The document discusses 'Rejuvenate Pointcut', a technique for recovering pointcut expressions in evolving aspect-oriented software to maintain the integrity of pointcuts across code changes. It outlines the motivation behind the approach, the methodology for analyzing and rejuvenating pointcuts, and evaluation results demonstrating its effectiveness in identifying new join points introduced in later software versions. The research includes a tool for implementation, available for public use, along with related materials and technical reports.

1. Rejuvenate Pointcut
A Tool for Pointcut Expression Recovery in Evolving
Aspect-Oriented Software
Raffi Khatchadourian1 Phil Greenwood2 Awais Rashid2
Guoqing Xu1
1 Ohio State University
2 Lancaster University
International Conference on Aspect-Oriented Software
Development, 2009
A

2. Motivation
Approach
Evaluation
More Information
Outline
1 Motivation
2 Approach
3 Evaluation
4 More Information
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

3. Motivation
Approach
Evaluation
More Information
Base-code
package p ;
public class A {
int f ;
void m1 () {
int a = f + 1;
}
Two methods whose
name begins with the
void m2 () {
character m.
int b = f + 2;
}
void n () {
int c = f + 3;
}
}
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

4. Motivation
Approach
Evaluation
More Information
Base-code
package p ;
public class A {
int f ;
void m1 () {
Two methods whose int a = f + 1;
name begins with the }
character m. void m2 () {
One method whose name int b = f + 2;
does not begin with the }
character m. void n () {
int c = f + 3;
}
}
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

5. Motivation
Approach
Evaluation
More Information
Base-code
package p ;
public class A {
int f ;
Two methods whose void m1 () {
name begins with the int a = f + 1;
character m. }
One method whose name void m2 () {
does not begin with the int b = f + 2;
character m. }
All method bodies access void n () {
a field f. int c = f + 3;
}
}
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

6. Motivation
Approach
Evaluation
More Information
Along Came a Pointcut
pointcut fragile() : execution(* m*(..));
Base-code V1
Selects m1() and m2() but not n().
Assume pointcut is correct in V1.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

7. Motivation
Approach
Evaluation
More Information
Along Came a Pointcut
pointcut fragile() : execution(* m*(..));
Base-code V1
Selects m1() and m2() but not n().
Assume pointcut is correct in V1.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

8. Motivation
Approach
Evaluation
More Information
Along Came a Pointcut
pointcut fragile() : execution(* m*(..));
Base-code V1
Selects m1() and m2() but not n().
Assume pointcut is correct in V1.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

9. Evolution
// . . .
void p () {
int d = f + 4;
}
// . . .
pointcut fragile() : execution(* m*(..));
Base-code V2
Same pointcut selects m1() and m2() but not n() and p().
Fragile!
CCC applies to p() in V2 but not selected!
How to identify such join points as code evolves?

10. Evolution
// . . .
void p () {
int d = f + 4;
}
// . . .
pointcut fragile() : execution(* m*(..));
Base-code V2
Same pointcut selects m1() and m2() but not n() and p().
Fragile!
CCC applies to p() in V2 but not selected!
How to identify such join points as code evolves?

11. Evolution
// . . .
void p () {
int d = f + 4;
}
// . . .
pointcut fragile() : execution(* m*(..));
Base-code V2
Same pointcut selects m1() and m2() but not n() and p().
Fragile!
CCC applies to p() in V2 but not selected!
How to identify such join points as code evolves?

12. Evolution
// . . .
void p () {
int d = f + 4;
}
// . . .
pointcut fragile() : execution(* m*(..));
Base-code V2
Same pointcut selects m1() and m2() but not n() and p().
Fragile!
CCC applies to p() in V2 but not selected!
How to identify such join points as code evolves?

13. Evolution
// . . .
void p () {
int d = f + 4;
}
// . . .
pointcut fragile() : execution(* m*(..));
Base-code V2
Same pointcut selects m1() and m2() but not n() and p().
Fragile!
CCC applies to p() in V2 but not selected!
How to identify such join points as code evolves?

14. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
A.f declares_field A contains p
gets_field declares_method
A.n()
gets_field declares_method
A.m1()
Would execution(* A.n()) also be suggested?

15. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
A.f declares_field A contains p
gets_field declares_method
A.n()
gets_field declares_method
A.m1()
Would execution(* A.n()) also be suggested?

16. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
A.f declares_field A contains p
gets_field declares_method
A.n()
gets_field declares_method
A.m1()
Would execution(* A.n()) also be suggested?

17. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
A.f declares_field A contains p
gets_field declares_method
A.n()
gets_field declares_method
A.m1()
Would execution(* A.n()) also be suggested?

18. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
declares_field
A.f gets_field A.n() declares_method A contains p
gets_field declares_method
gets_field A.p() declares_method
A.m1()
Would execution(* A.n()) also be suggested?

19. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
declares_field
A.f gets_field A.n() declares_method A contains p
gets_field declares_method
gets_field A.p() declares_method
A.m1()
Would execution(* A.n()) also be suggested?

20. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
declares_field
A.f gets_field A.n() declares_method A contains p
gets_field declares_method
gets_field A.p() declares_method
A.m1()
Would execution(* A.n()) also be suggested?

21. Pointcut Rejuvenation: Leveraging Commonality
Phase I: Analysis using Concern Graphs
Extract commonalities between currently selected join points.
Phase II: Rejuvenation
Apply extracted patterns to new version of the base-code.
A.m2()
gets_field declares_method
declares_field
A.f gets_field A.n() declares_method A contains p
gets_field declares_method
gets_field A.p() declares_method
A.m1()
Would execution(* A.n()) also be suggested?

22. Suggestion Ranking Scheme
Measurements for Suggestion Ranking
α error: How strong are the relationships between advised
shadows compared to ones captured by a pattern?
β error: How well does the pattern express the same
intentions as the pointcut?

23. Suggestion Ranking Scheme
Measurements for Suggestion Ranking
α error: How strong are the relationships between advised
shadows compared to ones captured by a pattern?
β error: How well does the pattern express the same
intentions as the pointcut?
All Join Point Shadows

24. Suggestion Ranking Scheme
Measurements for Suggestion Ranking
α error: How strong are the relationships between advised
shadows compared to ones captured by a pattern?
β error: How well does the pattern express the same
intentions as the pointcut?
ω a join point shadow; code correspondi
All Join Point Shadows
A a piece of advice
Apce a pointcut bound to advice A; a set of
Apce a subsequent revision of Apce
P the original program, the underlying b
P a subsequence revision of program P
ΩP the set of join point shadows containe
IG P a finite graph representing structura
tween program elements in P
π an acyclic path (sequence of arcs) in I
ΠP a set of acyclic paths derived from pro

25. P
o.w.
Suggestion Ranking Scheme
+
(CG P ))|
Measurements for Suggestion Ranking
if |Apce | = 0
α error: How strong are the relationships between advised
+
ths(CG P ))| shadows compared to ones captured by a pattern? (2
β error:
o.w.
How well does the pattern express the same
intentions as the pointcut?
=0
ω a join point shadow; code correspondi
A
All Join Point Shadows
a piece of advice
(3
Apce a pointcut bound to advice A; a set of
Apce a subsequent revision of Apce
π )) + err β (ˆ , Apce )abs(ˆ ) original program, the underlying b
ˆ π P
πthe
(4
P a subsequence revision of program P
ΩP the set of join point shadows containe
tribute equations. IG P a finite graph representing structura
tween program elements in P
π an acyclic path (sequence of arcs) in I
ΠP a set of acyclic paths derived from pro

26. P
o.w.
Suggestion Ranking Scheme
+
(CG P ))|
Measurements for Suggestion Ranking
if |Apce | = 0
α error: How strong are the relationships between advised
+
ths(CG P ))| shadows compared to ones captured by a pattern? (2
β error:
o.w.
How well does the pattern express the same
intentions as the pointcut?
=0
ω a join point shadow; code correspondi
A
All Join Point Shadows
a piece of advice
(3
Apce a pointcut bound to advice A; a set of
Apce a subsequent revision of Apce
π )) + err β (ˆ , Apce )abs(ˆ ) original program, the underlying b
ˆ π P
πthe
(4
P a subsequence revision of program P
ΩP the set of join point shadows containe
tribute equations. IG P a αfinite graph representing structura
tween program elements in P
π an acyclic path (sequence of arcs) in I
ΠP a set of acyclic paths derived from pro

27. P
o.w.
Suggestion Ranking Scheme
+
(CG P ))|
Measurements for Suggestion Ranking
if |Apce | = 0
α error: How strong are the relationships between advised
+
ths(CG P ))| shadows compared to ones captured by a pattern? (2
β error:
o.w.
How well does the pattern express the same
intentions as the pointcut?
=0
ω a join point shadow; code correspondi
A
All Join Point Shadows
a piece of advice
(3
Apce a pointcut bound to advice A; a set of
Apce a subsequent revision of Apce
π )) + err β (ˆ , Apce )abs(ˆ ) original program, the underlying b
ˆ π P
πthe
(4
P a subsequence revision of program P
ΩP the set of join point shadows containe
tribute equations. IG P a αfinite graph representing structura
tween program elements in P
π an acyclic path (sequence of arcs) in I
ΠP a set of acyclic paths derived from pro

