Николай Бьернер «Program Analysis and Testing using Efficient Satisfiability Modulo Theories Solvers»

Program Analysis and Testing
using Satisfiability Modulo Theories

Yandex
2 October 2012, Moscow

Nikolaj Bjørner
Senior Researcher
Microsoft Research
1

Agenda

Context: Software Engineering Research @ Microsoft

Propaganda: Software Engineering Research Tools

Application: Fuzzing and Test Case Generation

Application: Program Verification & Bit precise Analysis

Application: String analysis - Formal Language Theory for Security

Technology: Z3 – An Efficient SMT Solver - Basics and Research

2

Takeaways
Context: Awareness about Microsoft Research

Propaganda: Cool software engineering research projects

Applications: Logic is the Calculus of Computation
Programs analysis tools use logic at their core

Technology: Z3 – An Efficient SMT Solver.
Modern SAT/SMT solver search in one slide and the
dichotomies of modern constraint search engines.

I rather address questions during talk and tune the highlighted
material according to interest (there are 3x too many slides )
3

Context

Team An Efficient SMT Solver
Leonardo de Moura, Nikolaj Bjørner, Christoph Wintersteiger
4

Context

Research in Software Engineering
Group Improve Software Development Productivity 5

Context

Organization Microsoft Research 6

Context

Microsoft Research Labs

Research :1%

Sales,
Support,
R&D
Marketing
~40000
~50000

Company 7

Propaganda

Core Expertise

Empirical Foundations:
Software Engineering Logic

Program Analysis:
Programming Languages
Performance, Reliability,
Design & Implementation
Security

8

Propaganda

Core Expertise


Program Analysis:
Security

9

Propaganda

Core Expertise


Program Analysis:
Security

10

Propaganda

Core Expertise


Program Analysis:
Security

11

Propaganda

Core Expertise

Empirical Software Engineering:

Analytics: what code is prone to bugs
(what code should I be testing)

for VS 2012 Team Foundation Server

12

Propaganda

.com

13

http://rise4fun.com/z3py

14

Propaganda

Academic Interns

15

Application

Fuzzing and Test Case Generation

SAGE

Internal. For Security Fuzzing External. For Developers

Runs on x86 instructions Runs on .NET code

Try it on: http://pex4fun.com

Finding security bugs before the hackers

black hat 16

Application: Fuzzing and Testing

Fuzzing and Test Case Generation
Dr. Strangelove?

Bug: ***433
SAGE “2/29/2012 3:41 PM Edited by *****
SubStatus -> Local Fix

I think the fuzzers are starting to become sentient.
We must crush them before it is too late.
Internal. For Security Fuzzing External. For Developers
In this case, the fuzzer figured out that if
[X was between A and B then Y would get
Runs on x86 instructionsto Z triggeringRuns onto happen……]
set U and V
.NET code
…..
And if this fuzzer asks on:the nuclear launch
Try it for http://pex4fun.com
codes, don’t tell it what they are …”

Finding security bugs before the hackers

black hat 17

SAGE by numbers
100s CPU-years - largest dedicated fuzz lab in the world

100s apps - fuzzed using SAGE

100s previously unknown bugs found

Billion+ computers updated with bug fixes

Millions of $ saved for Users and Microsoft

10s of related tools (incl. Pex), 100s DART citations

3+ Billion constraints - largest usage for any SMT solver
18
Adapted from [Patrice Godefroid, ISSTA 2010]

Application

Test case generation
unsigned GCD(x, y) {
requires(y > 0);
while (true) {
unsigned m = x % y;
if (m == 0) return y;
x = y;
y = m;
}
}

19

Application

requires(y > 0); (y0 > 0) and
while (true) { (m0 = x0 % y0) and
unsigned m = x % y;
SSA
not (m0 = 0) and
if (m == 0) return y; (x1 = y0) and
x = y; (y1 = m0) and
y = m; (m1 = x1 % y1) and
} (m1 = 0)
} We want a trace where the loop is
executed twice.
20

