Software-Based Approaches to Software...

Skidmore College

Software-Based Approaches to Software

Protection

Ginger Myles

[email protected]

University of Arizona

Department of Computer Science

and

IBM Almaden Research Center

February 9, 2006 – p. 1

Skidmore College

Introduction

In this talk we will discuss...

The three major threats against IP in software

Software-based solutions

February 9, 2006 – p. 2

Skidmore College

3 Major Threats

Malicious Reverse Engineering: the extracting of a piece of aprogram in order to reuse it in ones own.

Software Tampering: the illegal modification of a program tocircumvent licence checks, to obtain access to digital mediaprotected by the software, etc.

Software Piracy: the illegal reselling of legally obtained copiesof a program.

February 9, 2006 – p. 3

Skidmore College

Malicious Reverse Engineering

Buy onecopy

Reusemodule

Sell

N Y

X

P

O

M

QM

Alice and Bob are competing software developers.

Bob reverse engineers Alice’s program and includes parts of it in

his own code.

Easier with Java bytecode, .NET, . . .

⇒ Alice obfuscates her code.

February 9, 2006 – p. 4

Skidmore College

What is Code Obfuscation?

T1 T2 T3P P′

A semantics-preserving transformation which

makes the program harder to understand

preserves original functionality

Idea is to obscure readability and understandability to such a

degree that it is less costly for the attacker to simply recreate

the program or purchase a legal copy.

February 9, 2006 – p. 5

Skidmore College

Code Obfuscation

T1 T2 T3P P′

We want to develop an algorithm such that

Maximize obscurity

Maximize resilience

Minimize cost

February 9, 2006 – p. 6

Skidmore College

Code Obfuscation

Layout obfuscation: alter the information that is unnecessaryto the execution of the application such as identifier namesand source code formatting.

Data obfuscation: alter the data structures used by theprogram.

Control-flow obfuscation: disguise the true control flow of theapplication.

February 9, 2006 – p. 7

Skidmore College

Layout Obfuscation

Name obfuscation: rename the identifiers in the program tomeaningless names.

� �

c l a s s C{

vo id f oo ( ) { . . . }

vo id bar ( i n t i ) { . . . }

vo id f o oba r ( i n t i ) { . . . }

S t r i n g t o S t r i n g ( ) { . . . }

}� �

⇒

� �

c l a s s C{

vo id a ( ) { . . . }

vo id b ( i n t i ) { . . . }

vo id c ( i n t i ) { . . . }

S t r i n g t o S t r i n g ( ) { . . . }

}� �

February 9, 2006 – p. 8

Skidmore College

Layout Obfuscation

Name obfuscation: rename the identifiers in the program tomeaningless names.

� �

c l a s s C{

vo id f oo ( ) { . . . }

vo id bar ( i n t i ) { . . . }

vo id f o oba r ( i n t i ) { . . . }

S t r i n g t o S t r i n g ( ) { . . . }

}� �

⇒

� �

c l a s s C{

vo id a ( ) { . . . }

vo id a ( i n t i ) { . . . }

vo id b ( i n t i ) { . . . }

S t r i n g t o S t r i n g ( ) { . . . }

}� �

February 9, 2006 – p. 9

Skidmore College

Data Obfuscation

Promote primitive types: change all primitives into instances of their

respective wrapper classes.

� �pub l i c c l a s s C {

s t a t i c i n t gcd ( i n t x , i n t y ) {

i n t t ;

wh i l e ( t rue ) {

boolean b = x % y == 0;

i f ( b ) r e t u r n y ;

t = x % y ;

x = y ;

y = t ;

}

}

pub l i c s t a t i c vo id main ( S t r i n g [ ] a){

System . out . p r i n t ( ”Answer : ” ) ;

System . out . p r i n t l n ( gcd ( 1 0 0 , 1 0 ) ) ;

}

}

� �

⇒


s t a t i c I n t e g e r gcd ( Integer x , Integer y){

Integer t = n u l l ;

wh i l e ( t rue ){

Boolean b = new Boolean ( x.intValue() %

y.intValue() == 0);

i f ( b.booleanValue() )

r e t u r n y ;

t = new Integer ( x.intValue() %

y.intValue() ) ;

x = y ;

y = t ;

}

}


System . out . p r i n t l n ( ”Answer : ” ) ;

System . out . p r i n t l n ( gcd ( new Integer(100) ,

new Integer(10) ) ;

