Sumit GulwaniMicrosoft Research, Redmond, USA

sumitg@microsoft.com

The Fixpoint Brushin

The Art of Invariant Generation

Workshop on Invariant Generation, Floc 2010

1. Program Transformations– Reduce need for sophisticated invariant generation.– E.g., control-flow refinement (PLDI ‘09), loop-flattening/peeling (PLDI ‘10/POPL ’08) non-standard cut-points (PLDI ‘08) quantitative attributes instrumentation (POPL ’10).

2. Colorful Logic– Language of Invariants.– E.g., arithmetic, uninterpreted fns, lists/arrays.

3. Fixpoint Brush– Automatic generation of invariants in some shade of

logic (such as conjunctive/k-disjunctive/predicate abstraction).

– E.g., Iterative, Template/Constraint based, Proof rules.2

Art of Invariant Generation

Fixpoint Brush

• Iterative and monotonic Forward – Backward

• Template/Constraint based

• Proof rules

• Iterative, but non-monotonic– Probabilistic Inference– Learning

3

Start with Precondition and propagate facts forward using Existential elimination operator until fixpoint.

• Data-flow analysis– Join at merge points– Finite abstract domains

• Abstract Interpretation– Join at merge pointsJoin(1 , 2) = strongest s.t. 1 ) and 2 ) – Widening for infinite height abstract domains.

• Model Checking– Analyze all paths precisely without join at merge

points.– Finite abstractions (e.g., boolean abstraction)

required• Counterexample guided abstraction refinement• BDD based data-structures for efficiency

4

Iterative Forward: Examples

Difference Constraints

• Abstract element: – conjunction of xi-xj · cij – can be represented using matrix M, where M[i]

[j]=cij

• Decide(M):1. M’ := Saturate(M);

• Saturation means closure with deductions of form: x-y ≤ c1 and y-z ≤ c2 implies x-z ≤ c1 +c2

2. Declare unsat iff 9i: M’[i][i] < 0

• Join(M1, M2): 1. M’1 := Saturate(M1); M’2 := Saturate(M2);

2. Let M3 be s.t. M3[i][j] = Max { M’1[i][j], M’2[i][j] }

3. return M3 5

1·y<51 Æ z=y+2

Example: Abstract Interpretation using Difference Constraints

y < 50

y := 0; z := 2;

y++; z++;

y=0, z=2

y=1 Æ z=3

?

?

?

?

true

y=0 Æ z=2

y=0 Æ z=2

0·y·1 Æ z=y+2

0·y·1 Æ z=y+2

1·y·2 Æ z=y+2

0·y·2 Æ z=y+20·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

FalseTrue

Assert (z=52)

6

y=F(a)a=ha,bi

z=a-1a=ha,bi

y=F(b)b=ha,bi

z=b-1b=ha,bi

z=a-1 Æ y=F(a)

z=b-1 Æ y=F(b)

ha,bi=1+z

y=F(ha,bi)

JoinlaJoinuf

Eliminateuf+la

y=F(1+z)

{ ha,bi }

Joinla+u

f

Combination: Example of Join Algorithm

7

• Uninterpreted Functions– A Polynomial-Time Algorithm for Global Value Numbering;

Gulwani, Necula; SAS ‘04 • Linear Arithmetic + Uninterpreted Functions

– Combining Abstract Interpreters; Gulwani, Tiwari; PLDI ’06

• Theory of Arrays/Lists – Quantified Abstract Domains

• Lifting Abstract Interpreters to Quantified Logical Domains; Gulwani, McCloskey, Tiwari; POPL ‘08

• Discovering Properties about Arrays in Simple Programs; Halbwachs, Péron; PLDI ‘08

– Shape Analysis• Parametric Shape Analysis via 3-Valued Logic;

Sagiv, Reps, Wilhelm; POPL ‘99, TOPLAS ‘028

Iterative Forward: References

• Theory of Arrays/Lists – Combination of Shape Analysis + Arithmetic

• A Combination Framework for tracking partition sizes; Gulwani, Lev-Ami, Sagiv; POPL ’09

• Non-linear Arithmetic– User-defined axioms + Expression Abstraction

• A Numerical Abstract Domain Based on Expression Abstraction and Max Operator with Application in Timing Analysis; Gulavani, Gulwani; CAV ’08

– Polynomial Equalities• An Abstract Interpretation Approach for Automatic

Generation of Polynomial Invariants; Rodriguez-Carbonell, Kapur; SAS ‘04

9

Iterative Forward: References

Fixpoint Brush

• Iterative and monotonic– Forward Backward

• Template/Constraint based

• Proof-rules

• Iterative, but non-monotonic– Probabilistic Inference– Learning

10

• Comparison with Iterative Forward– Positives: Can compute preconditions, Goal-directed– Negatives: Requires assertions or template assertions.

