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Evidence-Based Verification. Li Tan Computer Science Department Stony Brook April 2002. Outline. Part I: Evidence-based verification. Motivations. The general framework. Applications. Part II: Evidence-based model checking Checker-independent evidence for model checking.
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Evidence-Based Verification Li Tan Computer Science Department Stony Brook April 2002 Evidence-Based Verification
Outline • Part I: Evidence-based verification. • Motivations. • The general framework. • Applications. • Part II: Evidence-based model checking • Checker-independent evidence for model checking. • Extracting the evidence from existing model checkers. • Post-model-checking analyses based on the evidence. • Efficiently certifying model-checking Result. • Constructing winning strategy for model-checking game. • Evaluating the quality of model-checking process. • A prototype on the Concurrency Workbench (CWB-NC). Evidence-Based Verification
Verification • Automatic verification: whether or not a transition system satisfies a property. • Successful Applications (in Stony Brook alone): Checking communication protocol, Mechanical Design, Medical Device, Anti-Block Braking System, etc. • Verification algorithm (Checker) works as a decision procedure for the problem. • "Yes/No" may not satisfy users. • Why does my design go wrong? • Could my design satisfy property trivially? • Can I trust the verification result? Evidence-Based Verification
Problems with Traditional Diagnostic Generation Diagnosis is about understanding the result, • A diagnostic routine may, • Perform its own reasoning. • Reuse the proof already computed by a checker. • Diagnostic routine is tightly geared to the structure of checkers. • Implementation requires the understanding of checkers. • Migrating a diag. routine onto another checker often requires major changes on both diag. routine and checker. • Proof used for one diagnostic schema may not be used for a different schema. • No additional checking on verificaton result. Evidence-Based Verification
Invalid Proof Portable Proof of Correctness Evidence-Based Verification Let the result carry its own proof Diagnostic Schema 1 Diagnostic Schema 2 Diagnostic Schema m … Verifier Checker 1 Checker 2 Checker n … Evidence-Based Verification
The General Framework • Defining Abstract Proof Structures(APS) as portable evidence. • APS encodes the proof structures of different checkers in a standard form. • APS carries the evidence to justify the result. • Extracting APS from existing checkers. • Utilizing APS to perform diagnoses. • Certifying verification result. • Generating diagnostic information. • Evaluating the quality of verification process. Evidence-Based Verification
Requirements • APS can be extracted from existing checkers. • The extraction should not affect the complexities of checkers. • The consistency of APS should be verified efficiently. • The time and space complexities of certifying APS should not exceed the complexities of checkers producing them. • A variety of diagnoses can be performed using APS. • APS should be defined for three major approaches for verification: model checking, equivalence checking, and preordering checking. Evidence-Based Verification
Evidence Model Checking: a Sub-framework Background of model checking. T²f • T is modeled as a Kripke structure T=<S, sI, !,V> • S is the set of states with the starting state sI2 S. • !µ S £ S is the transition relation. • V: A! 2S is an evaluation for atomic propositions. • f is encoded in some temporal logic. • CTL AG(a ) AF b) • Model-checking problem can be encoded as a Boolean equation system Evidence-Based Verification
Fixpoint Equation System: Syntax Given a set of variables X and a complete lattice {H, <}, • si2 {m, n} is a (least, greatest) fixpoint operator. • fi: HX!H is monotonic. • q2 HX is an environment for E. • {HX, ½}is a complete lattice. • q[X/h] maps X 2X to h 2 H. • Denote E (k) for the tail of E starting from kth equation. Evidence-Based Verification
Equation System: Semantics [E]: HX!HX is a function on environments Evidence-Based Verification
