1. Use of Models in Analysis and Design Sriram K. Rajamani Rigorous Software Engineering Microsoft Research, India

2. Models Abstractions of reality All branches of science and engineering use models. Some examples: –Differential equations –State machines Models enable conquering complexity –Allow focus on one issue at a time, while ignoring others

3. Models in software engineering Mainstream mantra: –“Code is truth, and only truth” Models are used, but not widely –Requirements capturing (UML): Used specialized domains: telcom, automotive, embedded sytstems –Development tools: Testing and verification –Design Model driven development

4. This talk Use of models in analysis and design –Personal experience Analysis: –Extracting analyzable models from source code using iterative refinement Design: –My assessment of state of the art and important research problems

5. Models in Analysis

6. Software Validation Large scale reliable software is hard to build and test. Different groups of programmers write different components. Integration testing is a nightmare.

7. Property Checking Programmer provides redundant partial specifications Code is automatically checked for consistency Different from proving whole program correctness –Specifications are not complete

8. Interface Usage Rules Rules in documentation –Incomplete, unenforced, wordy –Order of operations & data access –Resource management Disobeying rules causes bad behavior –System crash or deadlock –Unexpected exceptions –Failed runtime checks

9. Does a given usage rule hold? Checking this is computationally impossible! Equivalent to solving Turing’s halting problem (undecidable) Even restricted computable versions of the problem (finite state programs) are prohibitively expensive

10. Why bother? Just because a problem is undecidable, it doesn’t go away!

11. Scientific curiosity Undecidability and complexity theory are the most significant contributions of theoretical computer science. Software property checking, a very practical and pressing problem is undecidable.

12. Automatic property checking = Study of tradeoffs Soundness vs completeness –Missing errors vs reporting false alarms Annotation burden on the programmer Complexity of the analysis –Local vs Global –Precision vs Efficiency –Space vs Time

13. Broad classification Underapproximations –Testing After passing testing, a program may still violate a given property Overapproximations –Type checking Even if a program satisfies a property, the type checker for the property could still reject it

14. Current trend Confluence of techniques from different fields: –Model checking –Automatic theorem proving –Program analysis Significant emphasis on practicality Several new projects in academia and industry

15. Model Checking Algorithmic exploration of state space of the system Several advances in the past decade: –symbolic model checking –symmetry reductions –partial order reductions –compositional model checking –bounded model checking using SAT solvers Most hardware companies use a model checker in the validation cycle

16. enum {N, T, C} state[1..2] int turn init state[1] = N; state[2] = N turn = 0 trans state[i]= N & turn = 0 -> state[i] = T; turn = i state[i] = N & turn !=0 -> state[i] = T state[i] = T & turn = i -> state[i] = C state[i] = C & state[2-i] = N -> state[i] = N state[i] = C & state[2-i] != N -> state[i] = N; turn = 2-i

17. N1,N2 turn=0 T1,N2 turn=1 T1,T2 turn=1 C1,N2 turn=1 C1,T2 turn=1 N1,T2 turn=2 T1,T2 turn=2 N1,C2 turn=2 T1,C2 turn=2 N = noncritical, T = trying, C = critical

18. Model Checking Strengths –Fully automatic (when it works) –Computes inductive invariants I such that F(I)  I –Provides error traces Weaknesses –Scale –Operates only on models How do you get from the program to the model?

19. Theorem proving –Early theorem provers were proof checkers They were built to support asssertional reasoning in the Hoare-Dijkstra style Cumbersome and hard to use –Greg Nelson’s thesis in early 80s paved the way for automatic theorem provers Theory of equality with uninterpreted functions Theory of lists Theory of linear arithmetic Combination of the above ! –Automatic theorem provers based on Nelson’s work are widely used ESC Proof Carrying Code

20. Theory of Equality. Symbols: =, , f, g, … Axiomatically defined: E = E E 2 = E 1 E 1 = E 2 E 1 = E 2 E 2 = E 3 E 1 = E 3 E 1 = E 2 f(E 1 ) = f(E 2 ) Example of a satisfiability problem: g(g(g(x)) = x  g(g(g(g(g(x))))) = x  g(x)  x Satisfiability problem decidable in O(n log n)

21. a : array [1..len] of int; int max := -MAXINT; i := 1; {  1  j  i. a[j]  max} while (i  len) if( a[i] > max) max := a[i]; i := i+1; endwhile {  1  j  len. a[j]  max} (  1  j  i. a[j]  max)  ( i > len)  (  1  j  len. a[j]  max}

22. Automatic theorem proving Strengths –Handles unbounded domains naturally –Good implementations for equality with uninterpreted functions linear inequalities combination of theories Weaknesses –Hard to compute fixpoints –Requires inductive invariants Pre and post conditions Loop invariants

23. Program analysis Originated in optimizing compilers –constant propagation –live variable analysis –dead code elimination –loop index optimization Type systems use similar analysis Are the type annotations consistent?

