CS265: Program Analysis, Testing,
and Debugging
Lecture 1
Koushik Sen
EECS, UC Berkeley
My Background
• Assistant Professor in CS since Fall 2006
– Office: Parlab (581 Soda Hall)
– Email: ksen@cs
• Ph.D. and M.S. in Computer Science
– University of Illinois at Urbana Champaign (2001-2006)
• Spent 1 year in software industry as a software developer
• B-Tech
– IIT Kanpur (1995-1999)
• Research Interests:
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Software Engineering,
Programming Languages, and
Formal Methods
Verification, Testing, Model Checking, Program Analysis
Please introduce yourselves
Logistics
Course Goals
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To help students start research in the area of
program analysis, model checking, testing, and
debugging of sequential, concurrent, and
distributed systems.
To help students to apply the techniques
learned in this course in their ongoing research
in other areas such as operating systems,
computer networks, security, and database
systems.
Should meet your Th+Sys prelims requirement
Course Communication
• All class materials will be on the website
– http://fa09.pbwiki.com/
• See website for announcements
• Class meets on Monday and Wednesday, 1:00
PM – 2:30 PM at 320 Soda Hall
• Office hour by appointment
Course Structure
• We will study papers
– You will read a paper and write a review of the
paper before each class
– There will be some guest lectures
• You need to do 2-3 programming assignments
– Test generation
– Dynamic analysis of concurrent programs
– Points-to analysis
• Project in teams of 1-2.
Course Grading
• Reviews and class participation: 30%
• 2-3 homework assignments: 30%
• Project: 40%
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A project must involve new research
Some sample projects will be posted online
Choose a project topic by 25th September, 2009
1-2 page project proposal due by 9th October, 2009
A 5-7 minute mid-semester project presentation
Final project demo or presentation
6 page project report in ACM SIGPLAN conference format
About the course
• This course is about software reliability
• Why is software reliability important?
• As society becomes more dependent on
software, the consequences of software failures
are non-trivial.
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Money lost.
Lives lost.
Market share lost.
Clients lost.
Jobs lost.
Software Bug => Space Disaster
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Ariane 5 Space mission
$7, 000, 000, 000
10 Years in the making
40 seconds after take off
the rocket exploded
Software Bug => Space Disaster
Attempt to cram a 64-floating point
number to a 16-bit integer failed
AT&T long distance service failed for 9 hours
• On January 15, 1990, one of
AT&T's #4ESS toll switching
systems in New York City
experienced an intermittent
failure that caused a major
service outage.
• Wrong BREAK statement in
C-Code
• Complete coverage could
have revealed this bug
during testing
/* ``C'' Fragment to Illustrate AT&T Defect */
do {
switch expression {
...
case (value):
if (logical) {
…
break;
}
else
{
…
}
}
case (value2):
…
…
} while (expression)
Software Bugs: Cause of Deaths
• Several deaths of cancer patients were due to
overdoses of radiation resulting from a race
condition between concurrent tasks in the
Therac-25 software.
180 Degree Bug
Torpedoes, that deviate more than 90
degree, explode to avoid self destruction
of the ship.
Once upon a time a ship fired a torpedo
but the torpedo was jammed in the tube.
Then the captain gave the command: Let's
turn around and return to the harbour!
What happened next is no mystery.
Cost of Failure
• Software failures were estimated to cost the
US economy about $60 billion annually [NIST
2002]
– Improvements in software testing infrastructure
may save one-third of this cost
• Testing accounts for an estimated 50%-80%
of the cost of software development [Beizer
1990]
Methods for Building Reliable Software
Safe Programming
Languages and Type
systems
Static Program
Analysis
Model
Checking
and
Theorem
Proving
Model Based Software
Development and Analysis
Testing
Dynamic Program
Analysis
Methods for Building Reliable Software
Safe Programming
Languages and Type
systems
Static Program
Analysis
Model
Checking
and
Theorem
Proving
Model Based Software
Development and Analysis
Testing
Dynamic Program
Analysis
Course Contents
• Automated test generation
• Software model checking and various
theoretical results that form the foundation of
software model checking
• Concurrent program analysis
• Abstract interpretation and points-to analysis
Course Contents
• Automated test generation
• Software model checking and various
theoretical results that form the foundation of
software model checking
• Concurrent program analysis
• Abstract interpretation and points-to analysis
Automated Test Generation
• Korat:
– Reading assignment: Korat: Automated Testing
Based on Java Predicates. Chandrasekhar
Boyapati, Sarfraz Khurshid, Darko Marinov (ISSTA
2002)
– Must submit review to the course wiki by 9/8
11:59 PM
• Concolic Testing: Homework 1
Automated Test Generation
• Generate test inputs
– To reveal bugs: assertion violation, crashes, wrong
output
– Improve software reliability
• Often cannot prove program correct
– Need to check program for all possible inputs
– Input domain is often infinite
– Pick inputs to satisfy certain coverage criteria
• Generate all legal inputs of bounded size
• Generate all legal inputs for full branch coverage
Course Contents
