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Compositional Formal Verification using MOCHA

Compositional Formal Verification using MOCHA. PI: Tom Henzinger Student 1: Freddy Mang (game-theoretic methods) Student 2: Ranjit Jhala (probabilistic systems) UC Berkeley. Compositional Methods for Probabilistic Systems. Luca De Alfaro Thomas A. Henzinger Ranjit Jhala UC Berkeley.

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Compositional Formal Verification using MOCHA

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  1. Compositional Formal Verification using MOCHA PI: Tom Henzinger Student 1: Freddy Mang (game-theoretic methods) Student 2: Ranjit Jhala (probabilistic systems) UC Berkeley

  2. Compositional Methods forProbabilistic Systems Luca De Alfaro Thomas A. Henzinger Ranjit Jhala UC Berkeley

  3. Introduction • A lot of work on making model checking a viable industrial tool • Symbolic Model Checking • Assume-Guarantee based “Compositional” Reasoning • The work has focused on systems that can be modelled accurately using non-determinism • Loss of information: Many systems cannot be appropriately modelled e.g. Communication Protocols, Embedded Components • Goal: To extend MOCHA to model and verify systems with probabilistic behavior • Assume-Guarantee style reasoning for such systems Compositional Methods for Probababilistic Systems

  4. Compositional Models • System Model is Compositional if: • Systems can be composed to obtain more complex systems • System properties can be decomposed into component properties • For non-deterministic systems, the trace-based or linear-time view • Advantages: • Refinement is simply trace containment • Assume-Guarantee rule to decompose refinement proof • Simulation as algorithmically checkable sufficient criterion for checking refinement • We conservatively generalise the trace-based view to systems with both non-deterministic and probabilistic choice • Our approach inherits the advantages mentioned above Compositional Methods for Probababilistic Systems

  5. The Linear-time (Trace-based) View • Given a set of variables X: • X-state: A valuation of the variables in X • X-trace: A sequence of X-states • X-language: A set of X-traces • Given a system P with variables X, its semantics |[ P ]| is an X-language • Refinement corresponds to trace inclusion: • P ¹ Q if |[ P ]| µ |[ Q ]| Compositional Methods for Probababilistic Systems

  6. Benefits of Linear-time View • Parallel composition corresponds to set intersection: • |[ P k Q ]| = |[ P ]| Å |[ Q ]| • Assume-Guarantee rule to decompose refinement checks [Abadi & Lamport 94, Alur & Henzinger 99, McMillan 97] • To show P1k P2¹ Q1k Q2 it suffices to check P1k Q2¹ Q1 and Q1k P2¹ Q2 • Simulation is an algorithmically efficient sufficient condition for refinement Compositional Methods for Probababilistic Systems

  7. Probabilistic Systems • We wish to model transition systems that can make both Probabilistic and Non-deterministic choice • At a state, the system does the following: • Picks one of several available distributions (or moves) over next state non-deterministically • Picks a next state out randomly out of the chosen distribution Compositional Methods for Probababilistic Systems

  8. Related Work • A large body of work on the modelling and verification of probabilistic systems • Vardi 1985, Courcoubetis & Yannakakis 1989 • Basic Model : Markov Decision Processes • Defining the behaviour using schedulers • Several complicated “branching-time” models based on Process Algebras: [JL91], [LS90] • Models based on I/O Automata by Segala [Segala95] • Semantics described as Trace Distributions • Refinement as trace distribution inclusion • Our contribution: • First simple “linear-time” style model with compositional semantics that allow Assume-Guarantee reasoning • Generalize traces to bundles, demonstrate that many of the properties of linear-time models generalize to systems with probabilistic choice Compositional Methods for Probababilistic Systems

  9. Prob. Systems: Example • There are 2 possible behaviours arising from the non-deterministic choice at • ¼ , ¾ • ½ , ½ ½ ½ ¼ ¾ Compositional Methods for Probababilistic Systems

  10. Semantics of Probabilistic Systems • Given a set of variables X: • X-state: A valuation of the variables in X • X-Move: A distribution over X-states • X-trace: A sequence of X-states • X-bundle: A distribution over X-traces • X-Probabilistic Language: A set of X-bundles • Given a Probabilistic system P with variables X, its semantics |[ P ]| is an X-Probabilistic language • Refinement corresponds to bundle inclusion: • P ¹ Q if |[ P ]| µ |[ Q ]| Compositional Methods for Probababilistic Systems