28. P
o.w.
Suggestion Ranking Scheme
+
(CG P ))|
Measurements for Suggestion Ranking
if |Apce | = 0
α error: How strong are the relationships between advised
+
ths(CG P ))| shadows compared to ones captured by a pattern? (2
β error:
o.w.
How well does the pattern express the same
intentions as the pointcut?
=0
ω a join point shadow; code correspondi
A
All Join Point Shadows
a piece of advice
(3
Apce a pointcut bound to advice A; a set of
β Apce a subsequent revision of Apce
π )) + err β (ˆ , Apce )abs(ˆ ) original program, the underlying b
ˆ π P
πthe
(4
P a subsequence revision of program P
ΩP the set of join point shadows containe
tribute equations. IG P a αfinite graph representing structura
tween program elements in P
π an acyclic path (sequence of arcs) in I
ΠP a set of acyclic paths derived from pro

29. Motivation
Approach
Evaluation
More Information
Suggestion Confidence
Each suggestion is associated with a confidence value ([0,1]).
A suggestion inherits the confidence of the pattern that
produced it.
A pattern’s confidence is calculated using a combination of α,
β, and the depth of the patterns.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

30. Motivation
Approach
Evaluation
More Information
Suggestion Confidence
Each suggestion is associated with a confidence value ([0,1]).
A suggestion inherits the confidence of the pattern that
produced it.
A pattern’s confidence is calculated using a combination of α,
β, and the depth of the patterns.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

31. Motivation
Approach
Evaluation
More Information
Suggestion Confidence
Each suggestion is associated with a confidence value ([0,1]).
A suggestion inherits the confidence of the pattern that
produced it.
A pattern’s confidence is calculated using a combination of α,
β, and the depth of the patterns.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

32. Implementation

33. Motivation
Approach
Evaluation
More Information
But How Well Does It Work?
Correlation analysis (Phase I) on 20+ AspectJ benchmarks.
Average confidence was 0.66.
Applied to 4 multi-versioned AspectJ projects (Phase II).
Rejuvenated pointcuts in major releases (26 in total).
Able to identify 94% of new shadows introduced in later
versions
On average, appearing in the top 4% of results.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

34. Motivation
Approach
Evaluation
More Information
But How Well Does It Work?
Correlation analysis (Phase I) on 20+ AspectJ benchmarks.
Average confidence was 0.66.
Applied to 4 multi-versioned AspectJ projects (Phase II).
Rejuvenated pointcuts in major releases (26 in total).
Able to identify 94% of new shadows introduced in later
versions
On average, appearing in the top 4% of results.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

35. Motivation
Approach
Evaluation
More Information
But How Well Does It Work?
Correlation analysis (Phase I) on 20+ AspectJ benchmarks.
Average confidence was 0.66.
Applied to 4 multi-versioned AspectJ projects (Phase II).
Rejuvenated pointcuts in major releases (26 in total).
Able to identify 94% of new shadows introduced in later
versions
On average, appearing in the top 4% of results.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

36. Motivation
Approach
Evaluation
More Information
But How Well Does It Work?
Correlation analysis (Phase I) on 20+ AspectJ benchmarks.
Average confidence was 0.66.
Applied to 4 multi-versioned AspectJ projects (Phase II).
Rejuvenated pointcuts in major releases (26 in total).
Able to identify 94% of new shadows introduced in later
versions
On average, appearing in the top 4% of results.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

37. Motivation
Approach
Evaluation
More Information
But How Well Does It Work?
Correlation analysis (Phase I) on 20+ AspectJ benchmarks.
Average confidence was 0.66.
Applied to 4 multi-versioned AspectJ projects (Phase II).
Rejuvenated pointcuts in major releases (26 in total).
Able to identify 94% of new shadows introduced in later
versions
On average, appearing in the top 4% of results.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

38. Motivation
Approach
Evaluation
More Information
But How Well Does It Work?
Correlation analysis (Phase I) on 20+ AspectJ benchmarks.
Average confidence was 0.66.
Applied to 4 multi-versioned AspectJ projects (Phase II).
Rejuvenated pointcuts in major releases (26 in total).
Able to identify 94% of new shadows introduced in later
versions
On average, appearing in the top 4% of results.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

39. Motivation
Approach
Evaluation
More Information
Tool and Material Downloads
Tool research prototype publicly available at
http://code.google.com/p/rejuvenate-pc.
Research related material publicly available at
http://sites.google.com/site/pointcutrejuvenation.
Full evaluation available in corresponding technical report.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

40. Motivation
Approach
Evaluation
More Information
Tool and Material Downloads
Tool research prototype publicly available at
http://code.google.com/p/rejuvenate-pc.
Research related material publicly available at
http://sites.google.com/site/pointcutrejuvenation.
Full evaluation available in corresponding technical report.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut

41. Motivation
Approach
Evaluation
More Information
Tool and Material Downloads
Tool research prototype publicly available at
http://code.google.com/p/rejuvenate-pc.
Research related material publicly available at
http://sites.google.com/site/pointcutrejuvenation.
Full evaluation available in corresponding technical report.
Khatchadourian, Greenwood, Rashid, Xu Rejuvenate Pointcut