1. Model Checking Java Programs David Park, Ulrich Stern, Jens Skakkebaek, and David L. Dill Stanford University

2. 2 Introduction Can model checking be usefully applied to programs? Model checking background Model checking & software A Java model checker Research directions

3. Background on model checking

4. 4 Model Checking “Model checking” analyzes the reachable state space of a system for certain properties. Analysis may –enumerate states and –may also look for paths (e.g., unfair cycles) State set representation can be –explicit (e.g., hash table of states) [E.g., SPIN, Mur  ] –symbolic (e.g., a Boolean function represented as a Boolean decision diagram [BDD]) [SMV, nuSMV, VIS]

5. 5 Explicit-state vs. symbolic Explicit state model checking has several advantages: More predictable (hard to diagnose reasons for BDD blowup). Avoid difficulties of translating everything to Boolean functions. Easier to deal with dynamic features of software (e.g. heap-allocated objects).

6. 6 Trivial Mur  example One state variable, two rules: count > 0 --> count := count - 1; count count := count + 1; 0 12310...

7. 7 Basic explicit model checking On-the-fly search procedure Initialize: Queue is empty; Table is empty; push s0 onto Queue; Loop: while Queue not empty do remove s from Queue; if s is NOT already in Table then enter s in Table; for all enabled rules r at s do s’ = succ(s,r); insert s’ into Queue;

8. 8 “Sweet spot” for model checking Designs that are control-dominated and nondeterministic (Nondeterminism stems from concurrency and environmental behavior) –Designers can’t foresee all cases and interactions –Directed or random testing gets poor coverage (there aren’t even good coverage metrics) –Simple static analysis methods either miss too many bugs or report too many false errors. –Even running a prototype results in infrequent, unrepeatable, hard-to-diagnose failures.

9. 9 Perspectives on model checking As a testing method –Model checking is more expensive and more thorough –“Tests” generated automatically –States are saved to avoid redundant work As a static analysis method –Model checking enumerates paths more precisely than traditional static analysis –Hence, it is more accurate and more costly.

10. 10 The three big problems Computational complexity (e.g., the state explosion problem) Finding properties to check Describing/constraining environment

11. Model checking of software

12. 12 The Key Requirement A new verification technique will not be adopted unless the benefits outweigh the costs.

13. 13 Special problems of software Bugs are less expensive in software than hardware or protocols –Field upgrades are often relatively inexpensive –Dynamic structures –Heap –Recursion –Dynamically allocated threads Large state spaces Complex environment –OS –Hardware –User

14. 14 Bugs are inexpensive Cost of verification must be minimized –Verifying code instead of high-level specs reduces specification burden –Checking implicit properties (e.g., deadlock) reduces cost Other costs must be displaced (e.g., manual testing) Target applications that have relatively high cost –Safety critical –Embedded real-time systems –Other cases: security?

15. 15 Dynamic stuff Heap Recursion Dynamically allocated Threads Eliminate artificial limitations of existing model checkers –Allow dynamic arrays –No a priori limit on state space, but that’s ok.

16. 16 Large state spaces –Software complexity is not highly constrained by physical resources Target embedded applications, which are somewhat resource-constrained (but less so each day). Use available model checking optimizations

17. 17 Complex environment OS Hardware User Hope that detailed constraints aren’t needed Bite the bullet and write specifications

18. A Java model checker

19. 19 Java Model checking Why Java? Lots of interest Well-defined thread model Possibly to be used for embedded real-time applications in future.

20. 20 Value of model checking for Java Concurrency problems still very hard to test and debug –Nondeterminism from scheduling –Seemingly reliable applications may break when on new hardware, JVM, or under different scheduling load. –Unpredictable, non-repeatable failures. Other sources of nondeterminism –Interactions with user, system calls

21. 21 Status of project Mostly an integration of existing ideas Prototype is implemented Implements a large subset of Java including most advanced features of Java. –Inheritance, overriding, overloading, exception handling Can deal with small programs written by others –Can’t deal with native code in libraries, etc.

22. 22 Properties checked Goal: Keep specification simple –Check properties that don’t have to be explicitly specified –Programmers are comfortable with in-line assertions. Checker looks for –deadlock –assertion failures –selected exceptions: array bounds, run-time type errors.... more coming soon.