• Automated test generation
• Software model checking and various
theoretical results that form the foundation of
software model checking
• Concurrent program analysis
• Abstract interpretation and points-to analysis
Software Model Checking
• Attempt to prove programs correct
– Abstract domain
– Create an abstraction of the program
– Show that the abstraction does not contain a “bad
state”
• Predicate abstraction and boolean programs
• Successfully used for model checking device
drivers
Software Model Checking
• BLAST: predicate abstraction
• Decidability results for various boolean program
models
– Forms theoretical foundation of various software
model checking algorithms
– Pushdown systems
– Pushdown systems with multiset
– Petri-Nets
– Multi-pushdown systems with bounded context
switch
Course Contents
• Automated test generation
• Software model checking and various
theoretical results that form the foundation of
software model checking
• Concurrent program analysis
• Abstract interpretation and points-to analysis
Concurrent Program Analysis
• Bugs due to concurrency are notorious
– Intermittent and hard to reproduce
– Common causes: data race, atomicity violations,
deadlocks, and other synchronization issues
– Much more difficult to analyze than sequential
programs
– Need to check program
• all schedules
• all inputs
– Pushdown system with stacks => undecidable
Concurrent Program Analysis
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Classic dynamic race detection algorithms
Atomicity checking and deadlock analysis
Explicit State Model Checking
Partial Order Reduction
Iterative preemption bounded model checking
Homework 2
Course Contents
• Automated test generation
• Software model checking and various
theoretical results that form the foundation of
software model checking
• Concurrent program analysis
• Abstract interpretation and points-to analysis
Abstract Interpretation and Points-to
Analysis
• Abstract interpretation
– Note software model checking is an instantiation of
abstract interpretation
– forms basis of most static program analyses
• Mayur Naik on Points-to analysis
– The most important analysis
– Anderson’s points-to analysis
– BDDs (Binary Decision Diagrams) for scalable points-to
analysis
– And a homework
Course Contents
• Automated test generation
• Software model checking and various
theoretical results that form the foundation of
software model checking
• Concurrent program analysis
• Abstract interpretation and points-to analysis
Summary
• 3 homework assignments will give you handson experience of program analysis
– In my opinion, this is quite useful to get a deep
understanding of the subject, and
– to jump start research in program analysis
Symbolic Execution
• Use symbolic values for input variables
• Execute the program symbolically on symbolic
input values
• Collect symbolic path constraints
• Use constraint solver to generate test inputs
for each execution path
Computation Tree: Execution Paths of a Program
• Can be seen as a binary tree with
possibly infinite depth
• Each node represents the
execution of a “if then else”
statement
• Each edge represents the
execution of a sequence of nonconditional statements
• Each path in the tree represents
an equivalence class of inputs
1
0
– Computation tree
0
1
1
1
0
0
1
1
0
1
Example of Computation Tree
void test_me(int x, int y) {
if(2*x==y){
if(x != y+10){
printf(“I am fine here”);
} else {
printf(“I should not reach here”);
ERROR;
}
}
}
N
2*x==y
N
ERROR
Y
x!=y+10
Y
Concrete Execution
int x = read();
int y = read();
int t;
t = x;
x = x + y;
if (x > y) {
y = 2*t;
if (x+1 == y) {
assert(false);
}
}
Concrete Execution
int x = read();
int y = read();
int t;
x=4
y=9
t = x;
x = x + y;
if (x > y) {
y = 2*t;
if (x+1 == y) {
assert(false);
}
}
t=4
x = 13
(13 > 9)
y=8
(14 == 8)
END
Symbolic Execution
(true | x = x0 )
int x = read();
int y = read();
int t;
t = x;
x = x + y;
if (x > y) {
y = 2 * t;
if (x+1 == y) {
assert(false);
}
}
(true | x = x0,y=y0)
(true | x = x0,y=y0,t=x0)
(true | x = x0+y0,y=y0,t=x0)
x0+y0 > y0
(x0>0 | x = x0+y0,y=2x0,t=x0)
(x0 <= 0 | x = x0+y0,y=y0,t=x0)
x0+y0 + 1 == 2x0
(x0 > 0 && y0 + 1  x0
| x = x0+y0,y=2x0,t=x0)
(x0 > 0 && y0 + 1 == x0
| x = x0+y0,y=2x0,t=x0)
Test Generation
(true | x = x0 )
int x = read();
int y = read();
int t;
t = x;
x = x + y;
if (x > y) {
y = 2 * t;
if (x+1 == y) {
assert(false);
}
}
Solve: x0 > 0 &&
y0+1  x0
Solution: x = 1, y=9
(true | x = x0,y=y0)
Solve: x0<= 0
Solution: x = 0,
y=0
Solve: x0 > 0 &&
y0+1 = x0
Solution: x = 2, y=1
(true | x = x0,y=y0,t=x0)
(true | x = x0+y0,y=y0,t=x0)
x0+y0 > y0
(x0 <= 0 | x = x0+y0,y=y0,t=x0)
(x0 > 0 | x = x0+y0,y=2x0,t=x0)
x0+y0 + 1 == 2x0
(x0 > 0 && y0 + 1  x0
| x = x0+y0,y=2x0,t=x0)
(x0 > 0 && y0 + 1 == x0
| x = x0+y0,y=2x0,t=x0)
Abstract Interpretation: Test to Proof
Example ( ) {
int new = read(); lock = 0;
1: do{
lock = 1;
old = new;
b = read();
2: if (b){
3:
lock=0;
new ++;
}
4:} while(new != old);
5: assert (lock==1);
}
Abstract Interpretation: Test to Proof
Example ( ) {
int new = read(); lock = 0;
1: do{
lock = 1;
old = new;
b = read();
2: if (b){
3:
lock=0;
new ++;
}
4:} while(new != old);
5: assert (lock==1);
}
Predicates:
LOCK, new==old
Abstract Interpretation: Test to Proof
Example ( ) {
int new = read(); lock = 0;
1: do{
lock = 1;
old = new;
b = read();
2: if (b){
3:
lock=0;
new ++;
}
4:} while(new != old);
5: assert (lock==1);
}
Predicates:
LOCK, new==old
1
LOCK , new==old
: LOCK
2
LOCK , new==old
3
: LOCK , : new = old
4
4
1
5
LOCK , new=old
: LOCK,
: new == old
Reachability Tree