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sav07_lecture_3_skeleton [2007/03/20 21:21]
vkuncak
sav07_lecture_3_skeleton [2007/03/21 12:28]
vkuncak
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 ===== Converting programs (with simple values) to formulas ===== ===== Converting programs (with simple values) to formulas =====
 +
  
  
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   * we can represent relations using set comprehensions;​ if our program c has two state components, we can represent its meaning R( c ) as $\{((x_0,​y_0),​(x,​y)) \mid F  \}$, where F is some formula that has x,y,x_0,y_0 as free variables.   * we can represent relations using set comprehensions;​ if our program c has two state components, we can represent its meaning R( c ) as $\{((x_0,​y_0),​(x,​y)) \mid F  \}$, where F is some formula that has x,y,x_0,y_0 as free variables.
  
-  * this is what I mean by ''​simple values''​later we will talk about modeling pointers and arrays, but we will still use this as a starting point.+  * simple values: ​variables are integers. ​ Later we will talk about modeling pointers and arrays, but what we say now applies
  
 Our goal is to find rules for computing R( c ) that are Our goal is to find rules for computing R( c ) that are
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   R( c ) -> error=false   R( c ) -> error=false
 +
  
  
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   R(havoc x) = frame(x)   R(havoc x) = frame(x)
-  R(assume F) = F[x:=x_0, y:=y_0, error:​=error_0]+  R(assume F) = F[x:=x_0, y:=y_0, error:​=error_0] ​& frame()
   R(assert F) = (F -> frame)   R(assert F) = (F -> frame)
  
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 This idea is important in static analysis. This idea is important in static analysis.
 +
 +
 +
 +
 +
  
  
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 ==== Symbolic execution ==== ==== Symbolic execution ====
  
-Symbolic execution converts programs into formulas by going forward. ​ It is therefore somewhat analogous to the way an [[interpreter]] for the language would work.  ​It is based on the notion of strongest ​postcondition.+Symbolic execution converts programs into formulas by going forward. ​ It is therefore somewhat analogous to the way an [[interpreter]] for the language would work.  ​ 
 + 
 +Avoid renaming all the time. 
 + 
 +  SE(F,k, c1; c2) = SE(F & R(c1), k+1, c2)             ​(update formula) 
 + 
 +  SE(F,k,(c1 [] c2); c2) = SE(F, k, c1) | SE(F,​k,​c2) ​  ​(explore both branches) 
 + 
 +Note: how many branches do we get? 
 + 
 +Strongest ​postcondition
 +\begin{equation*} 
 +  sp(P,r) = \{ s_2 \mid \exists s_1.\ s_1 \in P \land (s_1,s_2) \in r \} 
 +\end{equation*} 
 +Like composition of a set with a relation. ​ It's called ''​relational image''​ of set $P$ under relation $r$.
  
 +Note: when proving our verification condition, instead of proving that semantics of relation implies error=false,​ it's same as proving that the formula for set sp(U,r) implies error=false,​ where U is the universal relation, or, in terms of formulas, computing the strongest postcondition of formula '​true'​.
  
 ==== Weakest preconditions ==== ==== Weakest preconditions ====
  
 While symbolic execution computes formula by going forward along the program syntax tree, weakest precondition computes formula by going backward. While symbolic execution computes formula by going forward along the program syntax tree, weakest precondition computes formula by going backward.
 +
 +  wp(Q, x=t) =
 +  wp(Q, assume F) =
 +  wp(Q, assert F) =
 +  wp(Q, c1 [] c2) = 
 +  wp(Q, c1 ; c2) = 
 +
 +==== Inferring Loop Invariants ====
 +
 +Suppose we compute strongest postcondition in a program where we unroll loop k times.
 +  * What does it denote?  ​
 +  * What is its relationship to loop invariant?
 +
 +Weakening strategies
 +  * maintain a conjunction
 +  * drop conjuncts that do not remain true
 +
 +Alternative:​
 +  * decide that you will only loop for formulas of restricted form, as in abstract interpretation and data flow analysis (next week)
 +
 +
 +
  