}

}

� �

February 9, 2006 – p. 10

Skidmore College

Data Obfuscation

Boolean splitter: every boolean variable is split into two variables and the

state of the original variable is reflected in the combined state of the two

variables.



i n t t ;




t = x % y ; x = y ; y = t ;

}

}




}

}

� �

⇒



i n t t8, t7 , t ;

f o r ( ; ; ) {

i f ( x%y==0) { t8=1;t7=0; }

e l s e { t8=0;t7=0; }

i f ( (t7ˆt8)!=0 )

r e t u r n y ;

e l s e {

t=x%y ; x=y ; y=t ;

}

}}

pub l i c s t a t i c vo id main ( S t r i n g [ ] a ) {


System . out . p r i n t l n ( gcd ( 1 0 0 , 1 0 ) ) ; }

}

� �

February 9, 2006 – p. 11

Skidmore College

Control-flow Obfuscation

Can be based on normal transformations an optimizingcompiler would perform

method inlining

loop unrolling

February 9, 2006 – p. 12

Skidmore College

Opaque Predicates

Opaque Predicate: A predicate P is opaque at a programpoint p, if at point p the outcome of P is known atembedding time. If P always evaluates to True we write P T

p ,

for False we write P Fp , and if P sometimes evaluates to

True and sometimes to False we write P ?p .

Inserted to make it difficult for an adversary to analyze thecontrol-flow of the application.

February 9, 2006 – p. 13

Skidmore College

Control-flow Obfuscation

Bogus branch: use an opaque predicate to insert a bogus if statement.



i n t t ;




t = x % y ; x = y ; y = t ;

}

}




}

}

� �

⇒



i n t t ;

i n t x = 2 ;




t = x % y ; x = y ; y = t ;

if(x*x < 0) return 0;

}

}




}

}

� �

February 9, 2006 – p. 15

Skidmore College

Code Obfuscation Example

Name obfuscation, boolean splitter, bogus branch, promote primitives



i n t t ;




t = x % y ; x = y ; y = t ;

}

}




}

}

� �

⇒


s t a t i c I n t e g e r a ( I n t e g e r x , I n t e g e r y ) {

I n t e g e r t8 , t7 , t ;

I n t e g e r b = new I n t e g e r ( 2 ) ;

f o r ( ; ; ) {

i f ( x . i n tVa l u e ( ) % y . i n tVa l u e () == 0){

t8 = new I n t e g e r ( 1 ) ; t7 = new I n t e g e r ( 0 ) ;

}e l s e {

t8 = new I n t e g e r ( 0 ) ; t7 = new I n t e g e r ( 0 ) ;

i f ( ( t7 . i n tVa l u e ( )ˆ t8 . i n tVa l u e ( ) ) != 0){

r e t u r n y ;

}e l s e {

t = new I n t e g e r ( x . i n tVa l u e () %

y . i n tVa l u e ( ) ) ;

x = y ; y = t ;

}

i f ( b . i n tVa l u e ( ) ∗ b . i n tVa l u e () < 0)

r e t u r n 0 ;

}

}



System . out . p r i n t l n ( a (new I n t e g e r (100) ,

new I n t e g e r ( 1 0 ) ) ) ;

}

}

� �February 9, 2006 – p. 16

Skidmore College

Software Tampering

Receivetrialsoftware

Removeexpiration check

if(date > Feb 9, 2006) {

display("trial expired");

exit();

}

C C′

Alice is software developer.

Alice gives Bob a trial version of her software.

Bob locates the expiration check and disables it. He now hasunlimited use of the software.

⇒ Alice tamperproofs her program.

February 9, 2006 – p. 17

Skidmore College

What is Software Tamperproofing?

W W

Alice Bob

T

P P′ P′′P′′′

K

Code obfuscation is used to hide a secret.

Tamperproofing is used to protect the secret from alteration .

Tamperproofing performs two duties

1. Detect that the software has been altered.

2. Once detection has occurred, cause the program to fail orrepair itself.

February 9, 2006 – p. 18

Skidmore College

Software Tamperproofing

W W

Alice Bob

T

P P′ P′′P′′′

K

We want to design an algorithm such that

software failure is stealthy

does not alert the attacker to the location of the failureinducing code.

