Transcript Network Protocols

BDDs & Theorem Proving
Binary Decision Diagrams
Dr. Eng. Amr T. Abdel-Hamid
Network Protocols
NETW 703
Lectures are based on slides by:
• K. Havelund & Agroce, Reliable Software: Testing and Monitoring, CMU.
• E. Clarke, Formal Methods, to be updated by course name
•S. Tahar, E. Cerny and X. Song, “ Formal Verification of Systems”.
Winter 2012
Binary Decision Diagrams
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 Ordered binary decision diagrams (OBDDs) are a canonical fo
rm for Boolean formulas.
 OBDDs are often substantially more compact than traditional n
ormal forms.
 Moreover, they can be manipulated very efficiently.
 Introduced at:
 R. E. Bryant. Graph-based algorithms for boolean function manip
ulation. IEEE Transactions on Computers, C-35(8), 1986.
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Binary Decision Trees
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 A Binary decision tree is a rooted, directed tree with two types
of vertices, terminal vertices and nonterminal vertices.
 Each nonterminal vertex v is labeled by a variable var(v) and h
as two successors:
 low (v) corresponding to the case where the variable is assign
ed 0, and high (v) corresponding to the case where the variabl
e is assigned 1.
 Each terminal vertex v is labeled by value(v) which is either 0
or 1
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Example
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 BDT for a two-bit comparator, f(a1,a2,b1,b2) = (a1  b1)  (a
2  b2)
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Binary Decision Diagram
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 i.e. exactly like decision TREE
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Reduced Ordered BDDs
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 In practical applications, it is desirable to have a canonical repr
esentation for Boolean functions.
 This simplifies tasks like checking equivalence of two formulas
and deciding if a given formula is satisfiable or not.
 Such a representation must guarantee that two Boolean functi
ons are logically equivalent if and
 only if they have isomorphic representations.
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Reduced Ordered BDD
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 Canonical Form property
 A canonical representation for Boolean functions is desirable:
 two Boolean functions are logically equivalent iff they have isomo
rphic representations
 This simplifies checking equivalence of two formulas and deciding if
a formula is satisfiable
 Two BDDs are isomorphic if there exists a bijection h between the g
raphs such that
 Terminals are mapped to terminals and nonterminals are mapped to
nonterminals
 For every terminal vertex v, value(v) = value(h(v)), and
 For every nonterminal vertex v: var(v) = var(h(v)), h(low(v)) = low(
h(v)), and h(high(v)) = high(h(v))
Canonical Form property
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 Bryant (1986) showed that BDDs are a canonical repr
esentation for Boolean functions under two restrictions:
 the variables appear in the same order along each path from th
e root to a terminal
 there are no isomorphic subtrees or redundant vertices
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Reduced Ordered Binary Decision D
iagrams (ROBDDs): CREATION
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 Canonical Form Property
 Requirement (1): Impose total order “<” on the variables in the for
mula: if vertex u has a nonterminal successor v, then var(u) < var(
v)
 Requirement (2): repeatedly apply three transformation rules (or i
mplicitly in operations such as disjunction or conjunction)
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RoBDD Creation
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1) Remove duplicate terminals: eliminate all but one terminal v
ertex with a given label and redirect all arcs to the eliminated v
ertices to the remaining one
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Comparator Example
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RoBDD Creation
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2. Remove duplicate nonterminals: if nonterminals u and v hav
e var(u) = var(v), low(u) = low(v) and high(u) = high(v), eliminat
e one of the two vertices and redirect all incoming arcs to the o
ther vertex
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3. Remove redundant tests: if nonterminal vertex v has low(v) =
high(v), eliminate v and redirect all incoming arcs to low(v)
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ROBDD Example
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 Creating the ROBDD for (x⊕y⊕z)
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Canonical Form Property (cont’d)
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 A canonical form is obtained by applying the transformation rul
es until no further application is possible
 Bryant showed how this can be done by a procedure called Re
duce in linear time
 Applications:
 checking equivalence: verify isomorphism between ROBDDs
 non-satisfiability: verify if ROBDD has only one terminal node, lab
eled by 0
 tautology: verify if ROBDD has only one terminal node, labeled by
1
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Variable Ordering Problem
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Variable Ordering Problem
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 The problem of finding the optimal variable order is NP-complete
 Some Boolean functions have exponential size ROBDDs for any order (e.g., multiplier)
 Heuristics for Variable Ordering
 Heuristics developed for finding a good variable order (if it exists)
 Intuition for these heuristics comes from the observation that ROBDDs tend to be
smaller when related variables are close together in the order
 Variables appearing in a subcircuit are related: they determine the subcircuit’s out
put should usually be close together in the order
 Dynamic Variable Ordering
 Useful if no obvious static ordering heuristic applies
 During verification operations (e.g., reachability analysis) functions change, hence
initial order is not good later on
 Good ROBDD packages periodically internally reorder variables to reduce ROBD
D size
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 Basic approach based on neighboring variable exchange
 Among a number of trials the best is taken, and the exchange is repeated
Model Checking
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 The Good:
 If it works, model checking (unlike theorem proving) is a pus
h-button tool.
