1. Interprocedural Analysis Noam Rinetzky Mooly Sagiv http://www.cs.tau.ac.il/~msagiv/courses/pa05.html Tel Aviv University 640-6706 Textbook Chapter 2.5

2. Outline u Challenges in interprocedural analysis u The trivial solution u Why isn’t it adequate u Simplifying assumptions u A naive solution u Join over valid paths u The call-string approach u The functional approach –A case study linear constant propagation –Context free reachability u Modularity issues u Other solutions

3. Challenges in Interprocedural Analysis u Respect call-return mechanism u Handling recursion u Local variables u Parameter passing mechanisms: value, value- result, reference, by name u Procedure nesting u The called procedure is not always known u The source code of the called procedure is not always available –separate compilation –vendor code –...

4. Extended Syntax of While a := x | n | a 1 op a a 2 b := true | false | not b | b 1 op b b 2 | a 1 op r a 2 S := [x := a] l | [call p(a, z)] l l’ | [skip] l | S 1 ; S 2 | if [b] l then S 1 else S 2 | while [b] l do S P := begin D S end D := proc id(val id*, res id*) is l S end l’ | D D

5. A Trivial treatment of procedures u Analyze a single procedure u After every call continue with conservative information –Global variables and local variables which “may be modified by the call” are mapped to 

6. A Trivial treatment of procedures begin proc p() is 1 [x := 1] 2 end 3 [call p()] 4 5 [print x] 6 end [a , x  ] [a , x  1] [x  0] [x][x] [x][x]

7. Advantages of the trivial solution u Can be easily implemented u Procedures can be written in different languages u Procedure inline can help u Side-effect analysis can help

8. Disadvantages of the trivial solution u Modular (object oriented and functional) programming encourages small frequently called procedures u Optimization –Modern machines allows the compiler to schedule many instructions in parallel –Need to optimize many instructions –Inline can be a bad solution u Software engineering – Many bugs result from interface misuse –Procedures define partial functions

9. Simplifying Assumptions u All the code is available u Simple parameter passing u The called procedure is syntactically known u No nesting u Procedure names are syntactically different from variables u Procedures are uniquely defined u Recursion is supported

10. Constant Example begin proc p() is 1 if [b] 2 then ( [a := a -1] 3 [call p()] 4 5 [a := a + 1] 6 ) [x := -2* a + 5] 7 end 8 [a=7] 9 ; [call p()] 10 11 ; [print(x)] 12 end

11. A naive Interprocedural solution u Treat procedure calls as gotos u Obtain a conservative solution u Find the least fixed point of the system: u Use Chaotic iterations DF entry (s) =  DF entry (v) =  {f(e)(DFentry(u) : (u, v)  E}

12. Simple Example begin proc p() is 1 [x := a + 1] 2 end 3 [a=7] 4 [call p()] 5 6 [print x] 7 [a=9] 8 [call p()] 9 10 [print a] 11 end proc p x=a+1 end a=7 call p 5 call p 6 print x a=9 call p 9 call p 10 print a [x  0, a  0] [x  0, a  7] [x  8, a  7] [x  8, a  9] [x , a  ]

13. Simple Example begin proc p() is 1 [x := a + 1] 2 end 3 [a=7] 4 [call p()] 5 6 [print x] 7 [a=9] 8 [call p()] 9 10 [print a] 11 end proc p x=a+1 end a=7 call p 5 call p 6 print x a=9 call p 9 call p 10 print a [x  0, a  0] [x  0, a  7] [x , a  ] [x , a  9] [x , a  ]

14. We want something better … u Let paths(v) denote the potentially infinite set paths from start to v (written as sequences of labels) u For a sequence of edges [e 1, e 2, …, e n ] define f [e 1, e 2, …, e n ]: L  L by composing the effects of basic blocks f [e 1, e 2, …, e n ](l) = f(e n ) (… (f(e 2 ) (f(e 1 ) (l)) …) u JOP[v] =  {f[e 1, e 2, …,e n ](  ) [e 1, e 2, …, e n ]  paths(v)}

15. Valid Paths () f1f1 f2f2 f k-1 fkfk f3f3 f4f4 f5f5 f k-2 f k-3 call q enter q exit q ret

16. void p() { if (...) { x = x + 1; p(); // p_calls_p1 x = x - 1; } return; } Invalid Path int x; void main() { x = 5; p(); return; }

