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Rigorous Software Development CSCI-GA 3033-011 Instructor: Thomas Wies Spring 2012 Lecture 12

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Axiomatic Semantics An axiomatic semantics consists of: – a language for stating assertions about programs; – rules for establishing the truth of assertions. Some typical kinds of assertions: – This program terminates. – If this program terminates, the variables x and y have the same value throughout the execution of the program. – The array accesses are within the array bounds. Some typical languages of assertions – First-order logic – Other logics (temporal, linear) – Special-purpose specification languages (Z, Larch, JML)

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Assertions for IMP The assertions we make about IMP programs are of the form: {A} c {B} with the meaning that: – If A holds in state q and q ! q – then B holds in q A is the pre-condition and B is the post-condition For example: { y x } z := x; z := z + 1 { y < z } is a valid assertion These are called Hoare triples or Hoare assertions c

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Semantics of Hoare Triples Now we can define formally the meaning of a partial correctness assertion: ² {A} c {B} iff 8 q 2 Q. 8 q 2 Q. q ² A Æ q ! q ) q ² B and the meaning of a total correctness assertion: ² [A] c [B] iff 8 q 2 Q. q ² A ) 9 q Q. q ! q Æ q ² B or even better: 8 q 2 Q. 8 q Q. q ² A Æ q ! q ) q ² B Æ 8 q 2 Q. q ² A ) 9 q 2 Q. q ! q Æ q ² B c c c c

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Inference Rules for Hoare Triples We write ` {A} c {B} when we can derive the triple using inference rules There is one inference rule for each command in the language. Plus, the rule of consequence ` A ) A ` {A} c {B} ` B ) B ` {A} c {B}

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Inference Rules for Hoare Logic One rule for each syntactic construct: ` {A} skip {A} ` {A[e/x]} x:=e {A} ` {A} if b then c 1 else c 2 {B} ` {A Æ b} c 1 {B} ` {A Æ : b} c 2 {B} ` {A} c 1 ; c 2 {C} ` {A} c 1 {B} ` {B} c 2 {C} ` { I } while b do c { I Æ : b} ` { I Æ b} c { I }

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Example: A Proof in Hoare Logic We want to derive that {n ¸ 0} p := 0; x := 0; while x < n do x := x + 1; p := p + m {p = n * m}

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} Only applicable rule (except for rule of consequence): ` {A} c 1 ; c 2 {B} ` {A} c 1 {C} ` {C} c 2 {B} c1c1 c2c2 B A ` {C} while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 {C} Example: A Proof in Hoare Logic

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} What is C? ` {C} while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 {C} Look at the next possible matching rules for c 2 ! Only applicable rule (except for rule of consequence): ` { I } while b do c { I Æ : b} ` { I Æ b} c { I } We can match { I } with {C} but we cannot match { I Æ : b} and {p = n * m} directly. Need to apply the rule of consequence first! c1c1 c2c2 B A Example: A Proof in Hoare Logic

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} What is C? BA ` {C} while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 {C} Look at the next possible matching rules for c 2 ! Only applicable rule (except for rule of consequence): ` { I } while b do c { I Æ : b} ` { I Æ b} c { I } ` A ) A ` {A} c {B} ` B ) B ` {A} c {B} Rule of consequence: c c A B I = A = A = C Example: A Proof in Hoare Logic

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} What is I ? ` { I } while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 { I } Lets keep it as a placeholder for now! ` I Æ x ¸ n ) p = n * m ` { I } while x < n do (x:=x+1; p:=p+m) { I Æ x ¸ n} ` { I Æ x

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} ` { I } while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 { I } ` I Æ x ¸ n ) p = n * m ` { I } while x < n do (x:=x+1; p:=p+m) { I Æ x ¸ n} ` { I Æ x

