Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
sav07_lecture_3_skeleton [2007/03/21 10:58] vkuncak |
sav07_lecture_3_skeleton [2007/03/21 14:27] (current) vkuncak |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| ====== Lecture 3 (Skeleton) ====== | ====== Lecture 3 (Skeleton) ====== | ||
| - | ===== Converting programs (with simple values) to formulas ===== | + | Summary of what we are doing in today's class: |
| + | {{vcg-big-picture.png}} | ||
| + | |||
| + | |||
| + | ===== Verification condition generation: converting programs into formulas ===== | ||
| ==== Context ==== | ==== Context ==== | ||
| Line 11: | Line 15: | ||
| * 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 | ||
| Line 24: | Line 28: | ||
| R( c ) -> error=false | R( c ) -> error=false | ||
| + | |||
| Line 37: | Line 42: | ||
| 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) | ||
| Line 102: | Line 107: | ||
| This idea is important in static analysis. | This idea is important in static analysis. | ||
| + | |||
| Line 127: | Line 133: | ||
| Like composition of a set with a relation. It's called ''relational image'' of set $P$ under relation $r$. | 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. | + | 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 ==== | ||
| Line 156: | Line 161: | ||
| - | ===== Proving quantifier-free linear arithmetic formulas ===== | + | |
| + | ===== One useful decision procedure: Proving quantifier-free linear arithmetic formulas ===== | ||
| Suppose that we obtain (one or more) verification conditions of the form | Suppose that we obtain (one or more) verification conditions of the form | ||
| Line 163: | Line 169: | ||
| \end{equation*} | \end{equation*} | ||
| - | whose validity we need to prove. We here assume that F contains only | + | whose validity we need to prove. We here assume that F contains only linear arithmetic. Note: we can check satisfiability of $F\ \land\ (\mbox{error}=\mbox{true})$. We show an algorithm to check this satisfiability. |
| - | + | ||
| - | Note: we can check satisfiability of $F\ \land\ (\mbox{error}=\mbox{true})$. | + | |
| ==== Quantifier Presburger arithmetic ==== | ==== Quantifier Presburger arithmetic ==== | ||
| Line 175: | Line 179: | ||
| T ::= var | T + T | K * T (terms) | T ::= var | T + T | K * T (terms) | ||
| A ::= T=T | T <= T (atomic formulas) | A ::= T=T | T <= T (atomic formulas) | ||
| - | F ::= F & F | F|F | ~F (formulas) | + | F ::= A | F & F | F|F | ~F (formulas) |
| To get full Presburger arithmetic, allow existential and universal quantifiers in formula as well. | To get full Presburger arithmetic, allow existential and universal quantifiers in formula as well. | ||
| Line 184: | Line 188: | ||
| Proof: small model theorem. | Proof: small model theorem. | ||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| ==== Small model theorem for Quantifier-Free Presburger Arithmetic (QFPA) ==== | ==== Small model theorem for Quantifier-Free Presburger Arithmetic (QFPA) ==== | ||
| Line 212: | Line 209: | ||
| * solution of Ax=b (A regular) has as components rationals of form p/q with bounded p,q | * solution of Ax=b (A regular) has as components rationals of form p/q with bounded p,q | ||
| * duality of linear programming | * duality of linear programming | ||
| - | * obtains bound $M = n(ma)^{2m+1}$, which needs $(2m+1)(\log n + \log m + \log a)$ bits | + | * 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. | * we could encode the problem into SAT: use circuits for addition, comparison etc. | ||
| Line 240: | Line 237: | ||
| * 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 | ||
| - | |||
| - | |||
| - | |||
| - | |||