  11. Semantics: dealing with choices • Non-deterministic, Probabilistic choice are “orthogonal” • Factor out non-determinism using schedulers [Derman70, Vardi 1985, Courcoubetis & Yannakakis 1989] • Given a scheduler, the execution is fully probabilistic • Outcome: A sequence of bundles of length i, 8 i > 0 • Semantics: Sum of the outcomes for all the different schedulers Compositional Methods for Probababilistic Systems

  12. ½ : , ½ : • ½ : , ½ : • ½ : , ½ : • ½ : , ½ : Schedulers: Example 1/2 1/2 4 Possible Schedulers, one outcome (bundle) for each Schedulers Outcomes (Bundles) Compositional Methods for Probababilistic Systems

  13. 1/2 1/2 Non-Det. Choice Vs Prob. Choice A B • Non-deterministic choice is more flexible than probabilistic choice • We want A ¹ B, but … Bundles of A Bundles of B ½ , ½ 1 1 1 Compositional Methods for Probababilistic Systems

  14. e , 1-e Non-Det. Choice Vs Prob. Choice 1/2 1/2 A B • Solution: Let the Scheduler be randomized • The scheduler of B can flip a coin to decide which nondeterministic choice to pick • The move of B is then the convex combination of its simple moves Bundles of B: For every e2 [0,1] In particular e= ½ matches A’s bundle Compositional Methods for Probababilistic Systems

  15. Concrete Model: Probabilistic Modules • Based on Reactive Modules [AH99] • State based model, each state corresponds to a valuation of the variables of the system • Probabilities enter in the update values of the variables • Module is made up of a set of Atoms • Each atom controls a set of variables • Atom: A set of guarded commands • At a state, out of the guards that are true (non-det choice) the system picks one command and updates variables using the distribution over next values of the command Compositional Methods for Probababilistic Systems

  16. Probabilistic Modules Transitions & Actions: Given X, Y, two sets of variables • Probabilistic Transition from X to Y is a pair (s,m) : X-state £ Y-move • Probabilistic Action from X to Y : A set of Probabilistic Transitions Atoms: • Atom A, has variables readX(A), ctrX(A) • A probabilistic Initial Action: initF(A) from ? to ctrX(A) • A probabilistic Update Action: updateF(A) from readX(A) to ctrX(A) Compositional Methods for Probababilistic Systems

  17. Module A Interface x,y External z Atom Ax controls x Init [] true-> ½ x:=0 ½ x:=1 Update [] true-> x’:= x [] y ->¼ x’:=:z ¾ x’= z Atom Ay controls y Init [] true-> y:=0 [] true-> y:=1 Update [] true-> y’:= z Probabilistic Modules Modules: • Declaration: 3 sets of variables extlX, intfX, privX • The observable variables or obsX = intfX [ extlX • Body: Finite set of Atoms, s.t. { ctrX(A) | A 2 Atoms } partitions intfX [ privX Compositional Methods for Probababilistic Systems

  18. Operations: Parallel Composition P1, P2 may be composed only if they have the same observables Result: P1k P2 where: • privX(P1k P2) = privX(P1) [ privX(P2) • intfX(P1k P2) = intfX(P1) [ intfX(P2) • extlX(P1k P2) = extlX(P1) [ extlX(P2) n intfX(P1k P2) • Atoms(P1k P2) = Atoms(P1) [ Atoms(P2) Compositional Methods for Probababilistic Systems

  19. Semantics: Schedulers & Outcomes Scheduler A scheduler s from X to Y: X-traces a Y-moves Outcome Given a scheduler s from X to X, Outcome(s) is the set of bundles bi where: bi(t) = bi-1(t(1)Lt(i-1)) £s(t(1)Lt(i-1))(t(i)) b0 = The “empty” bundle Compositional Methods for Probababilistic Systems

  20. Semantics: Atomic Schedulers Schedulers of a Module: • Based on the schedulers of each Atom Atom Schedulers: atomå(A) = set of all schedulers s from readX(A) to ctrX(A) s.t • (¢, s(e)) 2 initF(A) • (t(n),s(t)) 2 updateF(A) for all readX(A)-Traces t of length n Composing Atom Schedulers: For schedulers s1 from X1 to Y1, s2 from X2 to Y2, s.t. Y1Å Y2 = ? (s1£s2) : from X1[ X2 to Y1[ Y2 s.t. (s1£s2)(t) = s1(t[X1]) £s2(t[X2]) Compositional Methods for Probababilistic Systems

  21. Module Semantics Schedulers of P • extlå(P) = set of all schedulers from extlX(P) [ intfX(P) to extlX(P) • modå(P) = extlå(P) £PA 2 Atoms(P) atomå(A) Language of P • L(P) = [s2 modå(P) Outcome(s) Trace Semantics of P • |[ P ]| = L(P) Compositional Methods for Probababilistic Systems