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} ` { I } while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 { I } ` I Æ x ¸ n ) p = n * m ` { I } while x < n do (x:=x+1; p:=p+m) { I Æ x ¸ n} What is C?Look at the next possible matching rules for c 2 ! Only applicable rule (except for rule of consequence): ` {A[e/x]} x:=e {A} ` { I [p+m/p} p:=p+m { I } ` { I Æ x

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} ` { I } while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 { I } ` I Æ x ¸ n ) p = n * m ` { I } while x < n do (x:=x+1; p:=p+m) { I Æ x ¸ n} ` { I Æ x

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} ` { I } while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 { I } ` I Æ x ¸ n ) p = n * m ` { I } while x < n do (x:=x+1; p:=p+m) { I Æ x ¸ n} ` { I Æ x

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` {n ¸ 0} p:=0; x:=0; while x < n do (x:=x+1; p:=p+m) {p = n * m} ` { I } while x < n do (x:=x+1; p:=p+m) {p = n * m} ` {n ¸ 0} p:=0; x:=0 { I } ` I Æ x ¸ n ) p = n * m ` I Æ x < n ) I [x+1/x, p+m/p] Lets just remember the open proof obligations!... Continue with the remaining part of the proof tree, as before. ` { I [0/x]} x:=0 { I } ` { n ¸ 0} p:=0 { I [0/x]} ` { I [0/p, 0/x]} p:=0 { I [0/x]} ` n ¸ 0 ) I [0/p, 0/x] Now we only need to solve the remaining constraints! Example: A Proof in Hoare Logic

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` I Æ x ¸ n ) p = n * m ` I Æ x < n ) I [x+1/x, p+m/p] Find I such that all constraints are simultaneously valid: ` n ¸ 0 ) I [0/p, 0/x] I ´ p = x * m Æ x · n ` p = x * n Æ x · n Æ x ¸ n ) p = n * m ` p = p * m Æ x · n Æ x < n ) p+m = (x+1) * m Æ x+1 · n ` n ¸ 0 ) 0 = 0 * m Æ 0 · n All constraints are valid! Example: A Proof in Hoare Logic

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Using Hoare Rules Hoare rules are mostly syntax directed There are three obstacles to automation of Hoare logic proofs: – When to apply the rule of consequence? – What invariant to use for while ? – How do you prove the implications involved in the rule of consequence? The last one is how theorem proving gets in the picture – This turns out to be doable! – The loop invariants turn out to be the hardest problem! – Should the programmer give them?

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Verification Conditions Goal: given a Hoare triple {A} P {B}, derive a single assertion VC(A,P,B) such that ² VC(A,P,B) iff ² {A} P {B} VC(A,P,B) is called verification condition. Verification condition generation factors out the hard work – Finding loop invariants – Finding function specifications Assume programs are annotated with such specifications – We will assume that the new form of the while construct includes an invariant: { I } while b do c – The invariant formula I must hold every time before b is evaluated.

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Verification Condition Generation Idea for VC generation: propagate the post- condition backwards through the program: – From {A} P {B} – generate A ) F(P, B) This backwards propagation F(P, B) can be formalized in terms of weakest preconditions.

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Weakest Preconditions The weakest precondition WP(c,B) holds for any state q whose c-successor states all satisfy B: q ² WP(c,B) iff 8 q 2 Q. q ! q ) q ² B Compute WP(P,B) recursively according to the structure of the program P. B WP(c,B) q q q c c c c

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Loop-Free Guarded Commands Introduce loop-free guarded commands as an intermediate representation of the verification condition c ::= assume b | assert b | havoc x | c 1 ; c 2 | c 1 c 2

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From Programs to Guarded Commands GC( skip ) = assume true GC(x := e) = assume tmp = x; havoc x; assume (x = e[tmp/x]) GC(c 1 ; c 2 ) = GC(c 1 ) ; GC(c 2 ) GC( if b then c 1 else c 2 ) = (assume b; GC(c 1 )) (assume : b; GC(c 2 )) GC({ I } while b do c) = ? where tmp is fresh