February 9, 2006 – p. 19

Skidmore College


W W

Alice Bob

T

P P′ P′′P′′′

K

We want to design an algorithm such that

software failure is stealthy

does not alert the attacker to the location of the failureinducing code.

� �

i f ( tamper ing )

abo r t ( ) ;� �

February 9, 2006 – p. 20

Skidmore College


Tamper detection techniques

Inspect the code

Inspect the state

Generate code

February 9, 2006 – p. 21

Skidmore College


Tamper detection techniques

Inspect the code

Inspect the state

Generate code

No matter which technique, the expected result should not berevealed to the attacker.

� �

i f ( cu r r en tDa t e >= Feb 9 , 2 006 )

e x i t ( ) ;� �

February 9, 2006 – p. 22

Skidmore College

Software Tamperproofing — Aucsmith

Upper

Lower

Decrypt&JumpCode

EncryptedCode block #2

Decrypt&JumpCode

Code block #3Encrypted

Decrypt&JumpCode


Code block #1Plaintext

Decrypt&JumpCode U

pper

Lower

Decrypt&JumpCode


Decrypt&JumpCode

Code block #1Encrypted

Decrypt&JumpCode


Decrypt&JumpCode

Code block #3Plaintext

⊗⊗

Tamper-proofObfuscate/

⊗

⊗

P

Key is the integrity verification kernels.

Each IVK contains 2n blocks of code.

Half are in upper memory, half in lower memory.

XOR each block in upper memory with a block in lower memory.

Result one block is decrypted and execution resumes at that block.

February 9, 2006 – p. 23

Skidmore College

Software Piracy

Resell

Make illegalcopies

Buy onecopy P

P

P

Alice is a software developer.

Bob buys one copy of Alice’s application and sells copies to third

parties.

⇒ Alice watermarks/fingerprints her program.

February 9, 2006 – p. 24

Skidmore College

What is Software Watermarking?

A technique used to aid in the prevention of software piracy.

Embed a unique identifier in a program.

Watermarking

Same identifier

Copyright notice

Discourages theft

Fingerprinting

Different identifier

Customer identification

Trace illegal copies

Discourages but does not prevent illegal copying and redistribution.

February 9, 2006 – p. 25

Skidmore College

Software Watermarking

A watermarking system consists of two functions:

embed(P, w, key) → P ′

recognize(P ′, key) → w

Watermarked Program

OriginalProgram

EmbedWatermark

ExtractWatermark

WW KK

P P′

February 9, 2006 – p. 26

Skidmore College

Software Watermarking

EmbedWatermark

AttackWatermark

OriginalProgram

Watermarked Program

ExtractWatermark

Attacked Program

Watermark

WW WP′

K

P P′

K

We want to develop an algorithm such that when we embedthe watermark W in the program P

W is resilient to various attacks.

W is stealthy.

W is large (high bit-rate).

The overhead (space and time) is low.

February 9, 2006 – p. 27

Skidmore College

Attacks on Software Watermarks

Subtractive Attack: The adversary examines the(disassembled/de-compiled) program in an attempt to discoverthe watermark and to remove all or part of it from the code.

W

Alice

KP′′

Bob

P′PK

February 9, 2006 – p. 28

Skidmore College


Additive Attack: The adversary adds a new watermark inorder to make it hard for the IP owner to prove that herwatermark is actually the original.

W W1

W1

WW

AdditiveAttack

P′P

K

Alice Bob

K1

KP′′

February 9, 2006 – p. 29

Skidmore College


Distortive Attack: A series of semantics-preservingtransformations are applied to the software in an attempt torender the watermark useless.

W W’ W’

DistortiveAttack

Bob

K

Alice

PK

P′ P′′

February 9, 2006 – p. 30

Skidmore College


Collusive Attack: The adversary compares two copies of thesoftware which contain different fingerprints in order toidentify the location.

F1

F2

CollusiveAttackP1

Alice Bob

PPK1

K2

P2

February 9, 2006 – p. 31

Skidmore College

Naive Watermarking Techniques

Constant String

� �

S t r i n g watermark = ‘ ‘ Copy r i gh t 2 0 0 6 . . . ’ ’ ;� �

� �

S t r i n g watermark = ‘ ‘CC Number 1 2 3 4 . . . ’ ’ ;� �

Easy to attack, unstealthy, high bit-rate, little overhead.