• Transfer Function for Assignment Node is easier.– Substitution takes role of existential elimination.

• Transfer Function for Conditional Node is challenging.– Requires abductive reasoning/under-approximations.

• Abduct(,g) = weakest ’ s.t. ’Æg ) • Case-split reasoning as opposed to Saturation based, and

hence typically not closed under conjunctions.• Optimally weak solutions for negative unknowns as

opposed to optimally strong solutions for positive unknowns.

11

Iterative Backward

• Program Verification using Templates over Predicate Abstraction;

Srivastava, Gulwani; PLDI ‘09• Assertion Checking Unified; Gulwani, Tiwari; VMCAI ‘07• Computing Procedure Summaries for Interprocedural

Analysis; Gulwani, Tiwari; ESOP ‘07

12

Iterative Backward: References

Fixpoint Brush

• Iterative and monotonic– Forward– Backward

Template/Constraint based

• Proof rules

• Iterative, but non-monotonic– Probabilistic Inference– Learning

13

Template-based Invariant Generation• Goal-directed invariant generation for

verification of a Hoare triple (Pre, Program, Post)

14

Prewhile (c) SPost

Pre ) II Æ :c ) Post(I Æ c)[S] ) I

9 II 8X

Verification Constraint(Second-order)

Base CasePrecisionInductive

Case

VCGen

• Key Idea: Reduce the second-order verification constraint to a first-order satisfiability constraint that can be solved using off-the-shelf SAT/SMT solvers– Choose a template for I (specific color/shade in some

logic).– Convert 8 into 9.

Trick for converting 8 to 9 is known for following domains:

• Linear Arithmetic– Farkas Lemma

• Linear Arithmetic + Uninterpreted Fns.– Farkas Lemma + Ackerman’s Reduction

• Non-linear Arithmetic– Grobner Basis

• Predicate Abstraction– Boolean indicator variables + Cover Algorithm

(Abduction)• Quantified Predicate Abstraction

– Boolean indicator variables + More general Abduction15

Key Idea in reducing 8 to 9 for various Domains

• Linear Arithmetic– Constraint-based Linear-relations analysis; Sankaranarayanan, Sipma, Manna; SAS ’04– Program analysis as constraint solving; Gulwani, Srivastava, Venkatesan; PLDI ‘08

• Linear Arithmetic + Uninterpreted Fns.– Invariant synthesis for combined theories; Beyer, Henzinger, Majumdar, Rybalchenko; VMCAI ‘07

• Non-linear Arithmetic– Non-linear loop invariant generation using Gröbner bases;

Sankaranarayanan, Sipma, Manna; POPL ’04• Predicate Abstraction

– Constraint-based invariant inference over predicate abstraction; Gulwani, Srivastava, Venkatesan; VMCAI ’09

• Quantified Predicate Abstraction– Program verification using templates over predicate

abstraction; Srivastava, Gulwani; PLDI ‘0916

Template-based Invariant Generation: References

Linear Arithmetic• Linear Arithmetic + Uninterpreted Fns.• Non-linear Arithmetic• Predicate Abstraction• Quantified Predicate Abstraction

17

Template-based Invariant Generation

Farkas Lemma

8X Æk(ek¸0) ) e¸0

iff 9k¸0 8X (e ´ + kkek)

Example • Let’s find Farkas witness ,1,2 for the implication

x¸2 Æ y¸3 ) 2x+y¸6• 9 ,1,2¸0 s.t. 8x,y [2x+y-6 ´ + 1(x-2) + 2(y-3)]• Equating coefficients of x,y and constant terms, we

get: 2=1 Æ 1=2 Æ -6=-21-32

which implies =1, 1=2, 2=1.18

Solving 2nd order constraints using Farkas Lemma

9I 8X 1(I,X)

• Second-order to First-order– Assume I has some form, e.g., j ajxj ¸ 0– 9I 8X 1(I,X) translates to 9aj 8X 2(aj,X)

• First-order to “only existentially quantified”– Farkas Lemma helps translate 8 to 9– 8X (Æk(ek¸0) ) e¸0) iff 9k¸0 8X (e ´ + kkek)

• Eliminate X from polynomial equality by equating coefficients.

