Dataflow Analysis for Software Product Lines

1. Dataflow Analysis forSoftware Product Lines Claus Brabrand IT University of Copenhagen Universidade Federal de Pernambuco [ brabrand@itu.dk ] Márcio Ribeiro Universidade Federal de Alagoas Universidade Federal de Pernambuco [ mmr3@cin.ufpe.br ] Paulo Borba Universidade Federal de Pernambuco [ phmb@cin.ufpe.br ] TársisTolêdo Universidade Federal de Pernambuco [ twt@cin.ufpe.br ]

2. < Outline > • Introduction • Software Product Lines • Dataflow Analysis (recap) • Dataflow Analyses for Software Product Lines: • feature in-sensitive(A1)vsfeature sensitive(A2, A3, A4) • Results: • A1vsA2vsA3vsA4 (in theory and practice) • Related Work • Conclusion

3. Introduction • Traditional Software Development: • One program = One product • Product Line: • A ”family” of products (of N ”similar” products): = = = 1x CAR 1x CELL PHONE 1x APPLICATION CARS CELL PHONES APPLICATIONS customize SPL: (Family ofPrograms)

4. Software Product Line • SPL: • Feature Model: (e.g.: ψFM ≡ VIDEO  COLOR) Ø Family of Programs: customize { Color} COLOR VIDEO 2F COLORVIDEO { Video } VIDEO Set of Features: F = { COLOR, VIDEO } { Color, Video } Configurations: Ø,{Color},{Video},{Color,Video} 2F VALID

5. Software Product Line Conditional compilation: • SPL: Family of s:  : fF |  |  Program COLOR VIDEO #ifdef(  ) ... #endif Alternatively,via Aspects(as in AOSD) COLORVIDEO VIDEO *** null-pointer exception!in configurations: {Ø, {COLOR}} Logo logo; ... ... logo.use(); #ifdef (VIDEO) logo = new Logo(); #endif Example (SPL fragment) Similarly for; e.g.: ■uninitialized vars ■unused variables ■...

6. Analysis of SPLs • The Compilation Process: • ...and for Software Product Lines: 0100101 1110110 1010011 1110111 compile run result ERROR! ANALYZE! 0100101 1110110 1010011 1110111 0100101 1110110 1010011 1110111 run customize compile 0100101 1110110 1010011 1110111 run compile run compile result result result 2F ANALYZE! ERROR! ERROR! ANALYZE! ERROR! ANALYZE! Feature-sensitivedata-flow analysis !

7. < Outline > • Introduction • Software Product Lines • Dataflow Analysis (recap) • Dataflow Analyses for Software Product Lines: • feature in-sensitive(A1)vsfeature sensitive(A2, A3, A4) • Results: • A1vsA2vsA3vsA4 (in theory and practice) • Related Work • Conclusion

8. Dataflow Analysis L • Dataflow Analysis: • 1)Control-flow graph • 2)Lattice(finiteheight) • 3)Transfer functions(monotone) Example: "sign-of-xanalysis"

9. Analyzing a Program 1)Program 2)Build CFG 3)Make Equations Annotated with program points 4)Solveequations: fixed-point computation(iteration) 5) SOLUTION (least fixed point):

10. < Outline > • Introduction • Software Product Lines • Dataflow Analysis (recap) • Dataflow Analyses for Software Product Lines: • feature in-sensitive(A1)vsfeature sensitive(A2, A3, A4) • Results: • A1vsA2vsA3vsA4 (in theory and practice) • Related Work • Conclusion

11. A1(brute force) L void m() { int x=0; ifdef(A) x++; ifdef(B) x--; } • A1 (feature in-sensitive): • N = 2Fcompilations! ψFM = A∨B _ _ _ | | | c = {A}: c = {B}: c = {A,B}: int x= 0; int x= 0; int x= 0; 0 0 0 x++; x++; x++; + x--; x--; x--; 0/+ + -

12. A2(consecutive) L void m() { int x=0; ifdef(A) x++; ifdef(B) x--; } • A2 (feature sensitive!): ψFM = A∨B _ _ _ | | | c = {A}: c = {B}: c = {A,B}: int x= 0; int x= 0; int x= 0; ✓ ✓ ✓ 0 0 0 x++; x++; x++; ✓ ✓ ✗ A: A: A: + + 0 ✓ ✗ ✓ B: B: B: x--; x--; x--; 0/+ + -

