1. Modeling Data in Formal Verification Bits, Bit Vectors, or Words http://www.cs.cmu.edu/~bryant Randal E. Bryant Carnegie Mellon University

2. – 2 – Overview Issue How should data be modeled in logical analysis? Verification, synthesis, test generation, security analysis, …Approaches Bits: Every bit is represented individually Words: View each word as arbitrary value E.g., unbounded integers Historic program verification work Bit Vectors: Finite precision words Captures true semantics of hardware and software

3. – 3 – Data Path Com. Log. 1 Com. Log. 2 Bit-Level Modeling Represent Every Bit of State Individually Behavior expressed as Boolean next-state over current state Historic method for most CAD, testing, and verification tools E.g., model checkers, test generators Control Logic

4. – 4 – Bit-Level Modeling in Practice Strengths Allows precise modeling of system Well developed technology BDDs & SAT for Boolean reasoningLimitations Every state bit introduces two Boolean variables Current state & next state Overly detailed modeling of system functions Don’t want to capture full details of FPU Making It Work Use extensive abstraction to reduce bit count Hard to abstract functionality

5. – 5 – Word-Level Abstraction #1: Bits → Integers View Data as Symbolic Words Arbitrary integers No assumptions about size or encoding Classic model for reasoning about software Can store in memories & registers x0x0 x1x1 x2x2 x n-1 x 

6. – 6 – Data Path Com. Log. 1 Com. Log. 2 Abstracting Data Bits Control Logic Data Path Com. Log. 1 Com. Log. 1 ?? What do we do about logic functions?

7. – 7 – Word-Level Abstraction #2: Uninterpreted Functions For any Block that Transforms or Evaluates Data: Replace with generic, unspecified function Only assumed property is functional consistency: a = x  b = y  f (a, b) = f (x, y) ALUALU f

8. – 8 – Abstracting Functions For Any Block that Transforms Data: Replace by uninterpreted function Ignore detailed functionality Conservative approximation of actual system Data Path Control Logic Com. Log. 1 Com. Log. 1 F1F1 F2F2

9. – 9 – Word-Level Modeling: History Historic Used by theorem provers More Recently Burch & Dill, CAV ’94 Verify that pipelined processor has same behavior as unpipelined reference model Use word-level abstractions of data paths and memories Use decision procedure to determine equivalence Bryant, Lahiri, Seshia, CAV ’02 UCLID verifier Tool for describing & verifying systems at word level

10. – 10 – Pipeline Verification Example Pipelined Processor Reference Model

11. – 11 – Abstracted Pipeline Verification Pipelined Processor Reference Model

12. – 12 – Experience with Word-Level Modeling Powerful Abstraction Tool Allows focus on control of large-scale system Can model systems with very large memories Hard to Generate Abstract Model Hand-generated: how to validate? Automatic abstraction: limited success Andraus & Sakallah, DAC 2004 Realistic Features Break Abstraction E.g., Set ALU function to A+0 to pass operand to outputDesire Should be able to mix detailed bit-level representation with abstracted word-level representation

13. – 13 – Bit Vectors: Motivating Example #1 Do these functions produce identical results? Strategy Represent and reason about bit-level program behavior Specific to machine word size, integer representations, and operations int abs(int x) { int mask = x>>31; return (x ^ mask) + ~mask + 1; } int test_abs(int x) { return (x < 0) ? -x : x; }

14. – 14 – Motivating Example #3 Is there a way to expand the program sketch to make it match the spec? bit[W] popSpec(bit[W] x) { int cnt = 0; for (int i=0; i<W; i++) { if (x[i]) cnt++; } return cnt; } Answer W=16: [Solar-Lezama, et al., ASPLOS ‘06] bit[W] popSketch(bit[W] x) { loop (??) { x = (x&??) + ((x>>??)&??); } return x; } x = (x&0x5555) + ((x>>1)&0x5555); x = (x&0x3333) + ((x>>2)&0x3333); x = (x&0x0077) + ((x>>8)&0x0077); x = (x&0x000f) + ((x>>4)&0x000f);

15. – 15 – Motivating Example #4 Is pipelined microprocessor identical to sequential reference model? Strategy Represent machine instructions, data, and state as bit vectors Compatible with hardware description language representation Verifier finds abstractions automatically Pipelined Microprocessor Sequential Reference Model

16. – 16 – Bit Vector Formulas Fixed width data words Arithmetic operations Add/subtract/multiply/divide, … Two’s complement, unsigned, … Bit-wise logical operations Bitwise and/or/xor, shift/extract, concatenatePredicates ==, <=Task Is formula satisfiable? E.g., a > 0 && a*a < 0 50000 * 50000 = -1794967296 (on 32-bit machine)