Application

requires(y > 0); (y0 > 0) and x0 = 2
while (true) { (m0 = x0 % y0) and y0 = 4
unsigned m = x % y;
SSA
not (m0 = 0) and Solver
m0 = 2
if (m == 0) return y; (x1 = y0) and x1 = 4
x = y; (y1 = m0) and y1 = 2
y = m; (m1 = x1 % y1) and m1 = 0
} (m1 = 0)
} We want a trace where the loop is
executed twice.
21


Test Case Generation Procedure

Run Test and Monitor Execution Path Condition
Path
Test
seed Known
Inputs
Paths
New input
Constraint
System
Solve Unexplored path

22

Application: Scalable bit-precise analysis

What is wrong here?
-INT_MIN=
INT_MIN
(INT_MAX+1)/2 +
(INT_MAX+1)/2
int binary_search(int[] arr, int low, void itoa(int n, char* s) {
= INT_MIN
int high, int key) if (n < 0) {
while (low <= high) *s++ = ‘-’;
{ n = -n;
// Find middle value
}
int mid = (low + high) / 2;
// Add digits to s
int val = arr[mid];
if (val == key) return mid; ….
if (val < key) low = mid+1;
else high = mid-1;
}
return -1;
} Book: Kernighan and Ritchie
Package: java.util.Arrays
Function: itoa (integer to ascii)
Function: binary_search

Application: Scalable bit-precise analysis

Bit-precise analysis

1 0 1 0 1 1
1 0 1 0 1 1  0 1 1 0 0 1
= 
0 1 1 0 0 1
1 0 1 0 1 1 0 1 1 0 0 1
=
0 0 1 0 0 1
Vector Bit-wise
Segments Concatenation operations Bit-wise and
1 0 1 0 1 1

+
1 0 1 0 1 1 0 1 0 0 1 1 0 0 1
[4:2] =
=
0 0 0 1 0 0
Vector Modular
Segments Extraction arithmetic Addition

Application: Verification
Hypervisor Verification (2007 – 2010) with

Partners:
• European Microsoft Innovation Center
Hypervisor
• Microsoft Research
Hardware
• Microsoft’s Windows Division
• Universität des Saarlandes

co-funded by the
German Ministry of Education and Research

http://www.verisoftxt.de
25


Microsoft Verifying C Compiler

26

SAT/SMT progress driven by applications:
VCC Performance Trends Nov 08 – Mar 09
1000
Modification in invariant
checking

Switch to Z3 v2

100

Z3 v2 update

10

1

Attempt to improve
Switch to Boogie2 Boogie/Z3 interaction

0.1


Verification Attempt Time vs.
Satisfaction and Productivity

By Michal Moskal (VCC Designer and Software Verification Expert),
Language quiz: “loose” or “lose” ?


The Importance of Speed


Building Verve 9 person-months

Source file Kernel.cs
Verification tool
Compilation tool C# compiler

Verified
Nucleus.bpl (x86) Kernel.obj (x86)

Boogie/Z3 TAL checker

Translator/
Linker/ISO generator
Assembler

Safe to the Last Instruction / Jean Yang & Chris Verve.iso
Hawbliztl PLDI 2010 31

Application: String Analysis
Why string analysis?
(motivating scenario)
Tomcat
v. < 6.0.18

req = http://www.x.com/%c0%ae%c0%ae/%c0%ae%c0%ae/private/

1) security check: req
must not contain
Analysis question: "../"
Does utf8decode 2) dir =
reject overlong utf8decode("%c0%ae
utf8-encodings such %c0%ae/%c0%ae%c
as "%C0%AE" for '.'? 0%ae/private/")
= "../../private/"

access granted to "../../private/"

Windows 2000 vulnerability: http://www.sans.org/security-resources/malwarefaq/wnt-unicode.php
Apache Tomcat vulnerability: http://web.nvd.nist.gov/view/vuln/detail?vulnId=CVE-2008-2938


Relativized Formal Language Theory

Classical Word Transducers
(e.g. decoding automata,
rational transductions)
Classical I/O Automata
(e.g. Mealy machine)

Classical
Word Acceptors
(NFA, DFA)