  22. Module B Interface x,y Atom Axy controls x,y Init [] true-> x,y:=0,0 [] true-> x,y:=0,1 [] true-> x,y:=1,0 [] true-> x,y:=1,1 Atom Bx controls x Init [] true-> x:=0 [] true-> x:=1 Update [] . . . Atom By controls y Init [] true-> y:=0 [] true-> y:=1 Update [] . . . The Importance of Atoms Module A Interface x,y • A ± B because: • A has a bundle where x,y have correlated values { ½: 0,0 ½: 1,1} • In B’s bundle it is not possible to get correlation, despite complete non-det in each atom, as the schedulers are independent Compositional Methods for Probababilistic Systems

  23. Module Q Intf q Extl p Priv q_ Module P Intf p Extl q Priv p_ Atom Qatom controls q,q_ Init [] true-> ½ q,q_:=0,0 ½ q,q_:=0,1 Update [] true-> q’,q_’:= q_,q_ Atom Patom controls p,p_ Init [] true-> ½ p,p_:=0,0 ½ p,p_:=0,1 Update [] true-> p’,p_’:= p_,p_ Why Visibility Restrictions ? • Motivated by need to restrict the power of the environment • Environment must not be able to read Private variables • If the environment could then both P and Q could have a bundle: • { ½ pq = 00 ! 00, ½ pq =00 ! 11} • P k Q can have no such bundle • Thus semantics would not be compositional Compositional Methods for Probababilistic Systems

  24. Compositional Semantics Theorem: [Semantics of Parallel Composition] |[ P1k P2 ]| = |[ P1 ]| Å |[ P2 ]| • The behaviours of P1k P2 is the intersection of the behaviours of P1 and P2 Compositional Methods for Probababilistic Systems

  25. Refinement Between Modules Module Refinement P ¹ Q if: • intfX(P) ¶ intfX(Q) and extlX(P) ¶ extlX(Q) • |[ P ]| µ |[ Q ]| Compositional Methods for Probababilistic Systems

  26. Refinement Is Compositional Theorem: Refinement is Compositional • P k Q ¹ P • If P ¹ Q , then P k R ¹ Q k R Theorem: Assume-Guarantee If P1k Q2¹ Q1 and Q1k P2¹ Q2, then P1k P2¹ Q1k Q2 Compositional Methods for Probababilistic Systems

  27. Checking Refinement • Sufficient condition for bundle inclusion: • Probabilistic Simulation [JL91, SL95] suffices for two closed systems each with a single atom • We modify this relation to extend it to our setting (where there are visibility restrictions) • We use an algorithm based on that of [BEM99] to check atomic Simulation • This approach makes the decomposition of the proof mandatory Compositional Methods for Probababilistic Systems

  28. Simulation: Example ¼ ½ ½ ½ ¼ A B • The three states of B match the two states of A • The probabilities are distributed over the states • Each state of B “mimics” the state of A depending on how much the state of A’s weight is given to the state of B Compositional Methods for Probababilistic Systems

  29. Bundle Inclusion but not Simulation ½ ½ ½ ½ • Difficulty of computing bundle inclusion: • A distribution of states of one system is equivalent to a distribution of states of the other • Schedulers look at histories – can look at entire trace • Modularity brings some problems – thus the standard simulation does not work Compositional Methods for Probababilistic Systems

  30. Current Work • Algorithm to check Bundle Inclusion exactly • Implementation of this work – extending MOCHA to handle probabilistic systems • Case Studies: • Communication Protocols with probabilistic behaviour • Embedded Components with probabilistic environments • Logics for Specification: • Correctness and performance properties • Compositional reasoning Compositional Methods for Probababilistic Systems

  31. References • M. Abadi & L. Lamport 1994: • The existence of Refinement Mappings, TOPLAS • R. Alur & T. A. Henzinger 1999: • Reactive Modules, Formal Methods in System Design 1999 • K. L. McMillan 1999: • A Compositional Rule for Hardware Design Refinement, CAV97 • Derman 1970: • Markov Decision Processes • M. Vardi 1985: • Automatic Verif. of Probabilistic Concurrent Finite-State Programs, FOCS 85 • C. Courcoubetis & M. Yannakakis: • The Complexity of Probabilistic Verification, JACM 1995 • [BEM 99] C. Baier & B. Engelen & C. Majster-Paderborn: • Deciding Bisimilarity and Similarity for Probabilistic Processes, JCSS 1999 • [JL91] B. Jonsson & K. Larsen • Specification and Refinement of Probabilistic Processes, LICS 1991 Compositional Methods for Probababilistic Systems

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