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Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Page 1: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Spring 2013Program Analysis and Verification

Lecture 1: Introduction

Roman ManevichBen-Gurion University

Page 2: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

December 31, 2008

30GB Zunes all over the world fail en masse

2

Page 3: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Zune bug 1 while (days > 365) { 2 if (IsLeapYear(year)) { 3 if (days > 366) { 4 days -= 366; 5 year += 1; 6 } 7 } else { 8 days -= 365; 9 year += 1; 10 } 11 }

3

Page 4: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Zune bug 1 while (366 > 365) { 2 if (IsLeapYear(2008)) { 3 if (366 > 366) { 4 days -= 366; 5 year += 1; 6 } 7 } else { 8 days -= 365; 9 year += 1; 10 } 11 }

Suggested solution: wait for tomorrow 4

Page 5: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

February 25, 1991

On the night of the 25th of February, 1991, a Patriot missile system operating in Dhahran, Saudi Arabia, failed to track and intercept an incoming Scud. The Iraqi missile impacted into an army barracks, killing 28 U.S. soldiers and injuring another 98.

Patriot missile failure

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Page 6: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Patriot bug – rounding error• Time measured in 1/10 seconds• Binary expansion of 1/10:

0.0001100110011001100110011001100....• 24-bit register

0.00011001100110011001100• error of

– 0.0000000000000000000000011001100... binary, or ~0.000000095 decimal

• After 100 hours of operation error is 0.000000095×100×3600×10=0.34

• A Scud travels at about 1,676 meters per second, and so travels more than half a kilometer in this time

Suggested solution: reboot every 10 hours6

Page 7: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

August 13, 2003

Billy Gates why do you make this possible ? Stop making moneyand fix your software!!

(W32.Blaster.Worm)

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Page 8: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Windows exploit(s)Buffer Overflow

void foo (char *x) { char buf[2]; strcpy(buf, x); } int main (int argc, char *argv[]) { foo(argv[1]); }

./a.out abracadabraSegmentation fault

Stack grows this way

Memory addresses

Previous frame

Return address

Saved FP

char* x

buf[2]

ab

ra

ca

da

br

Page 9: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Buffer overrun exploits int check_authentication(char *password) { int auth_flag = 0; char password_buffer[16];

strcpy(password_buffer, password); if(strcmp(password_buffer, "brillig") == 0) auth_flag = 1; if(strcmp(password_buffer, "outgrabe") == 0) auth_flag = 1; return auth_flag;}int main(int argc, char *argv[]) { if(check_authentication(argv[1])) { printf("\n-=-=-=-=-=-=-=-=-=-=-=-=-=-\n"); printf(" Access Granted.\n"); printf("-=-=-=-=-=-=-=-=-=-=-=-=-=-\n"); } else printf("\nAccess Denied.\n"); }

(source: “hacking – the art of exploitation, 2nd Ed”) 9

Page 10: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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(In)correct usage of APIs Application trend: Increasing number of libraries and APIs

– Non-trivial restrictions on permitted sequences of operations

Typestate: Temporal safety properties

– What sequence of operations are permitted on an object?

– Encoded as DFA

e.g. “Don’t use a Socket unless it is connected”

init connected closed

err

connect)( close)(

getInputStream)(getOutputStream)(

getInputStream)(getOutputStream)(getInputStream)(

getOutputStream)(

close)(

*

Page 11: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Challengesclass SocketHolder { Socket s; }

Socket makeSocket() { return new Socket(); // A }

open(Socket l) { l.connect(); }talk(Socket s) { s.getOutputStream()).write(“hello”); }

main() { Set<SocketHolder> set = new HashSet<SocketHolder>(); while(…) { SocketHolder h = new SocketHolder(); h.s = makeSocket(); set.add(h); } for (Iterator<SocketHolder> it = set.iterator(); …) { Socket g = it.next().s; open(g); talk(g); }}

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Page 12: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Testing is not enough

• Observe some program behaviors• What can you say about other behaviors?