Relativized Formal Language Theory
string transformation
Symbolic Word Transducers

Classical Word Transducers modulo Th()

Classical Word Transducers
(e.g. decoding automata,
rational transductions)
Classical I/O Automata
(e.g. Mealy machine)

Symbolic Word Acceptors
Classical

Classical WordAcceptors modulo Th()
Word Acceptors
(NFA, DFA)

regex matching

Rex & Bek – Symbolic RegEx & String Analysis
Application:

Transducers

Margus Veanes


Symbolic Finite Transducer (SFT)
• Classical transducer modulo a rich label theory
• Core Idea: represent labels with guarded
transformation functions
– Separation of concerns: finite graph / theory of labels

Concrete transitions: Symbolic transition:

p 1920 guard
p
transitions
 x. 8016 ≤ x ≤ 7FF16/
‘x80’/ … ‘x7FF’/ [C016|x10,6, 8016|x5,0]
“xC2x80” “xDFxBF”

q q bitvector
operations

Technology

SMT: Satisfiability Modulo Theories

𝑥 2 + 𝑦 2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 0.1

Technology

Solution/Model
1 7
𝑥2 + 𝑦2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 0.1 sat, 𝑥 = , 𝑦=
8 8

Technology

Solution/Model
1 7
𝑥2 + 𝑦2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 0.1 sat, 𝑥 = , 𝑦=
8 8
𝑥 2 + 𝑦 2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 1

Technology

Solution/Model
1 7
𝑥2 + 𝑦2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 0.1 sat, 𝑥 = , 𝑦=
8 8
𝑥 2 + 𝑦 2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 1 unsat, Proof

Technology

Solution/Model
1 7
𝑥2 + 𝑦2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 0.1 sat, 𝑥 = , 𝑦=
8 8

Is execution path P feasible? Is assertion X violated?

SAGE

Is Formula F Satisfiable (over Theory of Reals)? 41

Technology

Solution/Model
1 7
𝑥2 + 𝑦2 < 1 𝑎𝑛𝑑 𝑥𝑦 > 0.1 sat, 𝑥 = , 𝑦=
8 8

Is execution path P feasible? Is assertion X violated?
W
I
SAGE T
N
E
S
S

Is Formula F Satisfiable (over Theory of Reals)? 42

Technology


𝑥 + 2 = 𝑦 ⇒ 𝑓 𝑠𝑒𝑙𝑒𝑐𝑡 𝑠𝑡𝑜𝑟𝑒 𝑎, 𝑥, 3 , 𝑦 − 2 = 𝑓(𝑦 − 𝑥 + 1)

43

Technology



Arithmetic

44

Technology



Array Theory Arithmetic

𝑠𝑒𝑙𝑒𝑐𝑡(𝑠𝑡𝑜𝑟𝑒 𝑎, 𝑖, 𝑣 , 𝑖) = 𝑣
𝑖 ≠ 𝑗 ⇒ 𝑠𝑒𝑙𝑒𝑐𝑡(𝑠𝑡𝑜𝑟𝑒 𝑎, 𝑖, 𝑣 , 𝑗) = 𝑠𝑒𝑙𝑒𝑐𝑡(𝑎, 𝑗)

45

Technology



Uninterpreted
Array Theory Arithmetic
Functions

𝑠𝑒𝑙𝑒𝑐𝑡(𝑠𝑡𝑜𝑟𝑒 𝑎, 𝑖, 𝑣 , 𝑖) = 𝑣
𝑖 ≠ 𝑗 ⇒ 𝑠𝑒𝑙𝑒𝑐𝑡(𝑠𝑡𝑜𝑟𝑒 𝑎, 𝑖, 𝑣 , 𝑗) = 𝑠𝑒𝑙𝑒𝑐𝑡(𝑎, 𝑗)

46

Technology

Job Shop Scheduling

Machines

Tasks

Jobs
1
P = NP? Laundry 𝜁 𝑠 =0⇒ 𝑠= + 𝑖𝑟
2

Technology

Job Shop Scheduling
Constraints:
Precedence: between two tasks of the same
job
3 1
2
4