17. – 17 – Decision Procedures Core technology for formal reasoning Boolean SAT Pure Boolean formula SAT Modulo Theories (SMT) Support additional logic fragments Example theories Linear arithmetic over reals or integers Functions with equality Bit vectors Combinations of theories Formula Decision Procedure Satisfying solution Unsatisfiable (+ proof)

18. – 18 – Recent Progress in SAT Solving

19. – 19 – BV Decision Procedures: Some History B.C. (Before Chaff) String operations (concatenate, field extraction) Linear arithmetic with bounds checking Modular arithmeticLimitations Cannot handle full range of bit-vector operations

20. – 20 – BV Decision Procedures: Using SAT SAT-Based “Bit Blasting” Generate Boolean circuit based on bit-level behavior of operations Convert to Conjunctive Normal Form (CNF) and check with best available SAT checker Handles arbitrary operations Effective in Many Applications CBMC [Clarke, Kroening, Lerda, TACAS ’04] Microsoft Cogent + SLAM [Cook, Kroening, Sharygina, CAV ’05] CVC-Lite [Dill, Barrett, Ganesh], Yices [deMoura, et al]

21. – 21 – Bit-Vector Challenge Is there a better way than bit blasting? Requirements Provide same functionality as with bit blasting Find abstractions based on word-level structure Improve on performance of bit blastingObservation Must have bit blasting at core Only approach that covers full functionality Want to exploit special cases Formula satisfied by small values Simple algebraic properties imply unsatisfiability Small unsatisfiable core Solvable by modular arithmetic …

22. – 22 – Some Recent Ideas Iterative Approximation UCLID: Bryant, Kroening, Ouaknine, Seshia, Strichman, Brady, TACAS ’07 Use bit blasting as core technique Apply to simplified versions of formula Successive approximations until solve or show unsatisfiable Using Modular Arithmetic STP: Ganesh & Dill, CAV ’07 Algebraic techniques to solve special case forms Layered Approach MathSat: Bruttomesso, Cimatti, Franzen, Griggio, Hanna, Nadel, Palti, Sebastiani, CAV ’07 Use successively more detailed solvers

23. – 23 – Iterative Approach Background: Approximating Formula Example Approximation Techniques Underapproximating Restrict word-level variables to smaller ranges of values Overapproximating Replace subformula with Boolean variable  Original Formula   +  + Overapproximation ++ More solutions: If unsatisfiable, then so is  Underapproximation  −   −− Fewer solutions: Satisfying solution also satisfies 

24. – 24 – Starting Iterations Initial Underapproximation (Greatly) restrict ranges of word-level variables Intuition: Satisfiable formula often has small-domain solution  1−1−

25. – 25 – First Half of Iteration SAT Result for SAT Result for  1 − Satisfiable Then have found solution for  Unsatisfiable Use UNSAT proof to generate overapproximation  1 + (Described later)  1−1− If SAT, then done 1+1+ UNSAT proof: generate overapproximation

26. – 26 – Second Half of Iteration SAT Result for SAT Result for  1 + Unsatisfiable Then have shown  unsatisfiable Satisfiable Solution indicates variable ranges that must be expanded Generate refined underapproximation  1−1− If UNSAT, then done 1+1+ SAT: Use solution to generate refined underapproximation 2−2−

27. – 27 – Iterative Behavior Underapproximations Successively more precise abstractions of  Allow wider variable rangesOverapproximations No predictable relation UNSAT proof not unique  1−1− 1+1+ 2−2−      k−k− 2+2+ k+k+     

28. – 28 – Overall Effect Soundness Only terminate with solution on underapproximation Only terminate as UNSAT on overapproximationCompleteness Successive underapproximations approach  Finite variable ranges guarantee termination In worst case, get  k −    1−1− 1+1+ 2−2−      k−k− 2+2+ k+k+      SAT UNSAT

29. – 29 – Generating Overapproximation Given Underapproximation  1 − Bit-blasted translation of  1 − into Boolean formula Proof that Boolean formula unsatisfiableGenerate Overapproximation  1 + If  1 + satisfiable, must lead to refined underapproximation Generate  2 − such that  1 −   2 −    1−1− 1+1+ UNSAT proof: generate overapproximation 2−2−

30. – 30 – Bit-Vector Formula Structure DAG representation to allow shared subformulas x + 2 z  1 x % 26 = v w & 0xFFFF = x x = y a 

31. – 31 – Structure of Underapproximation Linear complexity translation to CNF Each word-level variable encoded as set of Boolean variables Additional Boolean variables represent subformula values x + 2 z  1 x % 26 = v w & 0xFFFF = x x = y a −− Range Constraints w x y z Æ