 The Bad:
 If the system is too large, model checking cannot be applied
because of state explosion.
 & The Ugly
 The system (and/or property) then needs to be suitably “abst
racted” in order to use model checking.
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Approximate Model Checking
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 Representing exact state sets may involve large BDDs
Compute approximations to reachable states
 Potentially smaller representation
 Over-approximation :
 No bugs found Circuit verified correct
 Bugs found may be real or false
 Under-approximation :
 Bug found Real bug
 No bugs found Circuit may still contain bugs
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Reachable states
Buggy states
Theorem Proving
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 Prove that an implementation satisfies a specification by mathematical re
asoning
 Implementation and specification expressed as formulas in a formal logic
 Required relationship (logical equivalence/logical implication) described as a
theorem to be proven within the context of a proof calculus
 A proof system:
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 A set of axioms and inference rules (simplification, rewriting, induction, etc.)
Theorem Proving Idea
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 Properties specified in a Logical Language (SPEC)
 System behavior also in the same language (DES)
 Establish (DES -> SPEC) as a theorem.
 A logical System:
 A language defining constants, functions and predicates
 A no. of axioms expressing properties of the constants, function, types, e
tc.
 Inference Rules
 A Theorem
 `follows' from axioms by application of inference rules has a proof
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First-Order Logic
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 Propositional logic: reasoning about complete sentences.
 First-order logic: also reasoning about individual objects and rel
ationships between them.
 Syntax
 Objects (in FOL) are denoted by expressions called terms:
 Constants a, b, c,... ; Variables u, v, w,... ;
 f(t1, t2,..., tn) where t1, t2,..., tn are terms and f a function symbol
of n arguments
 Predicates:
 true (T) and false (F)
 p(t1, t2,..., tn) where t1, t2,..., tn are terms and p a predicate symb
ol of n arguments
First-Order Logic (cont.)
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 Formulas:
 Predicates:
P and Q formulas, then P, P  Q, P  Q, P  Q,
P  Q are formulas
 x a variable, P a formula, then x.P, x.Q are formulas
(x is not free in P, Q)
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First-Order Logic (cont’d)
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 The Validity Problem of FOL
 To decide the validity for formulas of FOL, the truth table method
does not work!
 Reason: must deal with structures not just truth assignments.
 Structures need not be finite ...
 Semi-decidable (partially solvable)
 There is an algorithm which starts with an input, and
1. if the input is valid then it terminates after a finite number of
steps, and outputs the correct value (Yes or No)
2. if the input is not valid then it reaches a reject halt or loops fo
rever
 Theorem (Church-Turing, 1936)
The validity problem for formulas of FOL is undecidable, but semi-de
cidable.
 Some subsets of FOL are decidable.
Higher-Order Logic
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 First-order logic: only domain variables can be quantified.
 Second-order logic: quantification over subsets of variables (i.e., over
predicates).
 Higher-order logics: quantification over arbitrary predicates and functi
ons.
 Higher-Order Logic:
 Variables can be functions and predicates,
 Functions and predicates can take functions as arguments a
nd return functions as values,
 Quantification over functions and predicates.
 Since arguments and results of predicates and functions can the
mselves be predicates or functions, this imparts a first-class stat
us to functions, and allows them to be manipulated just like or
dinary values
HOL
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 Example 1: (mathematical induction)
 P. [P(0)  (n. P(n)  P(n+1))]   n.P(n)
(Impossible to express it in FOL)
 Example 2: Function Rise defined as
Rise (c, t) = c(t)  c(t+1)
 Rise expresses the notion that a signal c rises at time t.
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Higher-Order Logic
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 Advantage:
 high expressive power!
 Disadvantages:
 Incompleteness of a sound proof system for most higher-order log
ics
 Theorem (Gödel, 1931)
 “There is no complete deduction system for the second-order logi
c”
 Inconsistencies can arise in higher-order systems if semantics not ca
refully defined
 “Russell Paradox”:
 Let P be defined by P(Q) = ¬Q(Q).