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Guarded Commands for Loops GC({ I } while b do c) = assert I ; havoc x 1 ;...; havoc x n ; assume I ; (assume b; GC(c); assert I ; assume false) assume : b where x 1,..., x n are the variables modified in c

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Computing Weakest Preconditions WP(assume b, B) = b ) B WP(assert b, B) = b Æ B WP(havoc x, B) = B[a/x](a fresh in B) WP(c 1 ;c 2, B) = WP(c 1, WP(c 2, B)) WP(c 1 c 2,B) = WP(c 1, B) Æ WP(c 2, B)

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Putting Everything Together Given a Hoare triple H ´ {A} P {B} Compute c H = assume A; GC(P); assert B Compute VC H = WP(c H, true) Infer ` VC H using a theorem prover.

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Example: VC Generation {n ¸ 0} p := 0; x := 0; {p = x * m Æ x · n} while x < n do x := x + 1; p := p + m {p = n * m}

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assume n ¸ 0; GC(p := 0; x := 1; {p = x * m Æ x · n} while x < n do x := x + 1; p := p + m ); assert p = n * m assume n ¸ 0; assume p 0 = p; havoc p; assume p = 0; GC(x := 0; {p = x * m Æ x · n} while x < n do x := x + 1; p := p + m ); assert p = n * m assume n ¸ 0; assume p 0 = p; havoc p; assume p = 0; assume x 0 = x; havoc x; assume x = 0; GC({p = x * m Æ x · n} while x < n do x := x + 1; p := p + m ); assert p = n * m assume n ¸ 0; assume p 0 = p; havoc p; assume p = 0; assume x 0 = x; havoc x; assume x = 0; assert p = x * m Æ x · n; havoc x; havoc p; assume p = x * m Æ x · n; (assume x < n; GC(x := x + 1; p := p + m); assert p = x * m Æ x · n; assume false ) assume x ¸ n; assert p = n * m assume n ¸ 0; assume p 0 = p; havoc p; assume p = 0; assume x 0 = x; havoc x; assume x = 0; assert p = x * m Æ x · n; havoc x; havoc p; assume p = x * m Æ x · n; (assume x < n; assume x 1 = x; havoc x; assume x = x 1 + 1; assume p 1 = p; havoc p; assume p = p 1 + m; assert p = x * m Æ x · n; assume false ) assume x ¸ n; assert p = n * m Computing the guarded command Example: VC Generation

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WP (assume n ¸ 0; assume p 0 = p; havoc p; assume p = 0; assume x 0 = x; havoc x; assume x = 0; assert p = x * m Æ x · n; havoc x; havoc p; assume p = x * m Æ x · n; (assume x < n; assume x 1 = x; havoc x; assume x = x 1 + 1; assume p 1 = p; havoc p; assume p = p 1 + m; assert p = x * m Æ x · n; assert false ) assume x ¸ n; assert p = n * m, true) WP (assume n ¸ 0; assume p 0 = p; havoc p; assume p = 0; assume x 0 = x; havoc x; assume x = 0; assert p = x * m Æ x · n; havoc x; havoc p; assume p = x * m Æ x · n; (assume x < n; assume x 1 = x; havoc x; assume x = x 1 + 1; assume p 1 = p; havoc p; assume p = p 1 + m; assert p = x * m Æ x · n; assert false ) assume x ¸ n, assert p = n * m, true) Computing the weakest precondition Example: VC Generation n ¸ 0 Æ p 0 = p Æ pa 3 = 0 Æ x 0 = x Æ xa 3 = 0 ) pa 3 = xa 3 * m Æ xa 3 · n Æ (pa 2 = xa 2 * m Æ xa 2 · n ) ((xa 2 < n Æ x 1 = xa 2 Æ xa 1 = x 1 + 1 Æ p 1 = pa 2 Æ pa 1 = p 1 + m) ) pa 1 = xa 1 * m Æ xa 1 · n) Æ (xa 2 ¸ n ) pa 2 = n * m))