Boolean (Fixpoint) Equation System • Syntax, • H={ {0, 1},< } is the Boolean lattice H. • q2 2X can be viewed as a set. • E is closed if X 2Xi also appears as a left side variable. • [E](q1)=[E](q2) for any q1, q22 HX. • Denote [E] for [E](q) • [E](X) assigns X a Boolean value. Evidence-Based Verification
Model Checking via BES • BES E= Kripke structure T+ Property F • E is closed. • A variable X in BES stands for $h s, f’ i$. • [E](X)=1 iff s ²Tf. • Many checkers (implicitly) construct BESs. • For m-calculus checker, BES=T+m-calculus. • For automaton-based checker, BES= parity automaton. • E can be constructed on-the-fly. Evidence-Based Verification
Evaluating Equation System: an Example Evidence-Based Verification
Support Set Evidence-Based Verification
Support Set (Continue) • By (a) and (b), support set implies a fixpoint solution for E. • By (c), support set respects the definition of least/or greatest fixpoints. • If r=1, no bad loop on . • If r=0, no good loop on . Theorem 1 [TanCle02] Let G=<r, X, X> be a support set for E, then [E](X)=r. Evidence-Based Verification
Extracting Support Set The extraction is, • practical. Support sets can be extracted from a wide range of existing checkers, • Boolean-Graph algorithm [And92], Linear Alternation-Free algorithms[CleSte91], On-the-fly algorithms for full m-calculus LAFP [LRS98] and SLP [TanCle02b], Automaton-based model checkers([BhaCle96a] and [KVW00]). • efficient. The overhead doesn't exceed the original complexities of these checkers. • simply. It only need have dependency relations recorded. Evidence-Based Verification
Application I: Certifying model-checking results • Checking (a) and (b) can be done in linear time. • Checking (c) can be reduced to even-loop problem (a nlogn problem[KKV01]). • Model checking is a NP Å co-NP problem [EmeJutSis93]. • The cost of certifying results < The cost of model checking. Evidence-Based Verification
Application II: model-checking game • Semantics: decide [E](X0) for E • Two players: I (asserting [E](X0)=0) and II (asserting [E](X0)=1) • A play is a sequence a=Xp0 Xp1…such that Xp0=X0 and if, • (spi Xpi=ÇX ’) 2E, then II chooses Xpi+12X' • (spi Xpi=ÆX ’) 2E, then I chooses Xpi+12X ’ • II wins a iff, • It's I's turn but I has no choice (X '=;), or, • The shallowest variable being visited infinitely often by a is a n-variable. Evidence-Based Verification
MC Game as a Diagnostic Routine • MC game is a fair game. • ([E])(X0)=1 ) II has a winning strategy. • ([E])(X0)=0 ) I has a winning strategy. • Two physical players: computer and user. • When the model-checking result is, • Yes ) The computer plays as II while the user as I. • No ) The computer plays as I while the user as II. • The user is always a loser if the MC result is correct and the computer uses the right strategy. Evidence-Based Verification
Constructing Winning Strategy for Computer • Given h r, X0, Xi as a support set for E • The computer will keep the play a=Xp0 Xp1… proceeding within support set: • If r=1 and spi Xpi=ÇX ’, then the computer (as II) chooses Xpi+12 (X(Xpi) ÅX '). • If r=0 and spi Xpi=ÆX ’, then the computer (as I) chooses Xpi+12 (X(Xpi) ÅX '). • The strategy is feasible: X(Xpi) is defined whenever Xpi is the computer’s turn. • The strategy is a winning strategy for the computer. Evidence-Based Verification
Evaluating Equation System: an Example Evidence-Based Verification
Application III:Evaluate the quality of MC • A positive result may hide the problem • T may pass AG(a ) AF b) trivially because a never occurs in T. • Is there the status of a state (Minicoverage [CKV01]) or a subformula (Vacuity [KV99]) irrelevant to the result? • Coverage problem of support set. • Has support set covered all the states and properties? Evidence-Based Verification
A Prototype on CWB-NC Evidence-Based Verification
Conclusion Checkers produce abstract proof structures as evidence. • APS is independent of checker. • Extracting APS won't affect the complexities of checkers. • APS justifies the correctness of result. • APs attests to the quality of verification. • A wide range of diagnostic information can be built on this evidence. • APSs are defined for Model checking, Equiv. checking, and Preordering Checking. Evidence-Based Verification