 By substituting P for Q, leads to P(P) = ¬P(P),
Theorem Proving Systems
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 Some theorem proving systems:
 Boyer-Moore (first-order logic)
 HOL (higher-order logic)
 PVS (higher-order logic)
 Lambda (higher-order logic)
From PVS website:
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“PVS is a large and complex system and it takes a l
ong while to learn to use it effectively. You should b
e prepared to invest six months to become a modera
tely skilled user”
HOL
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 HOL (Higher-Order Logic) developed at University of Cambridge
 Interactive environment (in ML, Meta Language) for machine assiste
d theorem proving in higherorder logic (a proof assistant)
 Steps of a proof are implemented by applying inference rules chosen
by the user; HOL checks that the steps are safe
 All inferences rules are built on top of eight primitive inference rules
 Mechanism to carry out backward proofs by applying built-in ML func
tions called tactics and tacticals
 By building complex tactics, the user can customize proof strategies
 Numerous applications in software and hardware verification
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HOL
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 HOL provides considerable built-in theorem-proving infrastructure:
 a powerful rewriting subsystems
 library facility containing useful theories and tools for general use
 Decision procedures for tautologies and semi-decision
 procedure for linear arithmetic provided as libraries
 The approach to mechanizing formal proof used in HOL is due to Ro
bin Milner.
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Proof Styles in HOL
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 Forward proof style:
Goal-directed (or Backward) proof style:
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Backward Proofs
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Example 1: Logic AND
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 AND Specification:
AND_SPEC (i1,i2,out) := out = i1 ∧ i2
 NAND specification:
NAND (i1,i2,out) := out = ¬(i1 ∧ i2)
 NOT specification:
NOT (i, out) := out = ¬ I
 AND Implementation:
AND_IMPL (i1,i2,out) := ∃x. NAND (i1,i2,x) ∧ NOT (x,
out)
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Example 1: Logic AND
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 Proof Goal:
∀ i1, i2, out. AND_IMPL(i1,i2,out) ⇒ ANDSPEC(i1,i2,out)
 Proof (forward)
AND_IMP(i1,i2,out) {from above circuit diagram}
∃ x. NAND (i1,i2,x) ∧ NOT (x,out) {by def. of AND impl}
NAND (i1,i2,x) ∧ NOT(x,out) {strip off “∃ x.”}
NAND (i1,i2,x) {left conjunct of line 3}
x =¬ (i1 ∧ i2) {by def. of NAND}
NOT (x,out) {right conjunct of line 3}
out = ¬ x {by def. of NOT}
out = ¬(¬(i1 ∧ i2) {substitution, line 5 into 7}
out =(i1 ∧ i2) {simplify, ¬¬ t=t}
AND (i1,i2,out) {by def. of AND spec}
AND_IMPL (i1,i2,out) ⇒ AND_SPEC (i1,i2,out)
Q.E.D.
Inductive Proofs
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 Inductive Proofs Must Have:
 Base Case (value):
where you prove it is true about the base case
 Inductive Hypothesis (value):
where you state what will be assume in this proof
 Inductive Step (value)
show:
 where you state what will be proven below
proof:
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 where you prove what is stated in the show portion
 this proof must use the Inductive Hypothesis sometime during th
e proof
Example 2
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 Prove this statement:
 Base Case (n=1):
 Inductive Hypothesis (n=p):
 Inductive Step (n=p+1):
 Show:
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Example 3 N-Bit Adder
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 Verification of Generic Circuits
 used in datapath design and verification
 idea: verify n-bit circuit then specialize proof for specific value of
n, (i.e., once proven for n, a simple instantiation of the theorem fo
r any concrete value, e.g. 32, gets a proven theorem for that insta
nce).
 use of induction proof
 Specification
 N-ADDER_SPEC (n,in1,in2,cin,sum,cout):=
(in1 + in2 + cin = 2n+1 * cout + sum)
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Example 3 N-Bit Adder
 Implementation
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Example 3 N-Bit Adder
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 Recursive Definition:
N-ADDER_IMP(n,in1[0..n-1],in2[0..n-1],cin,sum[0..n-1],cout):=
∃ w. N-ADDER_IMP(n-1,in1[0..n-2],in2[0..n-2],cin,sum[0..n-2],w) ∧ N-AD
DER_IMP(1,in1[n-1],in2[n-1],w,sum[n-1],cout)
Notes:
 N-ADDER_IMP(1,in1[i],in2[i],cin,sum[i],cout) = ADDER_IMP(in1[i],in2
[i],cin,sum[i],cout)
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 Data abstraction function (vn: bitvec → nat) to relate bit vectors to
natural numbers:
 vn(x[0]):= bn(x[0])
 vn(x[0,n]):= 2n * bn(x[n]) + vn(x[0,n-1]
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Example 3 N-Bit Adder
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 Proof goal:
∀ n, in1, in2, cin, sum, cout. N-ADDER_IMP(n,in1[0..n-1],in2[0..n-1],ci
n,sum[0..n-1],cout) ⇒ N-ADDER_SPEC(n, vn(in1[0..n-1]), vn(in2[
0..n-1]), vn(cin), vn(sum[0..n-1]), vn(cout))
 As an example can be instantiated with n = 32:
∀ in1, in2, cin, sum, cout. N-ADDER_IMP(in1[0..31],in2[0..31],cin,sum[0.