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The resulting VC is equivalent to the conjunction of the following implications Example: VC Generation n ¸ 0 Æ p 0 = p Æ pa 3 = 0 Æ x 0 = x Æ xa 3 = 0 ) pa 3 = xa 3 * m Æ xa 3 · n n ¸ 0 Æ p 0 = p Æ pa 3 = 0 Æ x 0 = x Æ xa 3 = 0 Æ pa 2 = xa 2 * m Æ xa 2 · n ) xa 2 ¸ n ) pa 2 = n * m n ¸ 0 Æ p 0 = p Æ pa 3 = 0 Æ x 0 = x Æ xa 3 = 0 Æ pa 2 = xa 2 * m Æ xa 2 < n Æ x 1 = xa 2 Æ xa 1 = x 1 + 1 Æ p 1 = pa 2 Æ pa 1 = p 1 + m ) pa 1 = xa 1 * m Æ xa 1 · n

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simplifying the constraints yields all of these implications are valid, which proves that the original Hoare triple was valid, too. Example: VC Generation n ¸ 0 ) 0 = 0 * m Æ 0 · n xa 2 · n Æ xa 2 ¸ n ) xa 2 * m = n * m xa 2 < n ) xa 2 * m + m = (xa 2 + 1) * m Æ xa 2 + 1 · n

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The Diamond Problem assume A; c d; assert B A ) WP (c, WP(c, B) Æ WP(d, B)) Æ WP (d, WP(c, B) Æ WP(d, B)) Number of paths through the program can be exponential in the size of the program. Size of weakest precondition can be exponential in the size of the program. c c d d

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Avoiding the Exponential Explosion Defer the work of exploring all paths to the theorem prover: WP(assume b, B, C) = (b ) B, C) WP(assert b, B, C) = (b Æ B, C) WP(havoc x, B, C) = (B[a/x], C)(a fresh in B) WP(c 1 ;c 2, B, C) = let F 2, C 2 = WP(c 2, B, C) in WP(c 1, F 2, C 2 ) WP(c 1 c 2,B, C) = let X = fresh propositional variable in let F 1, C 1 = WP(c 1, X, true) and F 2, C 2 = WP(c 2, X, true) in (F 1 Æ F 2, C Æ C 1 Æ C 2 Æ (X, B)) WP(P, B) = let F, C = WP(P, B, true) in C ) F

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Translating Method Calls to GCs /*@ requires P; @ assignable x 1,..., x n ; @ ensures Q; T m (T 1 p 1,..., T k p k ) {... } A method call y = x.m(y 1,..., y k ); is desugared into the guarded command assert P[x/this, y 1 /p 1,..., y k /p k ] ; havoc x 1 ;..., havoc x n ; havoc y ; assume Q [ x/this, y / \result ]

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Handling More Complex Program State When is the following Hoare triple valid? {A} x.f = 5 {x.f + y.f = 10} A ought to imply y.f = 5 Ç x = y The IMP Hoare rule for assignment would give us: (x.f + y.f = 10) [5/x.f] ´ 5 + y.f = 10 ´ y.f = 5 (we lost one case) How come the rule does not work?

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Modeling the Heap We cannot have side-effects in assertions – While generating the VC we must remove side-effects! – But how to do that when lacking precise aliasing information? Important technique: postpone alias analysis to the theorem prover Model the state of the heap as a symbolic mapping from addresses to values: – If e denotes an address and h a heap state then: – sel(h,e) denotes the contents of the memory cell – upd(h,e,v) denotes a new heap state obtained from h by writing v at address e

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Heap Models We allow variables to range over heap states – So we can quantify over all possible heap states. Model 1 – One heap for each object – One index constant for each field. We postulate f1 f2. – r.f1 is sel(r,f1) and r.f1 = e is r := upd(r,f1,e) Model 2 (Burnstall-Bornat) – One heap for each field – The object address is the index – r.f1 is sel(f1,r) and r.f1 = e is f1 := upd(f1,r,e)

