Exploiting Predicate Structure for Efficient Reachability Detection

Exploiting Predicate Structure for Efficient Reachability Detection Sujatha Kashyap Dr. Vijay K. Garg Parallel and Distributed Systems Laboratory

Outline • Problem Statement ( Motivation) • Notation and Background • Overview of Technique • Experimental Results • comparison with SPIN • Concluding Remarks

Complexity of Model Checking • Explicit state representation [Clarke, Emerson 1981] • Labeled transition systems. • CTL model checking in O(|M|.|f|) (Clarke, Emerson, Sistla 1986) • |M| is very large (state space explosion). • Implicit representation • E.g., BDDs [McMillan 1991]. • Model checking becomes PSPACE-complete in the size of the structure (Feigenbaum et al. 1999) Motivation: To find a happy medium.

Concurrency and Partial Orders • Approaches exploiting the nature of concurrent events: • Partial-order models • Lamport 1978: “happened-before” relation • Mazurkiewicz 1986: “traces” • McMillan 1991: Petri net unfoldings • Partial-order reduction • Valmari 1990: stubborn sets • Peled 1993: ample sets • Godefroid 1996: persistent sets a1 a3 a2 a1 a3 a2 a2 a3 a1 a2 a3 a1

Basic Notation s0 • Program P = (S, T, s0) • S: Finite set of states • T: Finite set of transitions • s0: Initial state • enabled(s)  T • All transitions executable from state s • s’ = α(s) • Only deterministic transitions • Event = occurrence of a transition • Interleaving sequence, w • states(w) δ α γ s3 β s2 s1 w = αβγ states(w) = {s0, s1, s2}

Independence of events [Mazurkiewicz 1986] • An independence relation I  T x T is an antireflexive, symmetric relation such that (α, β)  I iff for all s  S, if α  enabled(s) : • Enabledness: β  enabled(s)  β  enabled(α (s)). • Commutativity: α, β  enabled(s)  α(β(s)) = β(α(s)). • The dependency relation D = (T X T) \ I. s α β s1 s2 β α r Note: We will assume that events belonging to the same process are always dependent.

Trace equivalence D v D w  v can be transformed into w by commuting only adjacent independent events. Example:I = {(b, c), (b, d), (e, f) (b, f)} v = abcdef D acbdef D acdbef D acdbfe D acdfbe= w

Traces s2 s1 f e c d c d f e • D partitions the interleaving sequences of a program P into equivalence classes, called traces. • σE: Trace with event set E. • States(σ) = v  σstates(v). d c b a a b s0 {abcde, abced, abdcf, abdfc, bacde, baced, badcf, badfc} I = {(a,b), (c,f), (d,e)} σ1 = {abcde, abced, bacde, baced} σ2 = {abdcf, abdfc, badcf, badfc}

Traces as Partial Orders • A trace corresponds to a partial order. s2 s1 e d c f f e c d  c d d f e c d c b a a b a b σ1 σ2 a b s0 State  order ideal (down-set) σ1 ={abcde, abced, bacde, baced} σ2 ={abdcf, abdfc, badcf, badfc} Q is an order ideal of a poset (P, ) iff Q  P and: x  Q, y  P: y  x  y  Q.

“Happened-before” [Lamport 1978] • The happened-before relation on a trace σE = [w] is the smallest transitive relation that satisfies: (α, β)  D  (w = u α v β w’) αβ where α, β E. • Note:  is antisymmetric • (E, ) is the poset corresponding to σE. Given the dependency relation D and a representative interleaving sequence of a trace, we can obtain the corresponding partial order.

Model Checking with Traces • EFσ(φ)  “Some reachable state of the trace σ satisfies φ.” • In general, NP-complete for boolean formulae φ [Chase, Garg 1993]. s2 • Tractable predicate classes for EF: • “Stable” predicates [Chandy, Lamport 1985] • Once it turns true, it stays true. • E.g., deadlock, termination. f c Stable c f d b a a b s0

Traces and Lattices {a, b, d, c, f} c f {a, b, d, f} {a, b, d, c} {a, b, d} f c d {a, b} c f {a} {b} a b d b a σ {} Trace as partial order O(σ) a b s0 Lattice of order ideals Interleaved representation • Order ideals of a poset form a lattice under the subset relation. • G and H are order ideals  G  H and G H are order ideals. Overload “order ideal” to mean “state”

