PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

A Framework for Source-Code-Level Interprocedural Dataflow Analysis of AspectJ

Software

Guoqing Xu and Atanas Rountev

Ohio State UniversitySupported by NSF under CAREER grant CCF-

0546040

22 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Outline Motivation Program representation- Control flow representation- Data flow representation

Proof-of-concept analyses- Object effect analysis- Dependence-analysis-based slicing

Experimental evaluation

33 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Interprocedural Dataflow Analysis Interprocedural dataflow analysis is important- For various software engineering and compiler

construction tasks- e.g. performance optimization, static software

verification, testing, software understanding and evolution

Powerful analyses for AspectJ are needed- AOP becomes more and more popular- Need good program representation- Need new algorithms

44 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Program Representation for AspectJ Properties of a good representation- Should be easy to use for various clients

Adapt existing Java analysis algorithm- Should provide clean separation between the base

code and aspects Automated reasoning of aspects-base-code

interaction

Advantages of source-code-level over bytecode-level analysis- Produces more relevant results- Provides clean separation of base code and aspects- Faster running time

55 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Proposed representation Has both properties- Take the large body of existing analysis algorithms

for Java, and adapt them easily to AspectJ- Define new analysis algorithms specifically for

AspectJ features Control flow representation [ICSE’07]- Complex interactions of advices- Dynamic advices

Data flow representation [this paper]- Using calls and returns along chains of nested

advice invocations- Expose the decision-making data for dynamic

advices

66 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Running Exampleclass Point { int x; void setX(int x) { this.x = x; } static void main(String[] a) { Point p = new Point(); p.setX(10); }}aspect BoundPoint { pointcut setterX(Point p) : call(void Point.setX(*)) && target(p); … // advices}

/* before1 */ before(Point p, int x) : setterX(p) { … }

/* around1 */ void around(Point p, int x) : setterX(p) { … proceed(p,x); … }

/* before2 */before(Point p) : setterX(p) { … }

/* after1 */after(Point p) : setterX(p) { … }

77 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Control-Flow Representation Interprocedural Control Flow Graph (ICFG)- Java-like representation for “normal” calls

No join points- New representation for interactions at join points

Interaction graph

Multiple advices applicable at a join point- Calls to represent nesting relationships- Use ph_* placeholder method to represent call to

proceed

Dynamic advices- Use ph_decision placeholder decision node to guard

call sites of dynamic advices

88 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

root

around1

before2 p.setX

before1after1

ph_root

entry

return

exit

before1

return

around1

after advices ...

before1

entry

exit

...

around1

exit

return

proceed

...

entry

...

ph_proceed1

exit

return

before2

entry entry

exit

...

return

p.setX

before2

entry

exit

...

Point.setX

CFG edge Call edge

99 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Handling of Dynamic Advices

before1

return

around1

return

ph_decision

ph_decision

T

T

ph_proceed1

F

F

exit

return

1010 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Data-flow Representation for IG Declarations for placeholder methods - Create a formal parameter for (1) the receiver

object or (2) a formal parameter of the crosscut method

- e.g. for shadow p.setX(6), the entry method is declared as void ph_root(Point arg0, int arg1)

Declarations for non-around-advice- The original list of formal parameters is directly

used- e.g.void before1(int arg0, Point arg1)

Call sites for advices are built with appropriate actual parameters - void ph_root(Point arg0, int arg1) { before1(arg1,

arg0); }

1111 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Handling of Around Advice Handling of around advice is

complicated- It must have all the necessary parameters of

the shadow call site- Parameters needed by an around advice can be

different for different shadows abc solution: replicate the body of an around

advice for each shadow that the around advice matches

Our solution: construct a globally valid list that includes parameters required at all matching shadows

1212 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Example setX(Point arg0, int arg1) setY(Point arg2, float arg3)

setZ(double arg4, Point arg5)around(Point p):call(void Point.set*(*)) &&

target(p)

around(Point arg0, int arg1, Point arg2, float

arg3, double arg4, Point arg5, int dv)

1313 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Handling of Around Advice Call to around advice- Non-trivial actual parameters are passed to

the call site only for the formals corresponding to the currently-active shadow

- A unique shadow ID is given for dv - e.g. setX(Point arg0, int arg1)

setY(Point arg2, float arg3) setZ(double arg4, Point arg5)

around(Point p):call(void Point.set*(*)) && target(p)around(Point arg0, int arg1, Point arg2, float arg3, double arg4, Point arg5, int dv) for p.setX(..), around(arg0, arg1, null, 0, 0, null, 0) for p.setY(..), around(null, 0, arg2, arg3, 0, null, 1)