24. Program analysis Strengths –Works on code –Pointer aware –Integrated into compilers –Precision efficiency tradeoffs well studied flow (in)sensitive context (in)sensitive Weakenesses –Abstraction is hardwired and done by the designer of the analysis –Not targeted at property checking (traditionally)

25. Model Checking, Theorem Proving and Program Analysis Very related to each other Different histories –different emphasis –different tradeoffs Complementary, in some ways Combination can be extremely powerful

26. What is the key design challenge in a model checker for software? It is the model!

27. Model Checking Hardware Primitive values are booleans States are boolean vectors of fixed size Models are finite state machines !!

28. Characteristics of Software Primitive values are more complicated –Pointers –Objects Control flow (transition relation) is more complicated –Functions –Function pointers –Exceptions States are more complicated –Unbounded graphs over values Variables are scoped –Locals –Shared scopes Much richer modularity constructs –Functions –Classes

29. Sequential C program Finite state machines Source code FSM model checker Traditional approach

30. Sequential C program Finite state machines Source code FSM abstraction model checker C data structures, pointers, procedure calls, parameter passing, scoping,control flow Automatic abstraction Boolean program Data flow analysis implemented using BDDs SLAM Push down model

31. An optimizing compiler doubles performance every 18 years -Todd Proebsting Computing power doubles every 18 months -Gordon Moore

32. When I use a model checker, it runs and runs for ever and never comes back… when I use a static analysis tool, it comes back immediately and says “I don’t know” - Patrick Cousot

33. Source Code Testing Development Precise API Usage Rules (SLIC) Software Model Checking Read for understanding New API rules Drive testing tools Defects 100% path coverage Rules Static Driver Verifier

34. SLAM – Software Model Checking SLAM innovations –boolean programs: a new model for software –model creation (c2bp) –model checking (bebop) –model refinement (newton) SLAM toolkit –built on MSR program analysis infrastructure

35. SLIC Finite state language for stating rules –monitors behavior of C code –temporal safety properties –familiar C syntax Suitable for expressing control-dominated properties –e.g. proper sequence of events –can encode data values inside state

36. State Machine for Locking UnlockedLocked Error Rel Acq Rel state { enum {Locked,Unlocked} s = Unlocked; } KeAcquireSpinLock.entry { if (s==Locked) abort; else s = Locked; } KeReleaseSpinLock.entry { if (s==Unlocked) abort; else s = Unlocked; } Locking Rule in SLIC

37. prog. P’ prog. P SLIC rule The SLAM Process boolean program path predicates slic c2bp bebop newton

38. do { KeAcquireSpinLock(); nPacketsOld = nPackets; if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; } } while (nPackets != nPacketsOld); KeReleaseSpinLock(); Example Does this code obey the locking rule?

39. do { KeAcquireSpinLock(); if(*){ KeReleaseSpinLock(); } } while (*); KeReleaseSpinLock(); Example Model checking boolean program (bebop) U L L L L U L U U U E

40. do { KeAcquireSpinLock(); nPacketsOld = nPackets; if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; } } while (nPackets != nPacketsOld); KeReleaseSpinLock(); Example Is error path feasible in C program? (newton) U L L L L U L U U U E

41. do { KeAcquireSpinLock(); nPacketsOld = nPackets; b = true; if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; b = b ? false : *; } } while (nPackets != nPacketsOld); !b KeReleaseSpinLock(); Example Add new predicate to boolean program (c2bp) b : (nPacketsOld == nPackets) U L L L L U L U U U E