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Hoare Rule for Field Writes To model writes correctly, we use heap expressions – A field write changes the heap of that field { B[upd(f, e 1, e 2 )/f] } e 1.f = e 2 {B} Important technique: model heap as a semantic object And defer reasoning about heap expressions to the theorem prover with inference rules such as (McCarthy): sel(upd(h, e 1, e 2 ), e 3 ) = e 2 if e 1 = e 3 sel(h, e 3 ) if e 1 e 3

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Example: Hoare Rule for Field Writes Consider again: { A } x.f = 5 { x.f + y.f = 10 } We obtain: A ´ (x.f + y.f = 10)[upd(f, x, 5)/f] ´ (sel(f, x) + sel(f, y) = 10)[upd(f, x, 5)/f] ´ sel(upd(f x 5) x) + sel(upd(f x 5) y) = 10 (*) ´ 5 + sel(upd(f, x, 5), y) = 10 ´ if x = y then 5 + 5 = 10 else 5 + sel(f, y) = 10 ´ x = y Ç y.f = 5 (**) To (*) is theorem generation. From (*) to (**) is theorem proving.

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Modeling new Statements Introduce – a new predicate isAllocated(e, t) denoting that object e is allocated at allocation time t – and a new variable allocTime denoting the current allocation time. Add background axioms: allocTime = 0 8 x t. isAllocated(x, t) ) isAllocated(x, t+1) isAllocated(null, 0) Translate new x.T() to havoc x; assume : isAllocated(x, allocTime); assume Type(x) = T; assume isAllocated(x, allocTime + 1); allocTime := allocTime + 1; **Translation of call to constructor x.T()**

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Invariant Generation

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Tools such as ESC/Java enable automated program verification by – automatically generating verification conditions and – automatically checking validity of the generated VCs. The user still needs to provide the invariants. – This is often the hardest part. Can we generate invariants automatically?

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Programs as Systems of Constraints 1: assume y ¸ z; 2: while x < y do x := x + 1; 3: assert x ¸ z `1`1 `1`1 `2`2 `2`2 `3`3 `3`3 ` err ` exit y ¸ z x ¸ z x ¸ y x < y Æ x = x + 1 x < z ½ 1 = move( ` 1, ` 2 ) Æ y ¸ z Æ skip(x,y,z) ½ 2 = move( ` 2, ` 2 ) Æ x < y Æ x = x + 1 Æ skip(y,z) ½ 3 = move( ` 2, ` 3 ) Æ x ¸ y Æ skip(x,y,z) ½ 4 = move( ` 3, ` err ) Æ x < z Æ skip(x,y,z) ½ 5 = move( ` 3, ` exit ) Æ x ¸ z Æ skip(x,y,z) move( ` 1, ` 2 ) = pc = ` 1 Æ pc = ` 2 skip(x 1,..., x n ) = x 1 = x 1 Æ... Æ x n = x n

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Program P = (V, init, R, error) V : finite set of program variables init : initiation condition given by a formula over V R : a finite set of transition constraints – transition constraint ½ 2 R given by a formula over V and their primed versions V – we often think of R as disjunction of its elements R = ½ 1 Ç... Ç ½ n error : error condition given by a formula over V

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P = (V, init, R, error) V = {pc, x, y, z} init = pc = ` 1 R = { ½ 1, ½ 2, ½ 3, ½ 4, ½ 5 } where ½ 1 = move( ` 1, ` 2 ) Æ y ¸ z Æ skip(x,y,z) ½ 2 = move( ` 2, ` 2 ) Æ x < y Æ x = x + 1 Æ skip(y,z) ½ 3 = move( ` 2, ` 3 ) Æ x ¸ y Æ skip(x,y,z) ½ 4 = move( ` 3, ` err ) Æ x < z Æ skip(x,y,z) ½ 5 = move( ` 3, ` exit ) Æ x ¸ z Æ skip(x,y,z) error = pc = ` err Programs as Systems of Constraints `1`1 `1`1 `2`2 `2`2 `3`3 `3`3 ` err ` exit y ¸ z x ¸ z x ¸ y x < y Æ x = x + 1 x < z

