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

1. A Framework for Source-Code-Level Interprocedural Dataflow Analysis of AspectJ Software Guoqing Xu and Atanas Rountev Ohio State University Supported by NSF under CAREER grant CCF-0546040

2. Outline • Motivation • Program representation • Control flow representation • Data flow representation • Proof-of-concept analyses • Object effect analysis • Dependence-analysis-based slicing • Experimental evaluation

3. 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

4. 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

5. 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

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

7. 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_* placeholdermethod to represent call to proceed • Dynamic advices • Use ph_decision placeholder decision node to guard call sites of dynamic advices

8. root before1 after1 around1 p.setX before2

9. Handling of Dynamic Advices ph_decision T F before1 return ph_decision T F around1 ph_proceed1 return return exit

10. 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); }

11. 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

12. 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)

13. 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)

14. Handling of Around Advice (Cond.) • Call to a placeholder method within an around advice • Use dv to select calls to placeholder methods • e.g. voidaround( 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); } }

15. 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

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

17. 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

18. 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

19. 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

20. 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

21. 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

22. Numbers of Edges in ICFG and SDG ▲- SDG Edges ■- ICFG edges # ICFG edges 2X smaller #SDG edges 3X smaller

23. Object Effect Analysis • The analysis has short running time • E.g. less than 3 sec for the largest program

24. Slicing Relevance Ratio and Time

25. 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