February 9, 2006 – p. 32

Skidmore College

Naive Watermarking Techniques

Switch Encoding

� �

switch ( E ) {

case 1 : { · · ·}

case 5 : { · · ·}

case 9 : { · · ·}

}� �

⇒

� �

switch ( E ) {

case 5 : { · · ·}

case 1 : { · · ·}

case 9 : { · · ·}

}� �

Easy to attack, stealthy , low bit-rate , no overhead.

February 9, 2006 – p. 33

Skidmore College

Watermarking Transformations

Naive approaches:

Renaming

L: X:EmbedP P′

Reordering

Embed

P P′

February 9, 2006 – p. 34

Skidmore College

Watermarking Transformations

Advanced approaches:

Alter program statistics

Embed

Extend program semantics

Embed

February 9, 2006 – p. 35

Skidmore College

Monden et al.

Semantics extending transformation.

Embeds the watermark in a dummy method that is added tothe application.

The embedding is accomplished through a speciallyconstructed sequence of instructions.

Since the inserted method is never executed there is flexibilityin how the instructions are constructed.

Can disguise the method by adding a call to the methodwhich is regulated by an opaque predicate.

COMPSAC 2000.

February 9, 2006 – p. 36

Skidmore College

Monden et al.

Encode 8 bits of the watermark by replacing the operand of every

BIPUSH instruction.

� �void whileI( ){

int i = 0;

while( i < 100 ){

i++;

}

}� �

⇒

� �0 i c o n s t 0 // push i n t con s t an t 0

1 i s t o r e 1 // s t o r e i n t o l o c a l v a r i a b l e 1

2 goto 8 // f i r s t t ime no inc r ement

5 i i n c 1 1 // add 1 to l o c a l v a r i a b l e 1

8 i l o a d 1 // l oad from l o c a l v a r i a b l e 1

9 b ipu sh 100 // push a sma l l i n t ( 100 )

11 i f i c m p l t 5 // compare , i f t r u e goto 5

14 r e t u rn // r e t u r n vo i d when done� �

February 9, 2006 – p. 37

Skidmore College

Monden et al.

Encode 3 bits of the watermark by replacing each arithmetic instruction.

iadd 000

iand 001

ior 010

ixor 011

irem 100

idiv 101

imul 110

isub 111

� �i n t f a c t ( i n t x){

i n t f a c t o r i a l = 1 ;

f o r ( i n t i =1; i <= x ; i++){

f a c t o r i a l ∗= i ;

}

r e t u r n f a c t o r i a l ;

}

� �

⇒

� �0 i c o n s t 1

1 i s t o r e 1

2 i c o n s t 1

3 i s t o r e 2

4 goto 14

7 i l o a d 1

8 i l o a d 2

9 imu l

10 i s t o r e 1

11 i i n c 2 1

14 i l o a d 2

15 i l o a d 0

16 i f i c m p l e 7

19 i l o a d 1

20 i r e t u r n

� �

February 9, 2006 – p. 38

Skidmore College

Arboit

Semantics extending transformation.

k branching points throughout the application are randomlyselected.

At each branching point either ∧P T , ∨¬P T , or ∨P F isappended to the predicate at that location.

The bits of the watermark are embedded through the opaquepredicate that has been chosen.

Within the opaque predicate the bits can be encoded eitheras constants or by assigning a rank to each of the opaquepredicates.

ICECR-5, 2002.

February 9, 2006 – p. 39

Skidmore College

Arboit Algorithm 1 Example

� �c l a s s C{

vo id m1( i n t a , i n t b){

. . .

i f ( a <= b ){ . . .}

e l s e { . . .}

. . .

}

}� �

W⇒

� �c l a s s C{

vo id m1( i n t a , i n t b){

. . .

i n t c=1;

i f ( ( a <= b) && ( c∗c >= 0)){ . . .}

e l s e { . . .}

. . .

}

}� �

February 9, 2006 – p. 40

Skidmore College

Qu and Potkonjak

5 2

4 3

1

5 2

4 3

1

5 2

4

1

3

Original Marked New coloring

⇒ ⇒

Renaming transformation.

Embed the mark by adding constraints (extra edges) to the register interference

graph.

Easy to attack by random register re-numbering.

3rd International Information Hiding Workshop, 1999.

February 9, 2006 – p. 41

Skidmore College

Interference Graph

Models the relationship between the variables in the procedure.

Each variable in the procedure is represented by a vertex.

If two variables have overlapping live ranges then the verticesare joined by an edge.