32. – 32 – Range Constraints w x y z Æ UNSAT Proof Subset of clauses that is unsatisfiable Clause variables define portion of DAG Subgraph that cannot be satisfied with given range constraints x + 2 z  1 x % 26 = v w & 0xFFFF = x x = y a Ç Æ Æ Ç Ç:

33. – 33 – Extracting Circuit from UNSAT Proof Subgraph that cannot be satisfied with given range constraints Even when replace rest of graph with unconstrained variables x + 2 z  1 x = y a Æ Æ Ç Ç: b1b1 b2b2 Range Constraints w x y z Æ UNSAT

34. – 34 – Generated Overapproximation Remove range constraints on word-level variables Creates overapproximation Ignores correlations between values of subformulas x + 2 z  1 x = y a Æ Æ Ç Ç: b1b1 b2b2 1+1+

35. – 35 – Refinement Property Claim  1 + has no solutions that satisfy  1 −’s range constraints Because  1 + contains portion of  1 − that was shown to be unsatisfiable under range constraints x + 2 z  1 x = y a Æ Æ Ç Ç: b1b1 b2b2 Range Constraints w x y z Æ UNSAT 1+1+

36. – 36 – Refinement Property (Cont.) Consequence Solving  1 + will expand range of some variables Leading to more exact underapproximation  2 − x + 2 z  1 x = y a Æ Æ Ç Ç: b1b1 b2b2 1+1+

37. – 37 – Effect of Iteration Each Complete Iteration Expands ranges of some word-level variables Creates refined underapproximation  1−1− 1+1+ SAT: Use solution to generate refined underapproximation 2−2− UNSAT proof: generate overapproximation

38. – 38 – Approximation Methods So Far Range constraints Underapproximate by constraining values of word-level variables Subformula elimination Overapproximate by assuming subformula value arbitrary General Requirements Systematic under- and over-approximations Way to connect from one to another Goal: Devise Additional Approximation Strategies

39. – 39 – Function Approximation Example Motivation Multiplication (and division) are difficult cases for SAT : Prohibit Via Additional Range Constraints §: Prohibit Via Additional Range Constraints Gives underapproximation Restricts values of (possibly intermediate) terms : Abstract as f (x,y) §: Abstract as f (x,y) Overapproximate as uninterpreted function f Value constrained only by functional consistency * x y x 01else y 0000 101x 0y§

40. – 40 – Challenges with Iterative Approximation Formulating Overall Strategy Which abstractions to apply, when and where How quickly to relax constraints in iterations Which variables to expand and by how much? Too conservative: Each call to SAT solver incurs cost Too lenient: Devolves to complete bit blasting. Predicting SAT Solver Performance Hard to predict time required by call to SAT solver Will particular abstraction simplify or complicate SAT? Combination Especially Difficult Multiple iterations with unpredictable inner loop

41. – 41 – STP: Linear Equation Solving Ganesh & Dill, CAV ’07 Solve linear equations over integers mod 2 w Capture range of solutions with Boolean Variables Example Problem Variables: 3-bit unsigned integers x: [x 2 x 1 x 0 ]y: [y 2 y 1 y 0 ]z: [z 2 z 1 z 0 ] Linear equations: conjunction of linear constraints General Form A x + b = 0 mod 2 w 3x+ 4y+ 2z= 0mod 8 2x+ 2y+ 2= 0mod 8 2x+ 4y+ 2z= 0mod 8

42. – 42 – Summary: Modeling Levels Bits Limited ability to scale Hard to apply functional abstractionsWords Allows abstracting data while precisely representing control Overlooks finite word-size effects Bit Vectors Realistic semantic model for hardware & software Captures all details of actual operation Detects errors related to overflow and other artifacts of finite representation Can apply abstractions found at word-level

43. – 43 – Areas of Agreement SAT-Based Framework Is Only Logical Choice SAT solvers are good & getting better Want to Automatically Exploit Abstractions Function structure Arithmetic properties E.g., associativity, commutativity Arithmetic reductions E.g., LU decomposition Base Level Should Be SAT Only semantically complete approach

44. – 44 – Choices Optimize for Special Formula Classes E.g., STP optimized for conjunctions of constraints Common in software verification & testing Iterative Abstraction Natural framework for attempting different abstractions Having SAT solver in inner loop makes performance tuning difficultOthers?

45. – 45 – Observations Bit-Vector Modeling Gaining in Popularity Recognition of importance Benchmarks and competitions Just Now Improving on Bit Blasting + SAT Lots More Work to be Done