• Concurrency makes things worse

• Smart testing is useful– requires the techniques that we will see in the

course

Page 13: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Static analysis definition

Reason statically (at compile time) about the possible runtime behaviors of a program

“The algorithmic discovery of properties of a program by inspection of its source text1”-- Manna, Pnueli

1 Does not have to literally be the source text, just means w/o running it

Page 14: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Is it at all doable?x = ?if (x > 0) { y = 42;} else { y = 73; foo();} assert (y == 42);

Bad news: problem is generally undecidable

Page 15: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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universe

Central idea: use approximation

Under Approximation

Exact set of configurations/behaviors

Over Approximation

Page 16: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Goal: exploring program states

initialstates

badstates

16

reachablestates

Page 17: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Technique: explore abstract states

initialstates

badstates

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reachablestates

Page 18: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Technique: explore abstract states

initialstates

badstates

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reachablestates

Page 19: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Technique: explore abstract states

initialstates

badstates

19

reachablestates

Page 20: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Technique: explore abstract states

initialstates

badstates

20

reachablestates

Page 21: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Sound: cover all reachable states

initialstates

badstates

reachablestates

Page 22: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Unsound: miss some reachable states

initialstates

badstates

reachablestates

Page 23: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Imprecise abstraction

initialstates

badstates

23

reachablestates

False alarms

Page 24: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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A sound message

x = ?if (x > 0) { y = 42;} else { y = 73; foo();} assert (y == 42); Assertion may be violated

Page 25: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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• Avoid useless result

• Low false alarm rate• Understand where precision is lost

Precision

UselessAnalysis(Program p) { printf(“assertion may be violated\n”);}

Page 26: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Runtime vs. static analysis

Runtime Static analysis

Effectiveness Can miss errorsFinds real errors

Can find rare errorsCan raise false alarms

Cost Proportional to program’s execution

Proportional to program’s complexity

No need to efficiently handle rare cases

Can handle limited classes of programs and still be useful

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Page 27: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Driver’s Source Code in C

PreciseAPI Usage Rules(SLIC)

Defects

100% pathcoverage

Rules

Static Driver Verifier

Environment model

Static Driver Verifier

Page 28: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Bill Gates’ Quote

"Things like even software verification, this has been the Holy Grail of computer science for many decades but now in some very key areas, for example, driver verification we’re building tools that can do actual proof about the software and how it works in order to guarantee the reliability." Bill Gates, April 18, 2002. Keynote address at WinHec 2002

Page 29: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

The Astrée Static Analyzer

Patrick CousotRadhia CousotJérôme Feret

Laurent MauborgneAntoine Miné Xavier Rival

ENS France

Page 30: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Objectives of Astrée

• Prove absence of errors in safety critical C code

• ASTRÉE was able to prove completely automatically the absence of any RTE in the primary flight control software of the Airbus A340 fly-by-wire system– a program of 132,000 lines of C analyzed

Page 31: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Objectives of Astrée

• Prove absence of errors in safety critical C code

• ASTRÉE was able to prove completely automatically the absence of any RTE in the primary flight control software of the Airbus A340 fly-by-wire system– a program of 132,000 lines of C analyzed

By Lasse Fuss (Own work) [CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

Page 32: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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A little about me• History

– Studied B.Sc., M.Sc., Ph.D. at Tel-Aviv University• Research in program analysis with IBM and Microsoft

– Post-doc in UCLA and in UT Austin– Joined Ben-Gurion University this year

• Example research challenges– What’s a good algorithm for automatically discovering (with no

hints) that a program generates a binary tree where all leaves are connected in a list?

– What’s a good algorithm for automatically proving that a parallel program behaves “well”?

– How can we automatically synthesize parallel code that is both correct and efficient?

Page 33: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Why study program analysis?

• Challenging and thought provoking– An approach for dealing with computationally hard

(usually undecidable) problems– Treat programs as mathematical objects

• Understand how to systematically– Design optimizations– Reason about correctness / find bugs (security)

• Some techniques may be applied in other domains– Computational learning– Analysis of biological systems

Page 34: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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What do you get in this course?