Resource: Machines execute at most one job
at a time

𝑠𝑡𝑎𝑟𝑡2,2 . . 𝑒𝑛𝑑2,2 ∩ 𝑠𝑡𝑎𝑟𝑡4,2 . . 𝑒𝑛𝑑4,2 = ∅

Technology

Job Shop Scheduling
Constraints: Encoding:
Precedence: 𝑡2,3 - start time of
job 2 on mach 3
3 1
2 𝑑2,3 - duration of
4
job 2 on mach 3
𝑡2,3 + 𝑑2,3 ≤ 𝑡2,4
Resource:
Not convex

𝑡2,2 + 𝑑2,2 ≤ 𝑡4,2
∨
𝑠𝑡𝑎𝑟𝑡2,2 . . 𝑒𝑛𝑑2,2 ∩ 𝑠𝑡𝑎𝑟𝑡4,2 . . 𝑒𝑛𝑑4,2 = ∅ 𝑡4,2 + d4,2 ≤ 𝑡2,2

Technology

Job Shop Scheduling

Technology

Job Shop Scheduling

Efficient solvers:
- Floyd-Warshal algorithm
case split - Ford-Fulkerson algorithm

case split

𝑧 − 𝑧 = 5 – 2 – 3 – 2 = −2 < 0

Technology

Microsoft Tools using
Z3 is used by many research groups
More than 19k downloads
Z3 places 1st in most categories in SMT competitions

SAGE HAVOC

Vigilante

52

Technology


SAGE HAVOC

Z3 solved more than 3 billion Vigilante
constraints created by SAGE
Checking Win8 and Office.

53

Technology


SAGE HAVOC

Z3 ships in
constraints created by SAGE Windows Server with the
Checking Win8 and Office. Static Driver Verifier
54

Technology


SAGE HAVOC used to check
Z3
Azure Firewall Policies

Z3 ships in
constraints created by SAGE Windows Server with the
Checking Win8 and Office. Static Driver Verifier
55

Technology

Research Areas

Algorithms Decidable Fragments

Heuristics

56
Logic is “The Calculus of Computer Science” Zohar Manna

Technology

Research Areas
Undecidable (FOL + LIA)

Semi Decidable (FOL)
Algorithms NEXPTIME (EPR)
PSPACE (QBF)
NP (SAT)

Heuristics

57

Technology

Research Areas

Essentially Uninterpreted Formulas
NEXPTIME (EPR)
Quantified Bit-Vector Logic
PSPACE (QBF)
NP (SAT)
Generalized array theory

Heuristics

58

Technology

Researchstructure that can be exploited.
Areas
Practical problems often have


Essentially Uninterpreted Formulas
NEXPTIME (EPR)
Quantified Bit-Vector Logic
PSPACE (QBF)
NP (SAT)
Generalized array theory

Heuristics

59

Technology

Little Engines of Proof

Freely available from http://research.microsoft.com/projects/z3
60

Technology
Research around Z3
Decision Procedures
Modular Difference Logic is Hard TR 08 B, Blass Gurevich, Muthuvathi.
Linear Functional Fixed-points. CAV 09 B. & Hendrix.
A Priori Reductions to Zero for Strategy-Independent Gröbner Bases SYNASC 09 M& Passmore.
Efficient, Generalized Array Decision Procedures FMCAD 09 M & B
Quantifier Elimination as an Abstract Decision Procedure IJCAR 10, B
Cutting to the Chase CADE 11, Jojanovich, M
Polynomials IJCAR 12, Jojanovich, M

Combining Decision Procedures
Model-based Theory Combination SMT 07 M & B. .
Proofs, Refutations and Z3 IWIL 08 M & B
On Locally Minimal Nullstellensatz Proofs. SMT 09 M & Passmore.
A Concurrent Portfolio Approach to SMT Solving CAV 09 Wintersteiger, Hamadi & M
Conflict Directed Theory Resolution Cambridge Univ. Press 12, M & B

Quantifiers, quantifiers, quantifiers
Efficient E-matching for SMT Solvers. CADE 07 M & B.
Relevancy Propagation. TR 07 M & B.
.Deciding Effectively Propositional Logic using DPLL and substitution sets IJCAR 08 M & B.
.Engineering DPLL(T) + saturation. IJCAR 08 M & B.
Complete instantiation for quantified SMT formulas CAV 09 Ge & M.
.On deciding satisfiability by DPLL(+ T) and unsound theorem proving. CADE 09 Bonachina, M & Lynch.
Generalized PDR SAT 12 Hoder & B..