The graph is colored so that we can assign the variables toregisters so that we minimize the number of registers requiredand variables that are live at the same time do not share aregister.

February 9, 2006 – p. 42

Skidmore College

Interference Graph Example

� �

v1 := 2 * 2

v2 := 2 * 3

v3 := 2 * v2

v4 := v1 + v2

v5 := 3 * v3� �

1

2

3 4

5

February 9, 2006 – p. 43

Skidmore College


� �

v1 := 2 * 2

v2 := 2 * 3

v3 := 2 * v2

v4 := v1 + v2

v5 := 3 * v3� �

1

2

3 4

5

February 9, 2006 – p. 44

Skidmore College


� �

v1 := 2 * 2

v2 := 2 * 3

v3 := 2 * v2

v4 := v1 + v2

v5 := 3 * v3� � 4

5

1

2

3

February 9, 2006 – p. 45

Skidmore College

Qu and Potkonjak

(e) Watermarked Bytecode

50 : iconst_1

53 : iload_254 : iconst_155 : isub56 : istore_257 : goto −> 12

62 : ireturn

21 : iconst_1

42 : baload43 : if_icmpeq −> 5046 : iconst_047 : goto −> 51

34 : ifne −> 60

38 : iload_237 : aload_0

39 : baload40 : aload_141 : iload_2

22 : ifne −> 33

30 : goto −> 3433 : iconst_1

13 : iconst_014 : if_icmplt −> 2117 : iconst_018 : goto −> 22

METHOD: fast_memcmp:([B[BI)Z0 : iconst_01 : istore 33 : iconst_04 : istore_35 : iconst_1

8 : iload_29 : iconst_110 : isub11 : istore_212 : iload_2

6 : istore 4

25 : iload 4

60 : iload 4

51 : istore 4

27 : invokestatic

v1

v2

v3

v4v5

v6

v7

(b) Original Interference Graph

v1

v2

v3

v4v5

v6

v7

(c) Watermarked Interference Graph

WatermarkEmbed

v7 2v6 4v5 3v4 3v3 2v2 1v1 0

variable register number(d) Register Assignment Table

(a) Original Bytecode

50 : iconst_151 : istore 353 : iload_254 : iconst_155 : isub56 : istore_257 : goto −> 1260 : iload 362 : ireturn

21 : iconst_1

42 : baload43 : if_icmpeq −> 5046 : iconst_047 : goto −> 51

34 : ifne −> 60

38 : iload_237 : aload_0

39 : baload40 : aload_141 : iload_2

22 : ifne −> 3325 : iload 3

30 : goto −> 3433 : iconst_1

13 : iconst_014 : if_icmplt −> 2117 : iconst_018 : goto −> 22

METHOD: fast_memcmp:([B[BI)Z0 : iconst_01 : istore 33 : iconst_04 : istore_35 : iconst_16 : istore 38 : iload_29 : iconst_110 : isub11 : istore_212 : iload_2

27 : invokestatic

February 9, 2006 – p. 46

Skidmore College

What is Software Birthmarking?

birthmark

B

B

BAppliesObfuscation

Copies toSell

Sells

P

P′1

P′

P′2

A technique used to address the illegal distribution of all orsome part of a program.

Extract identifying characteristics from two programs to showthat one is a copy of the other.

February 9, 2006 – p. 47

Skidmore College

How Does Birthmarking Differ From Watermarking?

It is often necessary to alter existing code or add code to theapplication in order to embed the watermark.

Birthmarks cannot prove authorship or identify the source ofan illegal redistribution.

Birthmarks only confirm that one program is a copy ofanother.

February 9, 2006 – p. 48

Skidmore College

Software Birthmarking

A Birthmarking system consists of the following functions:

extract(p) → bp

extract(q) → bq

similarity(p, q) → [0, 1]

We want to develop an algorithm such that

B is resilient to various transformations.

B is credible.

February 9, 2006 – p. 49

Skidmore College

Software Birthmarking

Idea similar to that of:

Plagiarism detection

Code clones

What makes it unique is that a birthmark is computed at themachine code level and considers semantics-preservingtransformations.

February 9, 2006 – p. 50

Skidmore College

Myles and Collberg

Compute the set of unique opcode sequences of length k for aset of modules.

k = 3 minimized the probability of false positives whilemaximizing resistance to transformations.

ACM SAC 2005.