42. do { KeAcquireSpinLock(); b = true; if(*){ KeReleaseSpinLock(); b = b ? false : *; } } while ( !b ); KeReleaseSpinLock(); b b b b Example Model checking refined boolean program (bebop) b : (nPacketsOld == nPackets) U L L L L U L U U U E b b !b

43. Example do { KeAcquireSpinLock(); b = true; if(*){ KeReleaseSpinLock(); b = b ? false : *; } } while ( !b ); KeReleaseSpinLock(); b : (nPacketsOld == nPackets) b b b b U L L L L U L U U b b !b Model checking refined boolean program (bebop)

44. Observations about SLAM Automatic discovery of invariants –driven by property and a finite set of (false) execution paths –predicates are not invariants, but observations –abstraction + model checking computes inductive invariants (boolean combinations of observations) A hybrid dynamic/static analysis –newton executes path through C code symbolically –c2bp+bebop explore all paths through abstraction A new form of program slicing –program code and data not relevant to property are dropped –non-determinism allows slices to have more behaviors

45. Current status of SDV Runs on 100s of Windows drivers Finds several bugs, proves several properties SDV now transferred from MSR to Windows division Used to check several DDK and inbox drivers Beta Released at WINHEC 2005!

46. Static Driver Verifier

47. Driver: Parallel port device driver Rule: Checks that driver dispatch routines do not call IoCompleteRequest(…) twice on the I/O request packet passed to it by the OS or another driver

57. Call #1

64. Call #2

65. SLAM/SDV History (with Tom Ball) 1999-2001 –foundations, algorithms, prototyping –papers in CAV, PLDI, POPL, SPIN, TACAS March 2002 –Bill Gates review May 2002 –Windows committed to hire two Ph.D.s in model checking to support Static Driver Verifier July 2002 –running SLAM on 100+ drivers, 20+ properties September 3, 2002 –made initial release of SDV to Windows (friends and family) April 1, 2003 –made wide release of SDV to Windows (any internal driver developer) September, 2003 –team of six in Windows working on SDV –researchers moving into “consultant” role November, 2003 –demonstration at Driver Developer Conference May, 2005 –Beta ships at WinHEC 2005!

67. SLAM Boolean program model has proved itself Successful for domain of device drivers –control-dominated safety properties –few boolean variables needed to do proof or find real counterexamples Counterexample-driven refinement –terminates in practice –incompleteness of theorem prover not an issue

68. What is hard? Abstracting –from a language with pointers (C) –to one without pointers (boolean programs) All side effects need to be modeled by copying (as in dataflow) Open environment problem

69. What stayed fixed? Boolean program model Basic tool flow Repercussions: –newton has to copy between scopes –c2bp has to model side-effects by value-result –finite depth precision on the heap is all boolean programs can handle

70. What changed? Interface between newton and c2bp We now use predicates for doing more things refine alias precision via aliasing predicates newton helps resolve pointer aliasing imprecision in c2bp

71. Model Checking, Theorem Proving and Program Analysis Very related to each other Different histories –different emphasis –different tradeoffs Complementary, in some ways Combination can be extremely powerful

72. What worked well? Specific domain problem Safety properties Shoulders & synergies Separation of concerns Summer interns & visitors Strategic partnership with Windows

73. Predictions The holy grail of full program verification has been abandoned. It will probably remain abandoned Less ambitious tools like powerful type checkers will emerge and become more widely used These tools will exploit ideas from various analysis disciplines Tools will alleviate the “chicken-and-egg” problem of writing specifications

74. Further Reading See papers, slides from: http://research.microsoft.com/slam http://research.microsoft.com/~sriram

75. Glossary Model checkingChecking properties by systematic exploration of the state-space of a model. Properties are usually specified as state machines, or using temporal logics Safety propertiesProperties whose violation can be witnessed by a finite run of the system. The most common safety properties are invariants ReachabilitySpecialization of model checking to invariant checking. Properties are specified as invariants. Most common use of model checking. Safety properties can be reduced to reachability. Boolean programs“C”-like programs with only boolean variables. Invariant checking and reachability is decidable for boolean programs. PredicateA Boolean expression over the state-space of the program eg. (x < 5) Predicate abstractionA technique to construct a boolean model from a system using a given set of predicates. Each predicate is represented by a boolean variable in the model. Weakest preconditionThe weakest precondition of a set of states S with respect to a statement T is the largest set of states from which executing T, when terminating, always results in a state in S.