1414 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Handling of Around Advice (Cond.) Call to a placeholder method within an around

advice- Use dv to select calls to placeholder methods- e.g. void around( Point arg0, int arg1, Point arg2, float arg3, double arg4, Point arg5, int dv){ switch(dv){ //corresponding to shadow setX case 0: ph_proceed0(arg0, arg1); break; //corresponding to shadow setY case 1: ph_proceed1(arg2, arg3); break; //corresponding to shadow setZ case 2: ph_proceed2(arg4, arg5); } }

1515 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Data with ph_decision Nodes For a dynamic advice, the data that

contributes to dynamic decision making is associated with the ph_decision node that guards the call to the advice- E.g. before(Point p, int i):if(i > 0) && args(i) &&

target(p) both p and i are associated with the ph_decision

node

1616 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

So What? Data flow is explicit with parameter passing Advice nesting relationship is clear Ready for data-flow analysis

1717 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Object Effect Analysis Compute for each object passed from the base code

into an advice, a state machine encoding all events that must happen on the object- The state machine is represented by a regular expression- Can directly be used for program verification (e.g. checking

type-state based properties)- E.g. (reset | ε) (( setX | ε) | ( getX (( setX | ε ) |( setRectangular |

ε ))))

((wx wy | ε)) ( ( (wx | ε) | ( rx ( ( wx | ε) | (( wx wy) | ε))))) - At the core of this analysis is a must-alias analysis

A typical example of interprocedural analysis for AspectJ- Need to track the interprocedural flow of data- Need to be aware of the separation between base code and

aspects

1818 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Object Effect Analysis (Cond.) Data flow problem- We define a lattice element for each reference-

typed formal parameter of a ph_root method- E.g. for void ph_root(Point arg0, int arg1) The lattice is {larg0, ┴ ,┬ }- Transfer functions: v1 = v2: fn(S) = S[v1 S(v2)] v1 = v2.fld: fn(S) = S[v1 ┴] v1.fld = v2: fn(S) = S v1 = new X: fn(S) = S[v1 ┴] other nodes: fn(S) = S - Compute meet-over-all-valid-paths soluction MVPn

1919 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Analysis Algorithm Phase 1: relate formals to variables

- For each variable in a method, compute a set of formal parameters that the variable may alias

- Bottom-up traverse call graph to compute a summary function that relates the return value of a method to its parameter(s)

Phase 2: Propagation of lattice elements- Top-down traverse the call graph starting from each ph_root

method- For each variable, replace the formal parameter associated to

it with the corresponding lattice element(s)

Phase 3: Effect graph construction- Prune ICFG by removing nodes that do not have a lattice

element- Compute SCC in the graph, and bottom-up traverse the SCC-

DAGs

2020 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Dependence-based Slicing Another typical example of interprocedural

dataflow analysis Given existing slicing algorithms for Java,

adapting it to AspectJ is very simple- Variables associated with ph_decision nodes are

considered as used - The slice for a call to proceed in an around advice

includes slices that are computed for the group of calls to ph_proceed methods in the advice

2121 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Experimental Evaluation Comparison of ICFG and SDG sizes between

source-code analysis and bytecode analysis Effect analysis results comparison between

using must-alias analysis and may-alias analysis

Slice relevance comparison between source-code slicing and bytecode slicing

Implementation based on the abc compiler- Between static weaving and advice weaving- Intraprocedural representation based on Jimple

from abc

2222 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Numbers of Edges in ICFG and SDG

# ICFG edges 2X

smaller

#SDG edges 3X smaller

▲ - SDG Edges

■- ICFG edges

2323 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Object Effect Analysis

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100120

Must / May

The analysis has short running time- E.g. less than 3 sec for the largest program

2424 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Slicing Relevance Ratio and Time

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2525 PRESTO: Program Analyses and Software Tools Research Group, Ohio State University

Conclusions We propose a program representation for AspectJ

software- A control flow representation and a data flow representation

Two proof-of-concepts analyses- Both are IDE (Interprocedural Distributive Environment)

problems, which require context-sensitivity and flow-sensitivity

- Representative of (1) a large class of existing Java analysis algorithms and (2) potentially new algorithms designed specifically for AspectJ

Experimental results- Source-code-level analysis is superior to bytecode-level

analysis- Program representation is easy to use and adapt existing

algorithms- New algorithms can be easily designed