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Programs as Transition Systems states Q are valuations of program variables V initial states Q init are the states satisfying the initiation condition init Q init = {q 2 Q | q ² init } transition relation ! is the relation defined by the transition constraints in R q 1 ! q 2 iff q 1, q 2 ² R error states Q err are the states satisfying the error condition error Q err = {q 2 Q | q ² error }

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Partial Correctness of Programs a state q is reachable in P if it occurs in some computation of P q 0 ! q 1 ! q 2 !... ! qwhere q 0 2 Q init denote by Q reach the set of all reachable states of P a program P is safe if no error state is reachable in P Q reach Å Q err = ; or, if Q reach is expressed as a formula reach over V ² reach Æ error ) false

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state space Q error states Q err Partial Correctness of Programs reachable states Q reach initial states Q init

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Example: Reachable States of a Program 1: assume y ¸ z; 2: while x < y do x := x + 1; 3: assert x ¸ z Reachable states reach = pc = ` 1 Ç pc = ` 2 Æ y ¸ z Ç pc = ` 3 Æ y ¸ z Æ x ¸ y Ç pc = ` exit Æ y ¸ z Æ x ¸ y `1`1 `1`1 `2`2 `2`2 `3`3 `3`3 ` err ` exit y ¸ z x ¸ z x ¸ y x < y Æ x = x + 1 x < z What is the connection with invariants? Can we compute reach?

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Invariants of Programs an invariant Q I of a program P is a superset of its reachable states: Q reach µ Q I an invariant Q I is safe if it does not contain any error states: Q I Æ Q err = ; or if Q I is expressed as a formula I over V ² I Æ error ) false reach is the smallest invariant of P. In particular, if P is safe then so is reach.

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state space Q error states Q err Partial Correctness of Programs reachable states Q reach initial states Q init safe invariant Q I

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Strongest Postconditions The strongest postcondition post( ½,A) holds for any state q that is a ½ -successor state of some state satisfying A: q ² post( ½,A) iff 9 q 2 Q. q ² A Æ q,q ² ½ or equivalently post( ½, A) = ( 9 V. A Æ ½ ) [V/V] Compute reach by applying post iteratively to init

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Example: Application of post A = pc = ` 2 Æ y ¸ z ½ = move( ` 2, ` 2 ) Æ x < y Æ x = x + 1 Æ skip(y,z) post( ½, A) = ( 9 V. A Æ ½ ) [V/V] = ( 9 pc x y z. pc = ` 2 Æ y ¸ z Æ pc = ` 2 Æ pc = ` 2 Æ x

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Iterating post A, if i = 0 post(post i -1 ( ½, A) if i > 0

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Finite Iteration of post May Suffice

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Example Iteration ½ 1 = move( ` 1, ` 2 ) Æ y ¸ z Æ skip(x,y,z) ½ 2 = move( ` 2, ` 2 ) Æ x < y Æ x = x + 1 Æ skip(y,z) ½ 3 = move( ` 2, ` 3 ) Æ x ¸ y Æ skip(x,y,z) ½ 4 = move( ` 3, ` err ) Æ x < z Æ skip(x,y,z) ½ 5 = move( ` 3, ` exit ) Æ x ¸ z Æ skip(x,y,z) post 0 (R, init) = init = pc = ` 1 post 1 (R, init) = post( ½ 1, init) = pc = ` 2 Æ y ¸ z post 2 (R, init) = post( ½ 2, post(R, init)) Ç post( ½ 3, post(R, init)) = pc = ` 2 Æ y ¸ z Æ x · y Ç pc = ` 3 Æ y ¸ z Æ x ¸ y post 3 (R, init) = post( ½ 2, post 2 (R, init)) Ç post( ½ 3, post 2 (R, init)) Ç post( ½ 4, post 2 (R, init)) Ç post( ½ 5, post 2 (R, init))