February 9, 2006 – p. 51

Skidmore College

Myles and Collberg

k-gram where k = 2

� �Method vo id main ( j a v a . l ang . S t r i n g [ ] )

0 g e t s t a t i c #13 < F i e l d j a v a . i o . P r i n tS t r eam out>

3 new #16 <C l a s s j a v a . l ang . S t r i n gBu f f e r>

6 dup

7 l d c #18 < S t r i n g ”15! = ”>

9 i n v o k e s p e c i a l #23 <Method j a v a . l ang . S t r i n gB u f f e r ( j a v a . l ang . S t r i n g)>

12 ldc2 w #24 <Long 15>

15 i n v o k e s t a t i c #29 <Method l ong f a c t ( l ong)>

18 i n v o k e v i r t u a l #33 <Method j a v a . l ang . S t r i n gB u f f e r append ( l ong)>

21 i n v o k e v i r t u a l #37 <Method j a v a . l ang . S t r i n g t o S t r i n g ()>

24 i n v o k e v i r t u a l #40 <Method vo id p r i n t l n ( j a v a . l ang . S t r i n g)>

27 r e t u r n

� �

� �{( g e t s t a t i c , new ) ,

(new , dup ) ,

( dup , l d c ) ,

( ldc , i n v o k e s p e c i a l ) ,

( i n v o k e s p e c i a l , l dc2 w ) ,

( ldc2 w , i n v o k e s t a t i c ) ,

( i n v o k e s t a t i c , i n v o k e v i r t u a l ) ,

( i n v o k e v i r t u a l , i n v o k e v i r t u a l ) ,

( i n v o k e v i r t u a l , r e t u r n )}

� �

February 9, 2006 – p. 52

Skidmore College

Myles and Collberg

Computing similarity:

similarity(bp, bq) = |bp∩bq |

|bp|

February 9, 2006 – p. 53

Skidmore College

Birthmarks and Watermarks

Birthmarks provide weaker evidence than software watermarks.

They can indicate that one program is likely to be a copyof another, but not who the original author is or who isguilty of piracy.

Birthmarks can be used in instances where watermarking isnot feasible

Birthmarks can be used in conjunction with watermarking toprovide stronger evidence of theft.

There are instances where watermarks are destroyed butbirthmarks are not.

February 9, 2006 – p. 54

Skidmore College

Summary

The IP in software is threatened in several different ways.

There are a variety of techniques to address the differentissues.

The goal behind software-based techniques is to require“enough” time, effort, and/or resources to break such that itis less costly for the attacker to simply rewrite the software orpurchase legal copies.

Currently no single mechanism exists to prevent all threethreats.

By combining the techniques, a stronger defense whichprovides multiple levels of protection can be achieved.

February 9, 2006 – p. 55

Skidmore College

Questions?

February 9, 2006 – p. 56

Skidmore College

Arboit Algorithm 2

k branching points throughout the application are randomlyselected.

At each branching point, MTbi

or MFbi

is created and a method

call is appended.

The bits of the watermark are encoded in the opaque methodthrough the opaque predicate that it evaluates.

February 9, 2006 – p. 57

Skidmore College

Arboit Algorithm 2 Example

� �

c l a s s C{

vo id m1( i n t a , i n t b ){

. . .

i f ( a <= b ) { . . . }

e l s e { . . . }

. . .

}

}� �

W⇒

� �

c l a s s C{

boolean m2(){

i n t c = 1 ;

r e t u r n ( c∗c >= 0);

}

vo id m1( i n t a , i n t b ){

. . .

i f ( ( a <= b) && m2 ( ) ) { . . . }

e l s e { . . . }

. . .

}

}� �

February 9, 2006 – p. 58

Skidmore College

Tamada et al.

Specific to Java class files.

Composed of 4 individual birthmarks

constant value in field variables

sequence of method calls

inheritance structure

used classes

IASTED International Conference on Software Engineering2004.

February 9, 2006 – p. 59

Skidmore College

Tamada et al.