• Learn basic principles of static analysis– Understand jargon/papers

• Learn a few advanced techniques– Some principled way of developing analysis– Develop one in a small-scale project

• Put to practice what you learned in logic, automata, programming

Page 35: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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My role

• Teach you theory and practice• Teach you how to think of new techniques

• E-mail: [email protected]• Office hours: Wednesday 13:00-15:00• Course web-page– Announcements– Forum– …

Page 36: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Requirements1. Summarize one lecture: 10% of grade– Submit initial summary– Get corrections/suggestions– Submit revised summary

2. Theoretical assignments and programming assignments: 50%– About 8 (some very small)– Must submit all– Must solve all questions– Otherwise re-submit (and get a lower grade)

3. Final project: 40%– Implement a program analyzer for a given component

Page 37: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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How to succeed in this course• Attend all classes• Make sure you understand material in class– Engage by asking questions and raising ideas

• Be on top of assignments– Submit on time– Don’t get stuck or give up on exercises – get help – ask me– Don’t start working on assignments the day before

• Be ethical

Joe (a day before assignment deadline):“I don’t really understand what you want from me in this assignment, can you help me/extend the deadline”?

Page 38: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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The static analysis approach

• Formalize software behavior in a mathematical model (semantics)

• Prove properties of the mathematical model– Automatically, typically with approximation of the

formal semantics

• Develop theory and tools for program correctness and robustness

Page 39: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

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Kinds of static analysis• Spans a wide range

– type checking … up to full functional verification

• General safety specifications • Security properties (e.g., information flow)• Concurrency correctness conditions (e.g., absence of

data races, absence of deadlocks, atomicity)• Correct usage of libraries (e.g., typestate)

• Underapproximations useful for bug-finding, test-case generation,…

Page 40: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Static analysis techniques

• Abstract Interpretation• Dataflow analysis

• Constraint-based analysis• Type and effect systems

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Page 41: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Static analysis for verification

program

specification

Abstractcounterexample

Analyzer

Valid

41

Page 42: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Relation to program verification

• Fully automatic

• Applicable to a programming language

• Can be very imprecise• May yield false alarms

• Requires specification and loop invariants

• Program specific

• Relatively complete• Provides counter examples• Provides useful documentation• Can be mechanized using

theorem provers

Static Analysis Program Verification

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Page 43: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Verification challenge

main(int i) { int x=3,y=1;

do { y = y + 1; } while(--i > 0) assert 0 < x + y;}

Determine what states can arise during any execution

Challenge: set of states is unbounded

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Page 44: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Abstract Interpretation

main(int i) { int x=3,y=1;

do { y = y + 1; } while(--i > 0) assert 0 < x + y;}

Recipe1) Abstraction2) Transformers3) Exploration

Challenge: set of states is unbounded Solution: compute a bounded representation of (a superset) of program states

Determine what states can arise during any execution

44

Page 45: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

1) Abstraction

main(int i) { int x=3,y=1;

do { y = y + 1; } while(--i > 0) assert 0 < x + y;}

• concrete state

• abstract state (sign)

: Var Z

#: Var{+, 0, -, ?}

x y i

3 1 7 x y i

+ + +

3 2 6

x y i

…45

Page 46: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

2) Transformers

main(int i) { int x=3,y=1;

do { y = y + 1; } while(--i > 0) assert 0 < x + y;}

• concrete transformer

• abstract transformer

x y i

+ + 0

x y i

3 1 0y = y + 1

x y i

3 2 0

x y i

+ + 0

y = y + 1

+ - 0 + ? 0

+ 0 0 + + 0

+ ? 0 + ? 0

46

Page 47: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

3) Exploration

+ + ? + + ?

x y i

main(int i) { int x=3,y=1;

do { y = y + 1; } while(--i > 0) assert 0 < x + y;}

+ + ?

+ + ?

? ? ?

x y i

+ + ?

+ + ?

+ + ?

+ + ?

+ + ?

+ + ?

47

Page 48: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Incompleteness

main(int i) { int x=3,y=1;

do { y = y - 2; y = y + 3; } while(--i > 0) assert 0 < x + y;}

+ ? ?

+ ? ?

x y i

+ ? ?

+ + ?

? ? ?

x y i

+ ? ?

+ ? ?

+ ? ?

48

Page 49: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Parity abstraction

challenge: how to find “the right” abstraction

while (x !=1 ) do { if (x % 2) == 0 { x := x / 2; } else { x := x * 3 + 1; assert (x %2 ==0); }}

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Page 50: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

How to find “the right” abstraction?