Introductory Background Reading

September 2011

Technology

Mile High: Modern SAT/SMT search

Backjump

literal assignments
Models
Conflict Clauses
Proofs

Propagate

Technology
Core Engine in Z3:
Modern DPLL/CDCL
Initialize 𝜖| 𝐹 𝐹 𝑖𝑠 𝑎 𝑠𝑒𝑡 𝑜𝑓 𝑐𝑙𝑎𝑢𝑠𝑒𝑠

Decide 𝑀 𝐹 ⟹ 𝑀, ℓ 𝐹 ℓ 𝑖𝑠 𝑢𝑛𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑

Propagate 𝑀 𝐹, 𝐶 ∨ ℓ ⟹ 𝑀, ℓ 𝐶∨ℓ 𝐹, 𝐶 ∨ ℓ 𝐶 𝑖𝑠 𝑓𝑎𝑙𝑠𝑒 𝑢𝑛𝑑𝑒𝑟 𝑀

Sat 𝑀 |𝐹 ⟹ 𝑀 𝐹 𝑡𝑟𝑢𝑒 𝑢𝑛𝑑𝑒𝑟 𝑀

Conflict 𝑀 𝐹, 𝐶 ⟹ 𝑀 𝐹, 𝐶 | 𝐶 𝐶 𝑖𝑠 𝑓𝑎𝑙𝑠𝑒 𝑢𝑛𝑑𝑒𝑟 𝑀

Learn 𝑀 𝐹| 𝐶⟹ 𝑀 𝐹, 𝐶 | 𝐶

Unsat 𝑀 𝐹 ∅ ⟹ 𝑈𝑛𝑠𝑎𝑡

Backjump 𝑀𝑀′ 𝐹 | 𝐶 ∨ ℓ ⟹ 𝑀ℓ 𝐶∨ℓ 𝐹 𝐶 ⊆ 𝑀, ¬ℓ ∈ 𝑀′

Resolve 𝑀 𝐹 | 𝐶′ ∨ ¬ℓ ⟹ 𝑀 𝐹 | 𝐶′ ∨ 𝐶 ℓ 𝐶∨ℓ ∈ 𝑀

Forget 𝑀 𝐹, 𝐶 ⟹ 𝑀 𝐹 𝐶 is a learned clause

Restart 𝑀 𝐹⟹ 𝜖 𝐹 [Nieuwenhuis, Oliveras, Tinelli J.ACM 06] customized

Technology
Core Engine in Z3:
Modern DPLL/CDCL
Initialize 𝜖| 𝐹 One 𝐹 𝑖𝑠 𝑎 expert𝑐𝑙𝑎𝑢𝑠𝑒𝑠
SAT 𝑠𝑒𝑡 𝑜𝑓 to another:
“It took me a year to
Decide 𝑀 𝐹 ⟹ 𝑀, ℓ 𝐹 ℓ 𝑖𝑠 𝑢𝑛𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑
understand the Mini-SAT
FUIP code”
Propagate 𝑀 𝐹, 𝐶 ∨ ℓ ⟹ 𝑀, ℓ 𝐶∨ℓ 𝐹, 𝐶 ∨ ℓ 𝐶 𝑖𝑠 𝑓𝑎𝑙𝑠𝑒 𝑢𝑛𝑑𝑒𝑟 𝑀
Mate Soos to
Sat 𝑀 |𝐹 ⟹ 𝑀 Niklas 𝑡𝑟𝑢𝑒 𝑢𝑛𝑑𝑒𝑟 𝑀
𝐹 Sörenson
over ice-cream
Conflict 𝑀 𝐹, 𝐶 ⟹ 𝑀 𝐹, 𝐶 | 𝐶 𝐶 𝑖𝑠 𝑓𝑎𝑙𝑠𝑒 𝑢𝑛𝑑𝑒𝑟 𝑀
at SAT 2012 in Trento
Learn 𝑀 𝐹| 𝐶⟹ 𝑀 𝐹, 𝐶 | 𝐶