.31],cout) ⇒ N-ADDER_SPEC(vn(in1[0..31]), vn(in2[0..31]), vn(cin)
, vn(sum[0..31]), vn(cout))
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Example 3 N-Bit Adder
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 Proof by induction over n:
 basis step:
N-ADDER_IMP(1,in1[0],in2[0],cin,sum[0],cout) ⇒ N-ADDER_SPEC(
1,vn(in1[0]),vn(in2[0]),vn(cin),vn(sum[0]),vn(cout))
 Induction Step:
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[N-ADDER_IMP(n,in1[0..n-1],in2[0..n-1],cin,sum[0..n-1],cout) ⇒ N-A
DDER_SPEC(n,vn(in1[0..n-1]),vn(in2[0..n-1]),vn(cin),vn(sum[0..n-1]),
vn(cout)) ] ⇒ [N-ADDER_IMP(n+1,in1[0..n],in2[0..n],cin,sum[0..n],co
ut) ⇒ N-ADDER_SPEC(n+1,vn(in1[0..n]),vn(in2[0..n]),vn(cin),vn(sum
[0..n]),vn(cout))]
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Conclusions
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 Advantages of Theorem Proving
 High abstraction and expressive notation
 Powerful logic and reasoning, e.g., induction
 Can exploit hierarchy and regularity, puts user in control
 Can be customized with tactics (programs that build larger proofs steps from ba
sic ones)
 Useful for specifying and verifying parameterized (generic) datapath-dominated
designs
 Unrestricted applications (at least theoretically)
 Limitations of Theorem Proving:
 Interactive (under user guidance): use many lemmas, large numbers of comma
nds
 Large human investment to prove small theorems
 Usable only by experts: difficult to prove large / hard theorems
 Requires deep understanding of the both the design and HOL (while-box verific
ation)
 must develop proficiency in proving by working on simple but similar problems.
We are not alone
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Theorem
proving
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Model che
cking
Testin
g
Hybrid Verification
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 Formal Verification using
 Theorem Proving + Model Checking
Theorem
Proving
Model
Checking
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Hybrid Verification
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|-Goal Imp. Spec.
|-Goal Imp.(x  y  ….)  Spec.((y= ..) (…..))
G1
G1’
G1’’ G2’
G2
G2’’ G3’
G3
…….
G3’’
Gn
Gn’
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Use model checking to verify Sub-Goals
Gn’’
Different Verification Methods
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 Testing (Simulation/Emulation)
 Theorem Proving
 Model checking (automatic verification)
Testing
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Theorem
Proving
Model
Checking
Semi-formal Verification
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Simulation
Driver
Simulation
Engine
Simulation
Monitor
Symbolic
Simulation
Guided vector
generation
Diagnosis of
Unverified
Portions
Conventional
Coverage
Analysis
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Devadas and Keutzer’s proposal:
A pragmatic suggestion for SOC verification
Extension
Semi-formal Verification
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 Smart simulation:
 Use properties to generate directed test vectors.
 Maximize chances of detecting bugs at small cost
 Coverage metrics crucial?
 Use metrics to determine
Unexercised parts of design: Guide vector generation
Adequacy of verification: When to stop?
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Did you find the BUG yet?
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 Verification and testing problem is an open question with multi-Billion
$ Research per year.
 A great Masters Research Topic
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A Final Proof
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



Software engineers want to be real engineers.
Real engineers use mathematics.
Formal methods are the mathematics of software engineering.
Therefore, software engineers should use formal methods.
Mike Holloway,
NASA
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Scientists Quotes
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“Teaching to unsuspecting youngsters the effective
use of formal methods is one of the joys of life be
cause it is so extremely rewarding”
“A formula is worth a thousand pictures” 
Edsger Wybe Dijkstra
(1930–2002)
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