Constant value in field values:� �

pub l i c c l a s s Sk inPane l extends JPane l

implements SandMarkGUIConstants

{

p r i v a t e Image image ;

p r i v a t e i n t imgWidth = −1;

p r i v a t e i n t imgHeight = −1;

p u b i l c Sk inPane l (){

s e tLayou t ( n u l l ) ;

se tBackground (new Co lo r (0 xe8d5bd ) ) ;

image = Too lK i t . g e tDe f a u l tToo lK i t ( ) . get Image (

g e tC l a s s ( ) . g e tC l a s s Load e r ( ) . g e tRe sou r c e (SAND IMAGE ) ) ;

i f ( image != n u l l ){

MediaTracker med = new MediaTracker ( t h i s ) ;

med . addImage ( image , 0 ) ;

t r y{

med . wa i t F o rA l l ( 10000 ) ;

}catch ( Excep t i on e){

throw new Runt imeExcept ion ( ex ) ;

}

wh i l e ( ( imgWidth = image . getWidth ( n u l l ) == −1 ||

( imgHeigth = image . g e tHe i gh t ( n u l l ) == −1);

}

}

}

� �

⇒� �

( j a v a . awt . Image , n u l l )

( i n t , −1)

( i n t , −1)

� �

February 9, 2006 – p. 60

Skidmore College

Tamada et al.

Sequence of method calls:

� �pub l i c c l a s s Sk inPane l extends JPane l implements SandMarkGUIConstants {

p r i v a t e Image image ; i n t imgWidth = −1; i n t imgHeight = −1;


s e tLayou t ( n u l l ) ; se tBackground (new Co lo r (0 xe8d5bd ) ) ;

image = Too lK i t . g e tDe f a u l tToo lK i t ( ) . get Image ( g e tC l a s s ( ) . g e tC l a s s Load e r ( ) . g e tRe sou r ce (SAND IMAGE ) ) ;


MediaTracker med = new MediaTracker ( t h i s ) ; med . addImage ( image , 0 ) ;

t r y{

med . wa i t F o rA l l ( 10000 ) ;



}

wh i l e ( ( imgWidth = image . getWidth ( n u l l ) == −1 || ( imgHeigth = image . g e tHe i gh t ( n u l l ) == −1);

}

}

}

� �

� �j a v a x . swing . JPane l.< i n i t >() , j a v a . awt . Co lo r .< i n i t >( i n t ) , j a v a . awt . Too lK i t j a v a . awt . Too l k i t . g e tDe f au l tTookk i t ( ) ,

C l a s s Ojec t . g e tC l a s s ( ) , C l a s sLoade r C l a s s . g e tC l a s s Load e r ( ) , j a v a . net .URL C l a s sLoade r . g e tRe sou r c e ( S t r i n g ) ,

j a v a . awt . Image j a v a . awt . Too l k i t . get Image ( j a v a . net .URL) , j a v a . awt . MediaTracker.< i n i t >(j a v a . awt . Component ) ,

vo id j a v a . awt . MediaTracker . addImage ( j a v a . awt . Image , i n t ) , boolean j a v a . awt . MediaTracker . w a i t F o rA l l ( l ong ) ,

Runt imeExcept ion.< i n i t >(Throwable ) , i n t j a v a . awt . Image . getWidth ( j a v a . awt . image . ImageObserver ) ,

i n t j a v a . awt . Image . g e tHe i gh t ( j a v a . awt . image . ImageObserver ) ,

� �

February 9, 2006 – p. 61

Skidmore College

Tamada et al.

Inheritance structure:� �



{












t r y{

med . wa i t F o rA l l ( 10000 ) ;



}



}

}

}

� �

⇒� �

j a v a x . swing . JPanel ,

j a v a x . swing . JComponent ,

j a v a . awt . Conta ine r ,

j a v a . awt . Component ,

j a v a . l ang . Object

� �

February 9, 2006 – p. 62

Skidmore College

Tamada et al.

Used classes:� �



{












t r y{

med . wa i t F o rA l l ( 10000 ) ;



}



}

}

}

� �

⇒

� �j a v a . awt . Co lor ,

j a v a . awt . Component ,

j a v a . awt . Image ,

j a v a . awt . image . ImageObserver ,

j a v a . awt . MediaTracer ,

j a v a . awt . Too l k i t ,

j a v a . l ang . C la s s ,

j a v a . l ang . C la s sLoade r ,

j a v a . l ang . Oject ,

j a v a . l ang . Runt imeExcept ion ,

j a v a . l ang . S t r i ng ,

j a v a . l ang . Throwable ,

j a v a . net .URL ,

j a v a x . swing . JPane l

� �

February 9, 2006 – p. 63

Software-Based Approaches to Software...

Documents

Transcript of Software-Based Approaches to Software...