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Example Iteration ½ 1 = move( ` 1, ` 2 ) Æ y ¸ z Æ skip(x,y,z) ½ 2 = move( ` 2, ` 2 ) Æ x < y Æ x = x + 1 Æ skip(y,z) ½ 3 = move( ` 2, ` 3 ) Æ x ¸ y Æ skip(x,y,z) ½ 4 = move( ` 3, ` err ) Æ x < z Æ skip(x,y,z) ½ 5 = move( ` 3, ` exit ) Æ x ¸ z Æ skip(x,y,z) post 2 (R, init) = post( ½ 2, post(R, init)) Ç post( ½ 3, post(R, init)) = pc = ` 2 Æ y ¸ z Æ x · y Ç pc = ` 3 Æ y ¸ z Æ x ¸ y post 3 (R, init) = post( ½ 2, post 2 (R, init)) Ç post( ½ 3, post 2 (R, init)) Ç post( ½ 4, post 2 (R, init)) Ç post( ½ 5, post 2 (R, init)) = pc = ` 2 Æ y ¸ z Æ x · y Ç pc = ` 3 Æ y ¸ z Æ x = y Ç pc = ` exit Æ y ¸ z Æ x · y Ç false

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Example Iteration post 3 (R, init) = = pc = ` 2 Æ y ¸ z Æ x · y Ç pc = ` 3 Æ y ¸ z Æ x ¸ y Ç pc = ` exit Æ y ¸ z Æ x · y post 4 (R, init) = post 3 (R, init) Fixed point: reach = post 0 (R, init) Ç post 1 (R, init) Ç post 2 (R, init) Ç post 3 (R, init) = pc = ` 1 Ç pc = ` 2 Æ y ¸ z Ç pc = ` 3 Æ y ¸ z Æ x ¸ y Ç pc = ` exit Æ y ¸ z Æ x · y

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Checking Safety An inductive invariant I contains the initial states and is closed under successors: ² init ) I and ² post(R, I ) ) I A program is safe if there exists a safe inductive invariant. reach is the strongest inductive invariant.

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Inductive Invariants for Example Program weakest inductive invariant: true – set of all states – contains error states strongest inductive invariant: reach pc = ` 1 Ç pc = ` 2 Æ y ¸ z Ç pc = ` 3 Æ y ¸ z Æ x ¸ y Ç pc = ` exit Æ y ¸ z Æ x ¸ y slightly weaker inductive invariant: pc = ` 1 Ç pc = ` 2 Æ y ¸ z Ç pc = ` 3 Æ y ¸ z Æ x ¸ y Ç pc = ` exit Can we drop another conjunct in one of the disjuncts?

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1: assume y ¸ z; 2: while x < y do x := x + 1; 3: assert x ¸ z Safe inductive invariant: pc = ` 1 Ç pc = ` 2 Æ y ¸ z Ç pc = ` 3 Æ y ¸ z Æ x ¸ y Ç pc = ` exit `1`1 `1`1 `2`2 `2`2 `3`3 `3`3 ` err ` exit y ¸ z x ¸ z x ¸ y x < y Æ x = x + 1 x < z Inductive Invariants for Example Program

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Computing Inductive Invariants We can compute the strongest inductive invariants by iterating post on init. Can we ensure that this process terminates? In general no: checking safety of programs is undecidable. But we can compute weaker inductive invariants – conservatively abstract the behavior of the program – iterate an abstraction of post that is guaranteed to terminate.

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PSUCS322 HM 1 Languages and Compiler Design II Formal Semantics Material provided by Prof. Jingke Li Stolen with pride and modified by Herb Mayer PSU Spring.

PSUCS322 HM 1 Languages and Compiler Design II Formal Semantics Material provided by Prof. Jingke Li Stolen with pride and modified by Herb Mayer PSU Spring.

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