17. A More Precise Solution u Only considers matching calls and returns (valid) u Can be defined via context free grammar u Every call is a different letter u Matching calls and returns Matched   | Matched Matched |( c Matched ) c for all [call p()] lc lr in P Valid  Matched | l c Valid for all [call p()] lc lr in P

18. A More Precise Solution u Only considers matching calls and returns (valid) u Can be defined via context free grammar u Every call is a different letter u Matching calls and returns Intra   | (l i,l j ) Intra for all l i, l j in Lab*\ Lab IP Matched   | Intra | Matched Matched | (l c,l n ) Matched (l x,l r ) for all [call p()] lc lr and p is ln S lx Valid  Matched | (l c,l n) Valid for all [call p()] lc lr for all [call p()] lc lr and p is ln S lx Let Lab* = all the labels in the program Lab IP ={l c,l r : [call p()] lc lr in the program}

19. The Join-Over-Valid-Paths (JVP) u For a sequence of edges [e 1, e 2, …, e n ] define f [e 1, e 2, …, e n ]: L  L by composing the effects of basic statements –f[](s)=s –f [e, p](s) = f[p] (f e (s)) u JVP l =  {f[e 1, e 2, …, e](  ) [e 1, e 2, …, e]  vpaths(l), e = (*,l)} u Compute a safe approximation to JVP u In some cases the JVP can be computed –Distributivity of f –Functional representation

20. The Call String Approach for Approximating JVP u No assumptions u Record at every node a pair (l, c) where l  L is the dataflow information and c is a suffix of unmatched calls u Use Chaotic iterations u To guarantee termination limit the size of c (typically 1 or 2) u Emulates inline (but no code growth) u Exponential in C u For a finite lattice there exists a C which leads to join over all valid paths

21. Simple Example begin proc p() is 1 [x := a + 1] 2 end 3 [a=7] 4 [call p()] 5 6 [print x] 7 [a=9] 8 [call p()] 9 10 [print a] 11 end proc p x=a+1 end a=7 call p 5 call p 6 print x a=9 call p 9 call p 10 print a [x  0, a  0] [x  0, a  7] 5,[x  0, a  7] 5,[x  8, a  7] [x  8, a  7] [x  8, a  9] 9,[x  8, a  9]  9,[x  10, a  9]  [x  10, a  9] 5,[x  8, a  7] 9,[x  10, a  9] 

22. begin 0 proc p() is 1 if [b] 2 then ( [a := a -1] 3 [call p()] 4 5 [a := a + 1] 6 ) [x := -2* a + 5] 7 end 8 [a=7] 9 ; [call p()] 10 11 ; [print(x)] 12 end 13 Recursive Example a=7 Call p 10 Call p 11 print(x) p If( … ) a=a-1 Call p 4 Call p 5 a=a+1 x=-2a+5 end 10:[x  0, a  7] [x  0, a  7] [x  0, a  0] 10:[x  0, a  7] 10:[x  0, a  6] 4:[x  0, a  6] 4:[x  -7, a  6] 10:[x  -7, a  6] 4:[x  -7, a  6] 4:[x  -7, a  7] 4:[x , a  ]

23. The Functional Approach u The meaning of a function is mapping from states into states u The abstract meaning of a function is function from an abstract state to abstract states

24. begin proc p() is 1 if [b] 2 then ( [a := a -1] 3 [call p()] 4 5 [a := a + 1] 6 ) [x := -2* a + 5] 7 end 8 [a=7] 9 ; [call p()] 10 11 ; [print(x)] 12 end Motivating Example a=7 Call p 10 Call p 11 print(x) p If( … ) a=a-1 Call p 4 Call p 5 a=a+1 x=-2a+5 end [x  0, a  7] [x  0, a  0] e.[x  -2e(a)+5, a  e(a)] [x  -9, a  7]

25. begin proc p() is 1 if [b] 2 then ( [a := a -1] 3 [call p()] 4 5 [a := a + 1] 6 ) [x := -2* a + 5] 7 end 8 [read(a)] 9 ; [call p()] 10 11 ; [print(x)] 12 end Motivating Example read(a) Call p 10 Call p 11 print(x) p If( … ) a=a-1 Call p 4 Call p 5 a=a+1 x=-2a+5 end [x  0, a  ] [x  0, a  0] e.[x  -2e(a)+5, a  e(a)] [x , a  ]