– 9aj 8X 2(aj,X) translates to 9aj 9¸k 3(aj,¸k)

• “only existentially quantified” to SAT– Bit-vector modeling for integer variables 19

Example

[n=1 Æ m=1]x := 0; y := 0;while (x <

100) x := x+n; y := y+m;[y ¸ 100]

a0 + a1x + a2y + a3n + a4m ¸ 0b0 + b1x + b2y + b3n + b4m ¸ 0

y ¸ x m ¸ 1n · 1

a2=b0=c4=1, a1=b3=c0=-1

a0 + a1x + a2y + a3n + a4m ¸ 0b0 + b1x + b2y + b3n + b4m ¸ 0c0 + c1x + c2y + c3n + c4m ¸ 0

y ¸ x m ¸ n

a2=b2=1, a1=b1=-1

a0 + a1x + a2y + a3n + a4m ¸ 0

Invalid triple or Imprecise Template

UNSAT

Invariant Template Satisfying Solution Loop Invariant

I

n=m=1 Æ x=y=0 ) I

I Æ x¸100 ) y¸100

I Æ x<100 ) I[xÃx+n, yÃy+m]

VCGen

• Linear Arithmetic• Linear Arithmetic + Uninterpreted Fns.• Non-linear Arithmetic Predicate Abstraction• Quantified Predicate Abstraction

21

Template-based Invariant Generation

Solving 2nd order constraints using Boolean Indicator Variables + Cover Algorithm

9I 8X 1(I,X)

• Second-order to First-order (Boolean indicator variables)– Assume I has the form P1 Ç P2, where P1, P2 µ P Let bi,j denote presence of pj2P in Pi

Then, I can be written as (Æj b1,j)pj) Ç (Æj b2,j)pj) 9I 8X 1(I,X) translates to 9bi,j 8X 2(bi,j,X)– Can generalize to k disjuncts

• First-order to “only existentially quantified”– Cover Algorithm helps translate 8 to 9– 9bi,j 8X 2(bi,j,X) translates to SAT formula 9bi,j 3(bi,j)

22

Example

[m > 0]x := 0; y :=

0;while (x <

m) x :=

x+1; y :=

y+1;[y=m]

I m>0 ) I[xÃ0, yÃ0] I Æ x¸m ) y=m I Æ x<m ) I[xÃx+1, yÃy+1]

VCGen

x·y, x¸y, x<yx·m, x¸m, x<my·m, y¸m, y<m

Suppose P = and k = 1

Then, I has the form P1, where P1 µ P m>0 ) P1[xÃ0, yÃ0] (1)P1 Æ x¸m ) y=m (2)P1 Æ x<m ) P1[xÃx+1, yÃy+1] (3)

Example

x·y, x¸y, x<yx·m, x¸m, x<my·m, y¸m, y<m

0·0, 0¸0, 0<00·m, 0¸m, 0<m0·m, 0¸m, 0<m

(1) m>0 ) P1 [xÃ0, yÃ0]

Hence, (1) ´ P1 doesn’t contain x<y, x¸m, y¸m ´ :bx¸m Æ :bx<y Æ :by¸m

xÃ0, yÃ0

(2) P1 Æ x¸m ) y=m There are 3 maximally-weak choices for P1

(computed using Predicate Cover Algorithm)

Hence, (2) ´ P1 contains at least one of above combinations ´ (bx<m) Ç (by·m Æ by¸m)Ç (bx·y Æ by·m)

(i) y·m Æ y¸m(ii) x<m(iii) x·y Æ y·m

Example

(1) :bx¸m Æ :bx<y Æ :by¸m

(2) (bx<m)Ç (by·mÆby¸m)Ç (bx·y Æ by·m)

(3) (by·m ) (by<mÇby·x))Æ:bx<mÆ:by<m

by·x , by·m , bx·y : true

rest: false

SAT Solver

I: (y=x Æ y·m)

[m > 0]x := 0; y :=

0;while (x < m) x := x+1; y := y+1;[y=m]

I

Obtained from solvinglocal/small SMT queries

Going beyond Invariant Generation with Constraint-based techniques…

26

Bonus Material

Where can we go?

