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## PowerPoint Slideshow about 'Automatic Predicate Abstraction of C-Programs' - dugan

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Presentation Transcript

Overview (1)

Motivation: Software systems typically infinite state

- model checking finite state check an abstraction of a software system

Automatic predicate abstraction:

- 1st proposed by Graf & Saidi
- concrete states mapped to abstract states under a finite set of predicates
- designed and implemented for
- finite state systems
- infinite state systems specified as guarded commands
- not implemented for a programming language such as C

The C2BP tool:

- performs automatic predicate abstraction of C programs
- given (P, E) BP(P, E) boolean program(P: C program, E: finite set of predicates)

Overview (2)

Boolean program BP(P, E): a C program with bool as single type

- plus some additional constructs
- same control structure as P
- contains only |E| boolean variables, one for each predicate in E
- e.g. (x<y) E {x<y} is a boolean variable for BP(P, E).then: {x<y} is true at program point P (x<y) is true at P
- transfer function: automatically for each statement s in P
- conservatively represents the effect of s on predicates in E

BEBOP model checker:

- used to analyze the boolean program
- performs interprocedural dataflow analysis using binary dicision diagrams (BDDs)

Results from applying C2BP

Pointer manipulating programs: identify invariants involving pointers

- more precise alias information than with a flow sensitive alias analysis
- structural properties of the heap preserved by list manipulating code

Examples on proof-carrying code: to identify loop invariants

SLAM toolkit: to check safety properties of windows NT device drivers

- C2BP & BEBOP to statically determine whether or not an assertion violation can take place in C-code
- demand-driven abstraction-refinement to automatically find new predicates for a particular assertion
- convergence (undeniability) was not a problem on all Windows NT drivers checked

Challenges of predicate abstraction in C (1)

Pointers: two related subproblems treated in a uniform way

- assignments through dereferenced pointers in original C-program
- pointers & pointer-dereferences in the predicates for the abstraction

Procedures: allow procedural abstraction in boolean programs. They also have:

- global variables
- procedures with local variables
- call-by-value parameter passing
- procedural abstraction – signatures constructed in isolation

Procedure calls: abstraction process is challenging in the presence of pointers

- after a call the caller must conservatively update local state modified by procedure
- sound and precise approach that takes side-effects into account

Make both abstraction and analysis more efficient by exploiting procedural abstraction. recursive proc. e.g. inlining

Challenges of predicate abstraction in C (2)

Unknown values: it is not always possible to determine the effect of a statement in the C-program in terms of the input predicate set E

- such nondeterminism () handled in BP with * (non-determenistic choice) which allows to implicitly express 3-valued domain for boolean variables

Precision-efficiency tradeoff: computing abstract transfer function for a statement s in the C-program with respect to the set E of predicates may require the use of a theorem prover

- O(2^|E|) calls to the theorem prover
- apply optimization techniques to reduce this number

Predicate abstraction overview

PA Problem: given (P, E) where

- P is a C-program
- E = {φ1, …, φn} is a set of pure boolean C-expressions over variables and constants of the C-language

Compute BP(P, E) which is a boolean program that

- has some control structure as P
- contains only boolean variables V = {b1, …, bn} where bi = {φi} represents predicate φi
- guaranteed to be an abstraction of P (superset of traces modulo …)

Assumption over a C-program:

- all interprocedural control flow is by if and goto
- all expressions are free of side-effects & short-circuit evaluation
- all expressions do not contain multiple pointer dereferences (e.g. **P)
- function calls occur at topmost level of expressions

Weakest precondition and cube (monoids)

Weakest precondition WP(s, φ): {ψ} s {φ}

- the weakest predicate whose truth before s entails truth of φ after s terminates (if it terminates)
- assignment: WP(x=e, φ) = φ[e/x] (no side-effects)
- Example: WP(x=x+1, x<5) = (x<5)[x+1/x] = x+1 < 5 = x<4
- central to predicate abstraction:p: s and φi Ep’: WP(s, φi) = true bj = {WP(s, φi)}C-code BP(P, E) code
- However, no such bj may exist if WP(s, φ) E
- Example:E = {(x<5), (x=2)}WP(x=x+1, x<5) = x <4 E strengthen the predicate by using DP x=2 x<4 use x=2 instead

p: if (bj) then bi = truep‘:

Strengthening and weakening

Cube over V: a conjunction ci1 … cikwhere ci1 {bij, bij} for bij V

Concretization function ε: ε(bi) = φi, ε(bi) = φi

- extend ε over disjunction of cubes in natural way

Predicate Fv(φ): largest disjunction of cubes c over V so that ε(c) φ

- Fv(φ) = { Vci | ci cubes_over(V) ε(ci) φ}

Strengthening of φ: ε(Fv(φ))

- weakest predicate over ε(V) that implies φ
- Example:ε(Fv(x<4)) = (x=2)

Weakening of φ: ε(Gv(φ)) where Gv(φ) = Fv(φ)

- ε(Gv(φ)) is the strongest predicate over ε(V) implied by φ

Theorem prover: for each cube, check implication decision procedure

- Simplify & Vampyre: equational (Nelson-Oppen) style provers

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