• Pick an abstract domain suited for your property– Numerical domains– Domains for reasoning about the heap– …

• Combination of abstract domains

• Another approach – Abstraction refinement

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Page 51: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Following the recipe (in a nutshell)

1) Abstraction

Concrete state Abstract statex

tn n n

x

t

n

2) Transformers

n

x

t

n

t n

x n

t->n = x

51

Page 52: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Example: shape (heap) analysis

t

x

n

x

t n

x

t n n

xt n n

xtt

x

ntt

ntx

tx

t

xemp

void stack-init(int i) {

Node* x = null;

do {

Node t =

malloc(…)

t->n = x;

x = t;

} while(--i>0)

Top = x;

} assert(acyclic(Top))

t

x

n n

x

t n n

x

t n n n

xt n n n

xt n n n

top52

Page 53: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

x

t n n

t

x

n

x

t n

x

t n n

xtt

x

ntt

ntx

tx

t

xemp

xt n

n

xt n

n

n

x

t

n

t n

x n

xt n

n

3) Exploration

x

t n

Top n

ntx Top

tx Top x

t n

Top n

void stack-init(int i) {

Node* x = null;

do {

Node t =

malloc(…)

t->n = x;

x = t;

} while(--i>0)

Top = x;

}

assert(acyclic(Top))

53

Page 54: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Example: polyhedra (numerical) domain

proc MC(n:int) returns (r:int) var t1:int, t2:int; begin if (n>100) then r = n-10; else t1 = n + 11; t2 = MC(t1); r = MC(t2); endif; end

var a:int, b:int; begin b = MC(a); end

What is the result of this program?54

Page 55: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

McCarthy 91 function

proc MC (n : int) returns (r : int) var t1 : int, t2 : int;begin /* (L6 C5) top */ if n > 100 then /* (L7 C17) [|n-101>=0|] */ r = n - 10; /* (L8 C14) [|-n+r+10=0; n-101>=0|] */ else /* (L9 C6) [|-n+100>=0|] */ t1 = n + 11; /* (L10 C17) [|-n+t1-11=0; -n+100>=0|] */ t2 = MC(t1); /* (L11 C17) [|-n+t1-11=0; -n+100>=0; -n+t2-1>=0; t2-91>=0|] */ r = MC(t2); /* (L12 C16) [|-n+t1-11=0; -n+100>=0; -n+t2-1>=0; t2-91>=0; r-t2+10>=0; r-91>=0|] */ endif; /* (L13 C8) [|-n+r+10>=0; r-91>=0|] */end

var a : int, b : int;begin /* (L18 C5) top */ b = MC(a); /* (L19 C12) [|-a+b+10>=0; b-91>=0|] */end

if (n>=101) then n-10 else 91

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Page 56: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Some things that should trouble you

• Does a result always exist?• Does the recipe always converge?• How “optimal” is the result?• How do I pick my abstraction?• How do come up with abstract transformers?• Other practical issues– Efficiency– How does it do in practice?

56

Page 57: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Change the abstraction to match the program

Abstraction refinement

program

specificationAbstractcounterexample

abstraction AbstractionRefinement counter

example

Verify

Valid

57

Page 58: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Recap: program analysis

• Reason statically (at compile time) about the possible runtime behaviors of a program

• use sound overapproximation of program behavior

• abstract interpretation– abstract domain – transformers – exploration (fixed-point computation)

• finding the right abstraction?

58

Page 59: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

Next lecture:semantics of programming languages

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Page 60: Program Analysis and Verification Spring 2013 Program Analysis and Verification Lecture 1: Introduction Roman Manevich Ben-Gurion University

References• Patriot bug:

– http://www.cs.usyd.edu.au/~alum/patriot_bug.html– Patrick Cousot’s NYU lecture notes

• Zune bug:–

http://www.crunchgear.com/2008/12/31/zune-bug-explained-in-detail/

• Blaster worm:– http://www.sans.org/security-resources/malwarefaq/w32_bl

asterworm.php• Interesting CACM article

– http://cacm.acm.org/magazines/2010/2/69354-a-few-billion-lines-of-code-later/fulltext

• Interesting blog post– http://www.altdevblogaday.com/2011/12/24/static-code-ana

lysis/

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