Postcondition: The best fit line shouldn’t deviate more than half a pixel from the real line, i.e., |y – (Y/X)x| · 1/2

27

Example: Bresenham’s Line Drawing Algorithm

[0<Y·X]v1:=2Y-X; y:=0; x:=0;while (x · X) out[x] := y; if (v1<0) v1:=v1+2Y; else v1:=v1+2(Y-X); y++;return out;[8k (0·k·X ) |out[k]–(Y/X)k| · ½)]

28

Transition System Representation

[0<Y·X]v1:=2Y-X; y:=0; x:=0;while (x·X) v1<0: out’=Update(out,x,y) Æ v’1=v1+2Y Æ y’=y Æ

x’=x+1 v1¸0: out’=Update(out,x,y) Æ v1=v1+2(Y-X) Æ

y’=y+1Æ x’=x+1[8k (0·k·X ) |out[k]–(Y/X)k| · ½)] [Pre]

sentry;while (gloop) gbody1: sbody1; gbody2: sbody2;[Post]

Where, gbody1: v1<0gbody2: v1¸0gloop: x·Xsentry: v1’=2Y-X Æ y’=0 Æ x’=0sbody1: out’=Update(out,x,y) Æ v’1=v1+2Y Æ x’=x+1 Æ

y’=ysbody2: out’=Update(out,x,y) Æ v’1=v1+2(Y-X) Æ x’=x+1

Æ y’=y+1

Or, equivalently,

29

Verification Constraint Generation & SolutionPre Æ sentry ) I’I Æ gloop Æ gbody1 Æ sbody1 )

I’I Æ gloop Æ gbody2 Æ sbody2 )

I’I Æ :gloop ) Post

0<Y·X Æ v1=2(x+1)Y-(2y+1)X Æ 2(Y-X)·v1·2Y Æ 8k(0·k·x ) |out[k]–(Y/X)k| · ½)

Given Pre, Post, gloop, gbody1, gbody2, sbody1, sbody2, we can find solution for I using constraint-based techniques.

Verification Constraint:

I:

30

The Surprise!

Verification Constraint:

Pre Æ sentry ) I’I Æ gloop Æ gbody1 Æ sbody1 )

I’I Æ gloop Æ gbody2 Æ sbody2 )

I’I Æ :gloop ) Post

• What if we treat each g and s as unknowns like I?• We get a solution that has gbody1 = gbody2 = false.

– This doesn’t correspond to a valid transition system.– We can fix this by encoding gbody1 Ç gbody2 = true.

• We now get a solution that has gloop = true.

– This corresponds to a non-terminating loop.– We can fix this by encoding existence of a ranking

function.• We now discover each g and s along with I.

– We have gone from Invariant Synthesis to Program Synthesis.

Fixpoint Brush

• Iterative and monotonic– Forward– Backward

• Template/Constraint based

Proof rules

• Iterative, but non-monotonic– Probabilistic Inference– Learning

31

• Problem specific.

• Requires understanding of commonly used design patterns.

• Can provide the most scalable solution, if applicable.

Case Study: Ranking function computation for estimating loop bounds.

32

Proof Rules

Consider the loop while (cond) X := F(X)Transition system representation s: cond Æ

X’=F(X)

Example: Transition system representation of the loop while (x < n) {x++; n--;} is x<n Æ x’=x+1 Æ n’=n-1

An integer valued function r is a ranking function for transition s if

s ) (r>0) s ) r[X’/X]·r-1We denote this by Rank(r,s).

33

Ranking Function

• Iterative Forward– Instrument counter c and find an upper bound n.

n-c is a ranking function. (Gulwani, Mehra, Chilimbi, POPL ‘09)

• Constraint-based– Assume a template a0 + iaixi for the ranking function r

and then solve for ai’s in the constraint

9ai (s ) (r>0 Æ r[X’/X]·r-1) using Farkas lemma– Goal directed– Complete PTIME method for synthesis of linear ranking

fns.• Podelski, Rybalchenko; VMCAI ‘04

• Proof Rules– “The Reachability Bound Problem”, Gulwani, Zuleger,

PLDI ‘1034

Finding Ranking Functions

If s ) (e>0 Æ e[X’/X] < e), then Rank(e,s).Candidates for e: e1-e2, for any conditionals e1 >

e2 in s

• n>0 Æ n’·n Æ A[n]A[n’] – Ranking function: n

If s ) (e¸1 Æ e[X’/X] · e/2), then Rank(log e,s).Candidates for e: e1/e2 for any conditionals e1 > e2

in s

• i’=i/2 Æ i>1– Ranking function: log (i)