 ===== Proving quantifier-free linear arithmetic formulas ===== ===== Proving quantifier-free linear arithmetic formulas =====
 +
 +Suppose that we obtain (one or more) verification conditions of the form
 +\begin{equation*}
 + F\ \rightarrow\ (\mbox{error}=\mbox{false})
 +\end{equation*}
 +
 +whose validity we need to prove. ​ We here assume that F contains only 
 +
 +Note: we can check satisfiability of $F\ \land\ (\mbox{error}=\mbox{true})$.
 +
 +==== Quantifier Presburger arithmetic ====
 +
 +Here is the grammar:
 +
 +  var = x | y | z | ...                    (variables)
 +  K = ... | -2 | -1 | 0 | 1 | 2 | ...      (integer constants)
 +  T ::= var | T + T | K * T                (terms)
 +  A ::= T=T | T <= T                       ​(atomic formulas)
 +  F ::= A  |  F & F |  F|F  |  ~F          (formulas)
 +
 +To get full Presburger arithmetic, allow existential and universal quantifiers in formula as well.
 +
 +Note: we can assume we have boolean variables (such as '​error'​) as well, because we can represent them as 0/1 integers.
 +
 +Satisfiability of quantifier-free Presburger arithmetic is decidable.
 +
 +Proof: small model theorem.
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +==== Small model theorem for Quantifier-Free Presburger Arithmetic (QFPA) ====
 +
 +First step: transform to disjunctive normal form.
 +
 +Next: reduce to integer linear programming:​
 +\begin{equation*}
 +  A\vec x = \vec b, \qquad \vec x \geq \vec 0
 +\end{equation*}
 +where $A \in {\cal Z}^{m,n}$ and $x \in {\cal Z}^n$.
 +
 +Then solve integer linear programming (ILP) problem
 +  * [[wk>​Integer Linear Programming]]
 +  * online book chapter on ILP
 +  * [[http://​www.gnu.org/​software/​glpk/​|GLPK]] tool
 +
 +We can prove small model theorem for ILP - gives bound on search.
 +
 +Short proof by {{papadimitriou81complexityintegerprogramming.pdf|Papadimitriou}}:​
 +  * solution of Ax=b (A regular) has as components rationals of form p/q with bounded p,q
 +  * duality of linear programming
 +  * obtains bound $M = n(ma)^{2m+1}$,​ which needs $\log n + (2m+1)\log(ma)$ bits
 +  * we could encode the problem into SAT: use circuits for addition, comparison etc.
 +
 +Note: if small model theorem applies to conjunctions,​ it also applies to arbitrary QFPA formulas.  ​
 +
 +Moreover, one can improve these bounds. ​ One tool based on these ideas is [[http://​www.cs.cmu.edu/​~uclid/​|UCLID]].
 +
 +Alternative:​ enumerate disjuncts of DNF on demand, each disjunct is a conjunction,​ then use ILP techniques (often first solve the underlying linear programming problem over reals). ​ Many SMT tools are based on this idea (along with Nelson-Oppen combination:​ next class).
 +  * [[http://​www.cs.nyu.edu/​acsys/​cvc3/​download.html|CVC3]] (successor of CVC Lite)
 +  * [[http://​combination.cs.uiowa.edu/​smtlib/​|SMT-LIB]] Standard for formulas, competition
 +
 +
 +==== Full Presburger arithmetic ====
 +
 +Full Presburger arithmetic is also decidable.
 +
 +Approaches:
 +  * Quantifier-Elimination (Omega tool from Maryland) - see homework
 +  * Automata Theoretic approaches: LASH, MONA (as a special case)
  
 ===== Papers ===== ===== Papers =====
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   * Presburger Arithmetic (PA) bounds: {{papadimitriou81complexityintegerprogramming.pdf}}   * Presburger Arithmetic (PA) bounds: {{papadimitriou81complexityintegerprogramming.pdf}}
   * Specializing PA bounds: http://​www.lmcs-online.org/​ojs/​viewarticle.php?​id=43&​layout=abstract   * Specializing PA bounds: http://​www.lmcs-online.org/​ojs/​viewarticle.php?​id=43&​layout=abstract
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