s.t. G Æ g implies that s does not affect e.

46 Quantified Abstract Domain: Example of Eliminate Let G = (F[0]>10 Æ 8 k: 0 · k F[0] ) Then Eliminate(G, F[0]) = true Æ 8 k: A ) B, where T = { k, F[k] } NotEffect(, F[k]) = k 0 NotEffect(, F[k]) = true A 1 = 0 · k10 ) 1 · kF[0] Æ F[0]>10, F[0]) = F[k]<10

47 2 · i · n Æ 8 k: 0 · k { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/250598/1/slides/slide_46.jpg", "name": "2 · i · n Æ 8 k: 0 · k

48 References Uninterpreted Functions –A polynomial time algorithm for global value numbering SAS 2004, S. Gulwani, G. Necula –Join algorithms for the theory of uninterpreted fns FSTTCS 2004, S. Gulwani, A. Tiwari, G. Necula Combination of Linear Arithmetic and Uninterpreted Fns –Combining Abstract Interpreters PLDI 2006, S. Gulwani, A. Tiwari Universally Quantified Abstract Domain –Lifting Abstract Interpreters to Quantified Logical Domains POPL 2008, S. Gulwani, B. McCloskey, A. Tiwari

49 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

50 Abstract Program Model / Problem Statement Linear Arithmetic e := y | c | e 1 § e 2 | c e g := e ¸ 0 Uninterpreted Functions e := y | F(e 1,e 2 ) g := e 1 =e 2 Combination e := y | c | e 1 § e 2 | c e | F(e 1,e 2 ) g := e ¸ 0 Assignment y := e Conditional g True False Non-deterministic Assignment y := ? Assertion Assert(g)

51 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

52 Assertion Checking: Linear Arithmetic Non-deterministic Conditionals –Equality Assertions: PTIME Perform abstract interpretation over linear equalities. Affine relationships among variables of a program, Karr 76 –Inequality assertions: ? Deterministic Conditionals: Undecidable –Even with equality conditionals and equality assertions –PCP Problem can be reduced to it. –A Note on Karrs Algorithm, H. Seidl, M. Muller-Olm, ICALP 2004

53 Reducing PCP Problem to Assertion Checking The following problem (PCP Problem) is undecidable. Given pairs: (u 1,v 1 ), …, (u m,v m ), where u i, v i 2 {0,1} * Decide: 9 a non-empty sequence i 1, …, i n such that u i 1 … u i n = v i 1 … v i n Given a PCP instance, we will construct an assertion checking problem over linear arithmetic such that the assertion holds iff the solution to PCP instance is false.

54 y=z ? Reducing PCP Problem to Assertion Checking y := 1; z := 1; y := 2 |u 1 | £ y + ; z := 2 |v 1 | £ z + ; False True Assert (d=0); y := 2 |u m | £ y + ; z := 2 |v m | £ z + ; * d := 1; d := 0;

55 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns

56 Assertion Checking: Uninterpreted Functions Non-deterministic Conditionals: PTIME –Abstract interpretation over uninterpreted fns. Deterministic Conditionals: Undecidable –PCP Problem can be reduced to it. –Checking Herbrand Equalities and Beyond, H. Seidl, M. Muller-Olm, VMCAI 2005

57 y=z ? Reducing PCP Problem to Assertion Checking y := 1; z := 1; y := u 1 (y); z := v 1 (z); False True Assert (d=0); y := u m (y); z := v m (z); * d := 1; d := 0; Think of u i, v i as sequences of applications of unary fns, one corresponding to 0 and other corresponding to 1.

58 Outline Decision Procedures –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns Logical Abstract Interpretation –Linear Arithmetic –Uninterpreted Functions –Combination of Linear Arithmetic and Uninterpreted Fns –Universally Quantified Formulas Hardness of Assertion Checking –Linear Arithmetic –Uninterpreted Functions Combination of Linear Arithmetic and Uninterpreted Fns

59 Assertion Checking: Combination of Linear Arithmetic & Uninterpreted Functions Deterministic Conditionals: Undecidable –No surprise since problem is undecidable for individual cases of linear arithmetic and uninterpreted fns. Non-deterministic Conditionals: ? At least coNP-hard –Even for equality conditionals. –A surprising result since assertion checking for individual cases of linear arithmetic (equalities) and uninterpreted fns is PTIME, but not for combination. –In contrast, decision procedures for linear arithmetic and uninterpreted fns can be combined in PTIME using Nelson- Oppen methodology. –Assertion checking over combination of linear arithmetic and uninterpreted fns, S. Gulwani, A. Tiwari, ESOP 2006

60 Reducing Unsatisfiability to Assertion Checking boolean 3-SAT instance with m clauses IsUnsatisfiable( ) { for j=1 to m c j := 0; for i=1 to k do if (*) 8 j s.t. var i occurs positively in clause j, c j := 1; else 8 j s.t. var i occurs negatively in clause j, c j := 1; y = c 1 + c 2 + … + c m ; Assert (y=0 Ç y=1 … Ç y=m-1); }

61 Encoding disjunction The check y=1 Ç y=2 can be encoded by the assertion F(y) = F(1)+F(2)-F(3-y)). The above trick can be recursively applied to construct an assertion that encodes y=0 Ç y=1 Ç … Ç y=m-1 –Eg., y=0 Ç y=1 Ç y=2 can be encoded by encoding F(y)=F(0) Ç F(y)=F(1)+F(2)-F(3-y)

62 Conclusion We showed how logical reasoning traditionally used in theorem proving can be exploited in an abstract interpretation setting. –We focused on conjunctive and universally quantified invariants over the domain of linear arithmetic and uninterpreted fns. There are several other interesting issues in program analysis that we did not address: –Destructive updates Points-to analysis, Shape analysis –Path-sensitive analysis Disjunctive invariants –Inter-procedural analysis Procedure summaries


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