• i’=2£i Æ i>0 Æ n>i Æ n’=n – Ranking function: log (n/i)

35

Arithmetic Iteration Patterns

If s ) (RSB(e’) < RSB(e) Æ e0), then Rank(RSB(e),s)Candidates for e: all bitvector variables x in s• x’=x&(x-1) Æ x0

– Ranking function: x•

– Ranking function: RSB(x)/2

RSB: Position of rightmost significant 1-bit

36

Bit-vector Iteration Pattern

Input: bitvector b while (BitScanForward(&id1,b)) b := b | ((1 << id1)-1); if (BitScanForward(&id2,~x)

break; b := b & (~((1 << id2)-1);

Let Rank(r1,s1) and Rank(r2,s2).Non-Interference NI(s1,s2,r2):• Non-enabling condition: s1 ± s2 = false• Rank preserving condition: s1 ) r2[x’/x] · r2

If NI(s1,s2,r2) and NI(s2,s1,r1), then Max(0,r1) + Max(0,r2) is a ranking fn for s1 Ç s2.

If NI(s1,s2,r2) then (r1,r2) is a lexicographic ranking fn for s1 Ç s2.

37

Composing ranking functions

Useful for backward symbolic execution across loops.

If s ) e’ ≤ e + c, then eafter ≤ ebefore + c£Rank(s)

38

Relating values before/after a loop

n := mwhile (e) { …… n :=

n+3; ……}

nafter · nbefore + 3£Rank(s)

Fixpoint Brush

• Iterative and monotonic– Forward– Backward

• Template/Constraint based

• Proof-rules

• Iterative, but non-monotonic– Probabilistic Inference– Learning

39

Undergraduate Textbook on Algorithms by Cormen, Leiserson, Rivest, Stein describes 3 fundamental methods for recurrence solving:

• Iteration Method– Expands/unfolds the recurrence relation

40

Recurrence Solving Techniques

Expand (unfold) the recurrence and express it as a summation of terms.

Consider the recurrence:T(n) = n + 3T(n/4)

T(n) = n + 3(n/4) + 9 T(n/16) = n + 3(n/4) + 9(n/16) + 27 T(n/64) ≤ n + 3(n/4) + 9(n/16) + 27(n/64) + … + 3log

4n θ(1)

≤ [n ∑i≥0 (3/4)i]+ θ(3log4n)

= 4n + o(n) = O(n)

41

Iteration Method

Undergraduate Textbook on Algorithms by Cormen, Leiserson, Rivest, Stein describes 3 fundamental methods for recurrence solving:

Example of a recurrence: T(n)=T(n-1)+2n Æ T(0)=0

• Iteration Method– Expands/unfolds the recurrence relation– Similar to Iterative approach

• Substitution Method– Assumes a template for a closed-form

42

Recurrence Solving Techniques vs. Our Fixpoint Brush

Recurrence Solving Techniques vs. Our Fixpoint Brush

Involves guessing the form of the solution and then substituting it in the equations to find the constants.

Consider the recurrence: T(n)=T(n-1)+2n Æ T(0)=0

• Let T(n) ≤ an2 + bn + c for some a,b,c• T(n) ≤ a(n-1)2+b(n-1)+c + 2n = an2 + (b-2a+2)n

+ (a+c-b) This implies (b-2a+2) ≤ b and (a+c-b) ≤ c• T(0) = c This implies c=0• Hence, b ≥ a ≥ 1

43

Substitution Method

Undergraduate Textbook on Algorithms by Cormen, Leiserson, Rivest, Stein describes 3 fundamental methods for recurrence solving:

Example of a recurrence: T(n)=T(n-1)+2n Æ T(0)=0

• Iteration Method– Expands/unfolds the recurrence relation– Similar to Iterative approach

• Substitution Method– Assumes a template for a closed-form– Similar to Template/Constraint-based approach

• Master’s Method– Provides a cook-book solution for T(n)=aT(n/b)

+f(n) 44

Recurrence Solving Techniques vs. Our Fixpoint Brush

Recurrence Solving Techniques vs. Our Fixpoint Brush

Provides a cook-book method for solving recurrences of the form T(n) = aT(n/b)+f(n), where a≥1 and b>1.

