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IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

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Page 1: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMSMartin Görg and Jianjun Zhao

Computer Science Department, Shanghai Jiao Tong University

Page 2: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Outline

Motivation and Background Difference Analysis Algorithm Evaluation of Quality and Feasibility Conclusions

Page 3: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Motivation and Background

Page 4: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Static Semantic Difference Analysis

static: source code analysis at compile time

semantic: differences in behavior

P P’modified

Page 5: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Why solve the Problem?

Motivation Reduce testing costs Produce correct software

Possible applications Debugging support Regression test selection Program slicing

Page 6: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

AOP and AspectJ

AOP encapsulates crosscutting concerns AspectJ

implementation of AOP extension to Java

public class C { int i;

void m1() { } …}

Base Code

aspect A { double C.d;

before() : … { } ….}

Aspect Code

Introduce

Advise

Page 7: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

AspectJ Example

1 aspect Constraints {

2 public boolean Shape.immovable = false;

2 void around(Shape s) : execution (public Shape+.set*(..)) && target(s)

{

3 if (!s.immovable) {proceed( ) ; } }

}

ITD

around advice

Page 8: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Hammocks

Single entry Single exit For any directed graph

S

E

Page 9: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Motivational Example

1 public class Point extends Shape {

2 private int x, y;

3 public void setX(int i){

4 x = i;

}

5 public void setY(int i){

6 y = i;

}

1 public class Point extends Shape {

2 private int x, y;

3 public void setX(int i){

4 x = i;

}

5 void setY(int i){

6 y = i;

}

a change in visibility alters program execution

Page 10: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

for AspectJ Programs

Difference Analysis Algorithm

Page 11: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Algorithm Outline

1. For every module in P find a matching module in P’ (module-level matching)

2. Build extended CFGs for all modules in P and P’ and identify hammocks

3. Perform node-by-node comparison on every pair of hammock graphs (node-level matching)

Page 12: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Matching at Module Level

Signature matching

Disjunctive matching

Obtain best match from multiple candidates

public void p1.C1.add(int, Object)P: boolean p1.C1.add(int, Object)P’:

public void p1.C1.add(int, Object)P: boolean p1.C1.add(double, Object)P’:

Page 13: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

1. Matching at Module Level

Problem: Not every AspectJ construct has a signature (most importantly: advices)

Solution:a) Define new AspectJ signatures

(e.g. [strictfp] before (Formals) :

[throws TypeList] : Pointcut {Body})b) Define disjunctive patterns for these

signatures

Page 14: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

2. Build CFGs and Hammocks

Page 15: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

3. Matching at Node Level

Simultaneous graph traversal Node-by-node comparison Recursive Two user inputs

Similarity threshold (S) Maximum lookahead (LH)

Page 16: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

3. Matching at Node Level

X

E

Y

H

P

X

E

Y

H

P’

S

U V

e

P

S

U V’

e

P’

Similarity Threshold S = 0.5; Lookahead LH = 1

Page 17: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Quality and Feasibility

Tests and Evaluation

Page 18: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Program LOC Diffs Affected Matched Errors

ants 1451

4 26 2446 (100%)

0

bean 199 19 30 268 (100%) 0

cona-stack 381 1 9 730 (100%) 0

dcm 3140

684 1523 2771 (100%)

0

figure 148 42 101 177 (100%) 0

introduction

233 4 18 362 (98.3%)

6

nullcheck 2980

136 258 2828 (98.2%)

78

quicksort 115 14 27 155 (100%) 0

spacewar 3053

1 283 4496 (100%)

0

tracing 330 55 164 442 (100%) 0

Signature definitions and disjunctive matching Minimal change with maximal effect Deficits: some combinations and swapped

statements

Page 19: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

S < 0.6: LH has only minor impact LH 20: within one minute S 0.6: slow for LH > 20, but not needed

0 10 20 30

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

Performance results

Similarity = 0.0Similarity = 0.6

Similarity = 1.0

Lookahead

tim

e (

ms)

sca

led

to l

og

Page 20: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

New Findings and Open Tasks

Conclusions

Page 21: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

What we did

New signatures for AspectJ modules Disjunctive matching

for AspectJ and Java modules a solution for modified signatures

Application of hammock algorithm from OO to AO

Evaluations using a tool implementation

Page 22: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Conclusions

Disjunctive matching is a good idea modules are correctly matched more work for node-level matching eliminates work of type-level matching replaces user interaction

Type-level matching is not required Hammock graph matching can be applied

given:a) correctly matched modulesb) appropriately modeled and labeled CFGs

Page 23: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Future Work

Improve disjunctive matching patterns Extend CFG representations Solve the swapping problem Handle dynamic pointcuts

Page 24: IDENTIFYING SEMANTIC DIFFERENCES IN ASPECTJ PROGRAMS Martin Görg and Jianjun Zhao Computer Science Department, Shanghai Jiao Tong University

Questions?

Thank You for listening