Meet-closed predicates [Chase, Garg 1995] • G φ and H φ  G∩ H  φ. • A meet-closed predicate φ has a “least” satisfying state • “least” = reached by executing the fewest number of events. • If some state G  φ, then there exists at least one “crucial event” e  G, such that it is necessary to execute e in order to reach any state (from G) that satisfies φ. • “necessary”, but not “sufficient”. • If the crucial event can be identified in polynomial time (O(|E|k) time, for some constant k), then φ is called a linear predicate. {a, b, d, c, f} G={a, b, d, f} H={a, b, d, c} GH={a, b, d} {a, b} {a} {b} {}

Linear predicates [Chase, Garg 1995]  φ • Examples of linear predicates: • “Local” predicates • Defined using only local variables from a single process. • A conjunction of local predicates • l1 l2  l3 …. • If crucial event is identified in O(|E|k), then EF(φ) takes O(|E|k+1) time. • Boolean formulae can be written as a disjunction of linear predicates! • EF(φ1φ2 …φm) = EF(φ1)  EF(φ2) …  EF(φm)

Trace Cover [Kashyap, Garg – ASE 2005] • A set of traces Δ of a program P is a trace coverfor P iff σΔ States(σ) is exactly the reachable state space of P. • σ1 σ2 :u σ1, w σ2, such that u is a prefix of w. • Lemma:σ1 σ2  States(σ1)  States(σ2) • Suffices to consider traces that are maximal under . s2 s1 f e c d c d f e d c b a a b s0 Trace Cover: σ1 = {abcde, abced, bacde, baced} σ2 = {abdcf, abdfc, badcf, badfc}

Generating representative interleavings • Persistent set [Godefroid, Pirottin 1993]: T enabled(s) is persistent in s iff for any non-empty path starting from s in the full state space graph: s = s1 s2s3… sn sn+1 where ti  T, 1  i  n, ti is independent of all transitions in T. t1 t2 t3 tn-1 tn b a b a s c c If {b, c} is persistent in s, then (a, b)  I, (a, c)  I. f

s2 s1 f e c d • Theorem 4 [Peled 1994]: Exploring a persistent set of events at each state is sufficient to construct a representative interleaving for each trace of P that is maximal under . c d f e d c b a b a s0 abd f c a b abc e d

Obtaining (E, ) • Assign vector timestamps to events [Mattern 1989, Fidge 1991] • Timestamp is an integer vector of dimension n (# of processes). • α.v denotes the timestamp of event α. • When α Piis concatenated to sequence τ: • dep(α) = all events in τ on which α is dependent. • For all j, initializeα.v[j] to the max jth component in dep(α). • Increment α.v[i]. b2 • Theorem: α.v < β.v α β a1, a2 P1 b1, b2 P2 (a2, b2)  D (22) a2 (02) a1 a2 b1 b2 a1 b1 (01) (10) Vector timestamps capture exactly the poset (E, )

Comparison with P.O. reduction p,q α β p,¬q p,q • A transition is invisible w.r.t. a set of variables if it does not change the value of any of them. • In p.o. reduction: • If persistent(s)  enabled(s) then every α persistent(s) must be invisible [Peled 1993]. • Reduction highly dependent on the properties being checked [Gerth et al., 1995]. • High expressibility: can check LTL-X, CTL-X [Peled and Wilke 1997]. • Our approach: • Don’t worry about invisibility. • Size of representation is independent of properties being checked . • Can check much more limited classes of predicates. α β p,q

SPIN PROMELA Reduced transition graph

“Trace Cover” SPIN Trace cover algorithms PROMELA Trace Cover • EFP(φ)  “Some reachable state of the program P satisfies φ.” • Let Δ be a trace cover for P. • EFP(φ) = σΔEFσ(φ)

Experimental Results (a) No errors in protocols

Experimental Results (a) Safety violations present in protocols

Concluding Remarks • Two-pronged approach: • Compact, implicit representation of state space. • Polynomial algorithms for model checking on the representation. • Main limitation: Expressibility • Future work: • Increase expressibility!

Exploiting Predicate Structure for Efficient Reachability Detection