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The Concurrency Workbench A Semantics Based Tool for the Verification of Concurrent Systems



The Concurrency Workbench: A Semantics Based Tool for the Veri cation of Concurrent Systems
Rance Cleaveland y Joachim Parrow z September 13, 1994 Bernhard Ste en x

The Concurrency Workbench is an automated tool for analyzing networks of nite-state processes expressed in Milner's Calculus of Communicating Systems. Its key feature is its breadth: a variety of di erent veri cation methods, including equivalence checking, preorder checking, and model checking, are supported for several di erent process semantics. One experience from our work is that a large number of interesting veri cation methods can be formulated as combinations of a small number of primitive algorithms. The Workbench has been applied to the veri cation of communications protocols and mutual exclusion algorithms and has proven a valuable aid in teaching and research.

Abstract

1 Introduction
This paper describes the Concurrency Workbench 11, 12, 13], a tool that supports the automatic veri cation of nite-state processes. Such tools are practically motivated: the development of complex distributed computer systems requires sophisticated veri cation techniques to guarantee correctness, and the increase in detail rapidly becomes unmanageable without computer assistance. Finite-state systems, such as communications protocols and hardware, are particularly suitable for automated analysis because their nitary nature ensures the existence of decision procedures for a wide range of system properties. A goal in the design of the Workbench is to incorporate several di erent veri cation methods, as well as process semantics, in a modular fashion. This means that each method may be applied to di erent semantic models, yielding a spectrum of techniques for reasoning about processes. The versatility of the Workbench has many advantages: it supports mixed veri cation strategies which use more than one method, it facilitates a comparison between many techniques for formal veri cation, and it makes the system easily extensible. This versatility contrasts with existing automated
Research supported by British Science and Engineering Research Council grant GC/D69464. This paper appeared in ACM Transactions on Programming Languages and Systems, 15(1):36{72, January 1993. y Computer Science Department, North Carolina State University, Box 8206, Raleigh, NC 27695, USA. Much of the work described in this paper was performed while the author was a research associate in the Department of Computer Science at the University of Sussex, Brighton, UK. z Swedish Institute of Computer Science, Box 1263, S-164 28 Kista, SWEDEN. Part of the work reported here was performed while the author was on leave at the University of Edinburgh, supported by a grant from the Science and Engineering Research Council. x Lehrstuhl fur Informatik II, RWTH Aachen, Ahornstra e 55, W-5100 Aachen, GERMANY

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tools, which typically embody a particular semantics and a particular form of veri cation. Examples of such systems include Aldebaran 22], AUTO 3], CESAR 47], COSPAN 28], EMC 6], and Winston 42]. Other tools, such as SPIN 32], perform more specialized kinds of analysis (such as deadlock detection) and are used primarily to validate (as opposed to verify) existing real-world systems. In order to achieve this exibility the algorithms in the Workbench are partitioned into three layers. The rst layer manages interaction with the user and also contains the basic de nition of process semantics in terms of labeled transition graphs, which describe the behavior of processes in terms of the communication events they may engage in. The second layer provides transformations that may be applied to transition graphs. These transformations enable the user of the Workbench to change the semantic model of processes under consideration. The third layer includes the basic analysis algorithms for establishing whether a process meets a speci cation. Depending on the veri cation method used, a speci cation may either be another process (describing the desired behavior) or a formula in a modal logic expressing a relevant property. The Workbench has been successfully applied to verifying communication protocols, notably the Alternating Bit Protocol, the CSMA/CD protocol 46] and the communication layer of the BHIVE multiprocessor 25], and mutual exclusion algorithms 55]; it has also been used to debug the Edinburgh Computer Science Department's electronic mailing system. It is currently being investigated as a tool for analyzing communications protocols by Swedish Telecom and by HewlettPackard, and it has been successfully used in education, in industry as well as in universities. The remainder of the paper is organized as follows. In the next section we describe the conceptual structure of the Workbench and give an overview of the di erent veri cation methods it supports. Section 3 presents the model of processes used and the process transformations that enable di erent semantics to be supported. Sections 4, 5 and 6 discuss the equivalence-checking, preorder-checking and model-checking facilities, respectively, while Section 7 describes actual sessions with the Workbench and concludes with a discussion of performance aspects of the system. Section 8 contains a brief account of some of the ways in which the system is being extended, and Section 9 presents our conclusions and directions for future work.

2 The Architecture of the Workbench
Figure 1 provides an overview of the Workbench. In order to supply a wide variety of approaches for veri cation while maintaining a conceptually economical core, the system is highly modularized. The system includes three major layers. The interface layer oversees the interaction between the Workbench and the user. Its key component is a command interpreter, which invokes the appropriate parts of the other layers and presents analysis results. Each veri cation method is implemented as one command, which may require parameters in the form of processes and modal formulas. Parsers transform the concrete syntax of such parameters into parse trees, which may be stored in environments maintained by the Workbench. There is also a package de ning the basic semantics of processes. Following 43], a process is interpreted as labeled transition graph that describes the states a process may enter and 2

2.1 The Interface Layer

? ? ?? ? ?? ? ?? ? ? ? ? ? ? ? ??

? ? ? Layer 2: ? SEMANTIC ? TRANSFORMATION ? ? ? ?? ? ?? ? ?? ? ?? ? ? ? ? ? ? ? ??equival- preorder ?? ?? ?? Layer 3: ?? model ? ? ence, ?? ? BASIC ? checker?? ? minimizer ? ?? ANALYSIS ? ? ? ?? ?? ?? ?? ? ?? ? ?? ?? ? ??
acceptance graphs deterministic graphs observation graphs

? ? ?? Layer 1: ? ? ? ?? INTERFACE ? ? ? ? ???? ? ?? ? ? ?? command interpreter ? ?? ? parsers unparsers environments ??? ? basic semantic definitions ??