• If f(n) = O(nlogba – є) for some constant є > 0,

then T(n) = θ(nlogba).

• If f(n) = θ(nlogba), then T(n) = θ(nlog

ba lg n).

• If f(n) = O(nlogba + є) for some constant є > 0,

and if a f(n/b) ≤ c f(n) for some constant c < 1 and all sufficiently large n, then T(n) = θ(f(n)).

Requires memorization of three cases, but then the solution of many recurrences can be determined quite easily, often with pencil and paper.

45

Master’s Method

Undergraduate Textbook on Algorithms by Cormen, Leiserson, Rivest, Stein describes 3 fundamental methods for recurrence solving:

Example of a recurrence: T(n)=T(n-1)+2n Æ T(0)=0

• Iteration Method– Expands/unfolds the recurrence relation– Similar to Iterative approach

• Substitution Method– Assumes a template for a closed-form– Similar to Template/Constraint-based approach

• Master’s Method– Provides a cook-book solution for T(n)=aT(n/b)

+f(n)– Similar to Proof-rules approach

46

Recurrence Solving Techniques vs. Our Fixpoint Brush

Recurrence Solving Techniques vs. Our Fixpoint Brush

Fixpoint Brush

• Iterative and monotonic– Forward– Backward

• Template/Constraint based

• Proof-rules

• Iterative, but non-monotonic (Distance to fixed-point decreases in each iteration).

Probabilistic Inference– Learning

47

Prog. Point

Invariant

entry x=0

1 x=0 Æ y=50

2 x·50 )y=50 Æ 50·x )x=y Æ x·100

3 x·50 )y=50 Æ 50·x )x=y Æ x<100

4 x<50 Æ y=50

5 x·50 Æ y=50

6 50·x<100 Æ x=y

7 50<x·100 Æ x=y

8 x·50 )y=50 Æ 50·x )x=y Æ x·100

exit y=100

Example

y := 50;

y = 100

False

x := x+1; x := x+1;y := y+1;

x < 50

x<100

True

True False

1

2

3

4

5

6

7

8

x = 0 Proof of Validity

entry

exit

48

Probabilistic Inference for Program Verification

• Initialize invariants at all program points to any element (from an abstract domain over which the proof exists)

• Pick a program point (randomly) whose invariant is locally inconsistent & update it to make it less inconsistent.

49

Consistency of an invariant I at program point

I is consistent at iff Post() ) I Æ I ) Pre()• Post() is the strongest postcondition of “the invariants at

the predecessors of ” at • Pre() is the weakest precondition of “the invariants at

the successors of ” at • Example:

I

Q

2

P

R

c

1

3 4

• Post(2) = StrongestPost(P,s)

• Pre(2) = (c ) Q) Æ (: c ) R)

s

50

Measuring Inconsistency of an invariant I at

Local inconsistency of invariant I at program point =

IM(Post(), I) + IM(I, Pre())

Where the inconsistency measure IM(1, 2) is some approximation of the number of program states that violate 1 ) 2

51

Example of an inconsistency measure IM

Consider the abstract domain of Boolean formulas (with the usual implication as the partial order).

Let 1 ´ a1 Ç … Ç an in DNF

and 2 ´ b1 Æ … Æ bm in CNF

IM(1, 2) = (ai,bj)

where (ai,bj) = 0, if ai ) bj

= 1, otherwise

52

• Program Verification as Probabilistic Inference; Gulwani, Jojic; POPL ’07

• Learning Regular Sets from Queries and Counterexamples;

Angluin; Information and Computing ‘87– Learning Synthesis of interface specifications for Java

classes; Alur, Madhusudan, Nam; POPL ‘05. – Learning meets verification;

Leucker; FMCO ‘06

• May/Must Analyses?

• Game-theoretic Analyses?

53

Iterative but non-monotonic: References

Fixpoint Brush: Summary

• Iterative and monotonic– Forward

• Requires method for Join, Existential Elimination– Backward

• Requires method for Abduct

• Template/Constraint based– Works well for small code-fragments– Requires a method to convert 8 to 9

• Proof rules– Scalable– Requires understanding of design patterns

• Iterative, but non-monotonic– Perhaps better for dealing with noisy systems.

54