13. A3(simultaneous) L void m() { int x=0; ifdef(A) x++; ifdef(B) x--; } • A3 (feature sensitive!): ψFM = A∨B _ _ _ | | | ∀c∈ {{A},{B},{A,B}}: ({A} = , {B} = , {A,B} = ) ✓ ✓ ✓ int x= 0; 0 0 0 ({A} = , {B} = , {A,B} = ) ✗ ✓ ✓ x++; A: + + 0 ({A} = , {B} = , {A,B} = ) ✗ ✓ ✓ B: x--; 0/+ + - ({A} = , {B} = , {A,B} = )

14. A4(shared) L void m() { int x=0; ifdef(A) x++; ifdef(B) x--; } • A4 (feature sensitive!): ψFM = A∨B _ | ψFM = A∨B: ( [[ψ]] = ) int x= 0; 0 ( [[ψ]] = ) …using BDD representation! (compact+efficient) x++; A: (A∨B)∧¬A∧¬B ≡ false i.e., invalid given wrt.the feature model, ψ! 0 + ( [[ψ∧¬A]] = , [[ψ∧A]] = ) B: x--; - 0/+ + 0 ( [[ψ∧¬A∧¬B]] = , [[ψ∧A∧¬B]] = , [[ψ∧¬A∧B]] = , [[ψ∧A∧B]] = )

15. Specification: A1, A2, A3, A4 A1 A2 A3 A4

16. A1, A2, A3, and A4 A2 A1 A4 A3

17. < Outline > • Introduction • Software Product Lines • Dataflow Analysis (recap) • Dataflow Analyses for Software Product Lines: • feature in-sensitive(A1)vsfeature sensitive(A2, A3, A4) • Results: • A1vsA2vsA3vsA4 (in theory and practice) • Related Work • Conclusion

18. Intraprocedural Evaluation • Four (qualitatively different)SPL benchmarks: • Implementation:A1, A2, A3, A4 in SOOT + CIDE • Evaluation:total time, analysis time, memory usage

19. Results (total time) • In theory: • In practice: 2F 2F 2F Feature sensitive (avg. gain factor): A2 (3x), A3 (4x), A4 (5x) (Reaching Definitions) 1x 1x 1x 2x 2x 2½x 3x 3x 5x 6x 8x 14x

20. Results (analysis time) • In theory: • In practice: A2 2F A3 vs On average (A2 vs A3): TIME(A4) : Depends ondegree of sharing in SPL ! A3 (1.5x) faster (Reaching Definitions) (caching!)

21. Results (memory usage) • In theory: • In practice: A2 A3 vs 2F SPACE(A4) : Depends ondegree of sharing in SPL ! Average 6.3 : 1 (Reaching Definitions)

22. < Outline > • Introduction • Software Product Lines • Dataflow Analysis (recap) • Dataflow Analyses for Software Product Lines: • feature in-sensitive(A1)vsfeature sensitive(A2, A3, A4) • Results: • A1vsA2vsA3vsA4 (in theory and practice) • Related Work • Conclusion

23. Related Work (DFA) • Path-sensitive DFA: • Idea of “conditionally executed statements” • Compute different analysis info along different paths (~ A2, A3, A4) to improve precision or to optimize “hot paths” • Predicated DFA: • Guard lattice values by propositional logic predicates (~ A4), yielding “optimistic dataflow values” that are kept distinct during analysis (~ A3and A4) “Constant Propagation with Conditional Branches” ( Wegman and Zadeck ) TOPLAS 1991 “Predicated Array Data-Flow Analysis for Run-time Parallelization” ( Moon, Hall, and Murphy ) ICS 1998 Our work:Automatically lift anyDFA to SPLs (with ψFM) ⇒feature-sensitive analysis for analyzing entire program family

24. Related Work (Lifting for SPLs) • Model Checking: • Type Checking: • Parsing: • Testing: Model checks all SPLs at the same time (3.5x faster) than one by one! (similar goal, diff techniques) Model Checking Lots of Systems: Efficient Verification of Temporal Properties in Software Product Lines” ( Classen, Heymans, Schobbens, Legay, and Raskin ) ICSE 2010 Type checking ↔ DFA (similar goal, diff techniques) Our: auto lift any DFA (uninitvars, null pointers, ...) “Type-Checking Software Product Lines - A Formal Approach” ( Kastnerand Apel ) ASE 2008 “Type Safety for Feature-Oriented Product Lines” ( Apel, Kastner, Grösslinger, and Lengauer) ASE 2010 (similar techniques, diff goal): Split and merging parsing (~A4) and also uses instrumentation “Variability-Aware Parsing in the Presence of Lexical Macros & C.C.” ( Kastner, Giarrusso, Rendel, Erdweg, Ostermann, and Berger )OOPSLA 2011 Select relevant feature combinations for a given test case Uses (hardwired) DFA (w/o FM) to compute reachability “Reducing Combinatorics in Testing Product Lines” ( Hwan, Kim, Batory, and Khurshid) AOSD 2011