26. The Functional Approach u Main idea: Iterate on the abstract domain of functions from L to L u Two phase algorithm –Compute the dataflow solution at the exit of a procedure as a function of the initial values at the procedure entry (functional values) –Compute the dataflow values at every point using the functional values u Can compute the JVP

27. Example: Constant propagation u L = Var  N  { ,  } u Domain: F:L  L –(f 1  f 2 )(x) = f 1 (x)  f 2 (x) x=7 y=x+1 x=y  env.env[x  7]  env.env[y  env(x)+1] Id=  env  L.env  env.env[x  7] ○  env.env  env.env[y  env(x)+1] ○  env.env[x  7] ○  env.env

28. Example: Constant propagation u L = Var  N  { ,  } u Domain: F:L  L –(f 1  f 2 )(x) = f 1 (x)  f 2 (x) x=7y=x+1 x=y  env.env[x  7]  env.env[y  env(x)+1] Id=  env.env  env.env[y  env(x)+1]   env.env[x  7]

29. Running Example 1 init a=7 9 Call p 10 Call p 11 print(x) 12 p1p1 If( … ) 2 a=a-1 3 Call p 4 Call p 5 a=a+1 6 x=-2a+5 7 end 8 begin 0 end 13 NFunction 0 e.[x  e(x), a  e(a)]=id 1 3-13 e. 

30. Running Example 1 NFunction 1 e. [x  e(x), a  e(a)]=id 2id 7 8 e.[x  -2e(a)+5, a  e(a)] 3id 4 e.[x  e(x), a  e(a)-1] 5 f 8 ○ e.[x  e(x), a  e(a)-1] = e.[x  -2(e(a)-1)+5, a  e(a)-1] 6 7 e.[x  -2(e(a)-1)+5, a  e(a)]  e.[x  e(x), a  e(a)] 8 a, x.[x  -2e(a)+5, a  e(a)] a=7 9 Call p 10 Call p 11 print(x) 12 p1p1 If( … ) 2 a=a-1 3 Call p 4 Call p 5 a=a+1 6 x=-2a+5 7 end 8 begin 0 end 13 0 e.[x  e(x), a  e(a)]=id 10 e.[x  e(x), a  7] 11 a, x.[x  -2e(a)+5, a  e(a)] ○ f 10

31. Running Example 2 NFunction 1 [x  0, a  7] 2 7 8 [x  -9, a  7] 3 [x  0, a  7] 4 [x  0, a  6] 1 [x  -7, a  6] 6 [x  -7, a  7] 7 [x , a  7] 8 [x  -9, a  7] 1 [x , a  ] a=7 9 Call p 10 Call p 11 print(x) 12 p1p1 If( … ) 2 a=a-1 3 Call p 4 Call p 5 a=a+1 6 x=-2a+5 7 end 8 begin 0 end 13 0 [x  0, a  0] 10 [x  0, a  7] 11 [x  -9, a  7]

32. Issues in Functional Approach u How to guarantee that finite height for functional lattice? –It may happen that L has finite height and yet the lattice of monotonic function from L to L do not u Efficiently represent functions –Functional join –Functional composition –Testing equality –Usually non-trivial –But can be done for distributive functions

33. Example Linear Constant Propagation u Consider the constant propagation lattice u The value of every variable y at the program exit can be represented by: y =  {(a x x + b x )| x  Var * }  c a x,c  Z  { ,  } b x  Z u Supports efficient composition and “functional” join –[z := a * y + b] –What about [z:=x+y]? u Computes JVP

34. Functional Approach via Context Free Reachablity u The problem of computing reachability in a graph restricted by a context free grammar can be solved in cubic time u Can be used to compute JVP in arbitrary finite distributive data flow problems (not just bitvector) u Nodes in the graph correspond to individual facts u Efficient implementations exit (MOPED)

35. Conclusion u Handling functions is crucial for abstract interpretation u Virtual functions and exceptions complicate things u But scalability is an issue u Assume-guarantee helps –But relies on specifications