Unsat 𝑀 𝐹 ∅ ⟹ 𝑈𝑛𝑠𝑎𝑡

Backjump 𝑀𝑀′ 𝐹 | 𝐶 ∨ ℓ ⟹ 𝑀ℓ 𝐶∨ℓ 𝐹 𝐶 ⊆ 𝑀, ¬ℓ ∈ 𝑀′

Resolve 𝑀 𝐹 | 𝐶′ ∨ ¬ℓ ⟹ 𝑀 𝐹 | 𝐶′ ∨ 𝐶 ℓ 𝐶∨ℓ ∈ 𝑀

Forget 𝑀 𝐹, 𝐶 ⟹ 𝑀 𝐹 𝐶 is a learned clause

Restart 𝑀 𝐹⟹ 𝜖 𝐹 [Nieuwenhuis, Oliveras, Tinelli J.ACM 06] customized

Technology

Mile High: Modern SMT procedures
Efficiently
Backtrack A way to

Backjump
certify
to equi-
satisfiability
satisfiable

values to satisfy
state

Models
formula
Learn new
fact that
prune as
Conflict Lemmas
many dead
branches as Efficient
Proofs
possible indexing for
propagating

Propagate
consequences

A way to certify
unsatisfiability

Technology

Research: Solving Horn Clauses

mc(x) = x-10 if x > 100
mc(x) = mc(mc(x+11)) if x  100
assert (x ≤ 101  mc(x) = 91)

∀𝑿. 𝑿 > 𝟏𝟎𝟎  mc(𝑿, 𝑿 − 𝟏𝟎)
∀𝑿, 𝒀, 𝑹. 𝑿 ≤ 𝟏𝟎𝟎  mc(𝑿 + 𝟏𝟏, 𝒀)  mc(𝒀, 𝑹)  mc(𝑿, 𝑹)
∀𝑿, 𝑹. mc(𝑿, 𝑹) ∧ 𝑿 ≤ 𝟏𝟎𝟏 → 𝑹 = 𝟗𝟏
Solver finds solution for mc Krystof Hoder & Nikolaj Bjorner, SAT67
2012
Bjorner, McMillan, Rybalchenko, SMT 2012

Technology

Research: SolvingR Efficiently
A key idea: Use partial solution to guide the search

Feasible Region 𝑥 3 + 2𝑥 2 + 3𝑦 2 − 5 < 0

Starting search
−4𝑥𝑦 − 4𝑥 + 𝑦 > 1
Partial solution:
𝑥 = 0.5
What is the core?

𝑥2 + 𝑦2 < 1 Can we extend it to 𝑦?

68
Dejan Jojanovich & Leonardo de Moura, IJCAR 2012

Takeaways
Context: Awareness about Microsoft Research

Propaganda: Cool software engineering research
projects.

Applications: Logic is the Calculus of Computation.
Programs analysis tools use logic at their core.

Technology: Z3 – An Efficient SMT Solver.
Modern SAT/SMT solver search in one slide
dichotomies of modern constraint search engines.
69

Summary
An outline of – an efficient SMT solver
Efficient logic solver for SE tools tackling intractable problems
http://research.microsoft.com/projects/z3

Software Engineering Research @ Microsoft
http://rise4fun.com

Academic internships
http://research.microsoft.com/en-us/jobs/intern

Contact
http://research.microsoft.com/~nbjorner
70
nbjorner@microsoft.com

Николай Бьернер «Program Analysis and Testing using Efficient Satisfiability Modulo Theories Solvers»

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Николай Бьернер «Program Analysis and Testing using Efficient Satisfiability Modulo Theories Solvers»