25. Emerging Interfaces

26. Emerging Interfaces CBSoft 2011: *** Best Tool Award *** "A Tool for Improving Maintainability of Preprocessor-based Product Lines" ( MárcioRibeiro, TársisTolêdo, Paulo Borba, Claus Brabrand )

27. < Outline > • Introduction • Software Product Lines • Dataflow Analysis (recap) • Dataflow Analyses for Software Product Lines: • feature in-sensitive(A1)vsfeature sensitive(A2, A3, A4) • Results: • A1vsA2vsA3vsA4 (in theory and practice) • Related Work • Conclusion

28. Conclusion(s) • It is possible to analyzeSPLsusingDFAs • Wecanautomatically"lift"anydataflowanalysis and make it feature sensitive: • A2)Consecutive • A3)Simultaneous • A4)Shared Simultaneous • A2,A3,A4much faster (3x,4x,5x) than naive A1 • A3 is (1.5x) faster thanA2 (caching!) • A4saves lots of memoryvsA3(sharing!) 6.3 : 1

29. Future Work • Explorehow all thisscales to…: • In particular: • …relative speed of A1vsA2vsA3vsA4 ? • …which analyses arefeasiblevsin-feasible? INTER-procedural data-flow analysis In progress...!

30. < Obrigado*> *)Thanks

31. BONUS SLIDES

32. Results (analysis time) ?! • In theory: • In practice: A2 (caching!) Nx1 ≠ 1xN 2F A3 2F vs On average (A2 vs A3): TIME(A4) : Depends ondegree of sharing in SPL ! A3 (1.5x) faster (Reaching Definitions)

33. A2vsA3(caching) • Cache misses inA2vsA3: • Normal cache: • As expected, A2incurs more cache misses (⇒ slower!) • Full/no cache*: • As hypothesized, this indeed affects A2more than A3 • i.e.,A3has better cache properties thanA2 A2 A3 vs *) we flush the L2 cache, by traversing an8MB “bogus array” to invalidate cache!

34. IFDEF normalization • Refactor"undisciplined"(lexical) ifdefs into "disciplined"(syntactic) ifdefs: • Normalize "ifdef"s (by transformation):

35. Example Bug from Lampiro • Lampiro SPL (IM client for XMPP protocol): *** uninitialized variable "logo" • (if feature "GLIDER" is defined) • Similar problems with: • undeclared variables, unused variables, null pointers, ...

36. BDD (Binary Decision Diagram) =  F(A,B,C)= A(BC) A A BDD minimized BDD B B B C C C C C           • Compact and efficientrepresentation forboolean functions (aka., set of set of names) • FAST: negation, conjunction, disjunction, equality ! 

37. Formula ~ Set of Configurations • Definitions (given F, set of feature names): • f Ffeature name • c 2Fconfiguration(set of feature names) cF • X  22set of config's (set of set of feature names)X 2F • Exampleifdefs: F [[ BA]] = { {A}, {B}, {A,B} } F = {A,B} [[ A(BC)]] F = {A,B,C} = { {A,B}, {A,C}, {A,B,C} }

38. Feature Model (Example) Note: | [[FM]]| = 3<32 = |2F| • Feature Model: • Feature set: • Formula: • Set of configurations: Engine Air Air 1.0 1.4 F= {Car, Engine, 1.0, 1.4, Air} [[ ]] = FM  Car  Engine  (1.01.4)  Air1.4 { {Car, Engine, 1.0}, {Car, Engine, 1.4}, {Car, Engine, 1.4, Air} }

39. Conditional Compilation • The 'ifdef' construction: • Syntactic variant of lexical #ifdef • Propositional Logic: wherefF(finite set of feature names) • Example: A STM : 'ifdef' '('  ')' STM ifdef(A){ ... }  : fF |  |  status.print("you die"); ifdef (DeluxeVersion && ColorDisplay) { player.redraw(Color.red); Audio.play("crash.wav"); } lives = lives - 1;

40. Lexical#ifdef Syntacticifdef • Simple transformation: • We do not handle non-syntactic '#ifdef's: Nestedifdef'salso give rise to a conj. of formulas Fair assumption (also in CIDE)

41. A4: LazySplitting (usingBDDs) CASE 1: "COPY" CASE 2: "APPLY" CASE 3: "SPLIT"        =  Ø      = Ø [=l, ... ] [=l, ... ] [=l, ... ] : S : S : S l ' = fS(l) l ' = fS(l) l ' = fS(l ) [=l ', ... ] [=l,=l' ,...] [=l, ... ]