23. 23 Translation strategy Translate statements to SAL guarded commands –Nondeterminism of guarded commands used to model scheduler, possible results of API calls. Implement JVM run-time in SAL –Heap, stack, are implemented as dynamic arrays –Classes, stack frames implemented as records

24. 24 Processing steps Java Byte-code Java Byte-code Jimple SAL Level 1 SAL Level 1 SAL Level 0 SAL Level 0 C++ Model Checker Model Checker Java Program Error Trace

25. 25 Java to SAL translation –Jimple statements SAL guarded commands Example: i0 = 1 is translated into (PC[TID] = label_0) --> next(Stack)[TID][SP].localVariables.i0 = 1; next(PC)[TID] = label_1; where PC : program counter TID : thread identifier of current thread label_0 : SAL label of the statement i0 = 1 SP : stack pointer

26. 26 Optimization: Atomic Blocks Idea (Bruening, 1999): execute large blocks of code without interleaving at the statement level. –Don’t need to save or copy intermediate states (just save state at end of block). –Avoid state explosion from fine-grained interleaving. vs.

27. 27 Atomic Blocks Assumption: all accesses to shared variables are locked. –This can and should be checked during verification using same method as in Eraser. Blocks are broken immediately after “unlock” events. –Not necessary to break at “lock”. There may be multiple locks and lots of other statements in the block. –This would miss deadlocks, –... but there is a more sophisticated deadlock check based on circular wait conditions that catches all of them. If a block fails to acquire a lock, it is aborted

28. 28 Atomic Blocks Model checker does the optimization on-the-fly –execution continues until unlock. Then state is saved and other threads can be executed. This is a special form of persistent set reduction (Wolper and Godefroid).

29. 29 Savings from atomic blocks CS193K ReaderWriter CS193K TurnDemo NASA’s Classic NASA’s Ksu_pipe 261,838 528 26,145 166 45,924 143 3,990,883 4991 1,030,130 1,356 68,715 236 118,047 234 14,022,723 15,762 1.0 496 1.0 158 1.0 321 1.0 800 442s 1.54s 30.4s 2.40s 46.6s 2.11s 6401s 11.8s ExampleBlocks?StatesRulesTimeReduction NYNY NYNY NYNY NYNY

30. 30 Optimization: Hash Compaction Idea: Instead of saving (large) states in state table, store (small) signatures (Wolper&Leroy, Stern&Dill). Tradeoff: May result in missed errors because state search falsely thinks it has seen a state before. Probability of missed error can be bounded –5-byte signatures, 80 million states: P(omission) < 0.13%. Outcomes: –Error found (guaranteed correct) –Ran out of space, no errors (inconclusive) –Searched all states, no errors (almost guaranteed correct).

31. 31 Related work Eraser [Savage et al., 1997] –Checks unlocked variables, but doesn’t replace test generation. Verisoft [Godefroid, 1996], Rivet [Bruening, 1999], Stoller 2000 –Systematically exercises design, but doesn’t check previously visited states (may do redundant work) Java PathFinder (NASA) –Similar goals, different optimizations, no SAL, no C++ dSPIN - dynamic data structures in SPIN (but no special optimizations)

32. The Future

33. 33 The three big problems (again) Computational complexity (e.g., the state explosion problem) Finding properties to check Describing/constraining environment More research is needed on all of these problems, in addition to integrating existing techniques.

34. 34 Computational complexity This problem requires an assault from many directions. Reduce the problem before model checking –Slicing based on property being checked. –Data and control abstraction. –E.g., Bandera system. Additional model checker optimizations –Better persistent set reductions –Heap-based optimization symmetry early garbage collection Partial verification –Provide guidance to most interesting parts of state space

35. 35 Finding properties to check Race detection –“Eraser” model - but requiring Java locks on all shared variables is neither necessary nor sufficient Misses higher-level locking constructs Misses higher-level atomicity requirements Possibilities of new models: lock ordering, “happens before” Check wider range of exceptions Specify and check requirements of standard libraries

36. 36 Environmental specification Write more robust programs –Detect and report bad environment behavior –Code to do this can be used by verifier to exclude false errors Specify reusable constraints for common cases (e.g., standard libraries) Slicing and abstraction can immunize verification from irrelevant environment problems. Better solutions are needed.

37. 37 Model checking & static analysis How can static analysis help with the previous problems? Should model checking be integrated with (or absorbed into) static analysis?

38. 38 Feasibility of model extraction Work with Dawson Engler and David Lie of Stanford FLASH multiprocessor cache coherence protocols implemented in C –Weird resource constraints –Very hard to debug when it crashes –10-30K lines of code –Code has been worked over very thoroughly

39. 39 Feasibility of model extraction Used xg++ compiler to extract a Mur  model –xg++ allows user to write state machines that traverse C++ data flow graph –Identifies messages, protocol state transitions –Ignores everything else (ad hoc slicing) –Method is specific to these protocols –Method is neither sound nor complete 9 Bugs found

40. 40 Web page http://verify.stanford.edu