Figure 1: Overview of the Workbench

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the state transitions that result when a process executes an action. A description of the syntax and semantics of processes can be found in Section 3. The semantics layer consists of algorithms for transforming the transition graphs generated by the interface layer. For example, the observation transformation adds transitions by permitting visible actions to absorb sequences of internal computation steps; thus the resulting transition graphs do not record the timing of internal computations in the corresponding processes. The deterministic transformation makes the transition graphs deterministic in the usual sense of the word: the resulting graphs do not record nondeterministic choices or internal computation steps. The acceptance transformation yields deterministic graphs augmented with information regarding the potential for in nite internal computation and for nondeterminism (and hence deadlock) in the form of acceptance sets. These transformations are described in Section 3.3. The semantics layer refers to processes represented as transition graphs rather than processes in the abstract syntax. Thus, future changes of the particular syntax will not require changes in this layer. The Workbench provides three main methods for proving that processes meet their speci cations, and the analysis layer contains the corresponding basic analysis algorithms. In the rst method, speci cations are themselves processes that describe precisely the high-level behavior required of an implementation. The corresponding algorithm determines whether two processes are equivalent in the sense of having the same behavior. This algorithm can also be used to minimize a process, i.e. compute an equivalent process with a minimal number of states. The de nition of equivalence and a brief account of the algorithm can be found in Section 4. The second method also uses processes as speci cations, but these speci cations are treated as minimal requirements to be met by implementations. In this approach speci cations can be annotated with \holes" (or \don't care" points); an implementation satis es one of these partial speci cations if it supplies at least the behavior demanded by the speci cation while lling in some of these holes. The method relies on an ordering relation, or preorder, between processes: a process A is \more de ned than" a process B if A has the same behavior as B except for the holes in B. The preorder algorithm determines if a process is more de ned than its speci cation in this sense. A de nition of the preorder and an account of the algorithm can be found in Section 5. The third method involves the use of a modal logic, the propositional (modal) mu-calculus. Assertions formulated in this logic are viewed as speci cations; examples of such assertions are \there are no deadlocks" or \every action of type a is always followed by an action of type b". The logic exhibits a considerable expressive power 20, 50]. The model-checking algorithm determines whether a process satis es such an assertion; it is described in Section 6. The basic analysis algorithms are \polymorphic" in the sense that they work equally well on the di erent kinds of transition graphs supplied by the semantics layer. For instance, the equivalence algorithm computes CCS strong equivalence on transition graphs. If applied to observation graphs then this equivalence corresponds to CCS observation equivalence on the original transition graphs; if the transition graphs have been made deterministic it corresponds to trace equivalence. Observation congruence and testing (failures) equivalence can also be computed by rst choosing 4

2.2 The Semantics Layer

2.3 The Analysis Layer

appropriate transformations and applying the general equivalence checking algorithm. Analogous results hold for the other basic analysis algorithms.

3 Representation of Processes
This section describes the syntax of the Calculus of Communicating Systems (CCS), which is used to de ne processes, or agents, used in the Workbench, and it shows how such agents are interpreted as transition graphs. Transformations of transition graphs are also introduced. They enable changes in the process semantics under consideration. We assume the reader to have some familiarity with CCS. CCS agents are built from a set of actions containing a distinguished unobservable (or silent) action . The observable (or communication) actions are divided into input actions and output actions. In the following a; b; : : : will range over input actions, and a; b; : : : will range over output actions. Input action a and output action a are said to be complementary, re ecting the fact that they represent input and output on the \port" a. We consider only communication actions without value parameters. Agents are de ned using the following standard operators from 43]. Nil Terminated process ? Unde ned process a: Pre xing by action a; unary pre x operator + Choice; binary in x operator j Parallel composition; binary in x operator n L Restriction on ( nite) set L of actions; unary post x operator f ] Relabeling by f , which maps actions to actions; unary post x operator Relabeling functions f are required to satisfy two conditions: f ( ) = , and f (a) = f (a). They are frequently written as a sequence of substitutions; for example p a1=b1; a2=b2] is the process p whose b1; b2; b1 and b2 transitions are relabeled to a1 , a2, a1 and a2 , respectively. We also assume a set of agent identi ers. An identi er A may be bound to an agent expression pA that may itself contain A. This enables recursively de ned processes. a Agents are given an operational semantics de ned in terms of a transition relation, ?!, where a p0 holds when p can evolve a is an action. Figure 2 de nes this relation formally. Intuitively, p?! into p0 by performing action a; in this case, p0 is said to be an a-derivative of p. The transition relation is de ned inductively on the basis of the aconstructors used to de ne an agent. Thus, a a a a:p?!p holds for any p, and p + q ?!p0 if either p?!p0 or q ?!p0. The agent pjq behaves like the \interleaving" of p and q with the possibility of complementary actions synchronizing to produce a action. p n L acts like p with the exception that no actions in L are allowed, while p f ] behaves like p with actions renamed by f . The agents Nil and ? are incapable of any transitions; the former represents a terminated process, while the latter can be thought of as a \don't care" state, or as an agent whose behavior is unknown. An agent is said to be (globally) divergent if it is capable of an in nite sequence of actions or if it may reach a state containing an unguarded occurrence of ? by performing some number of actions. Here, one agent expression, E , is guarded in another, E 0, if a:E is a subexpression of E 0 5

3.1 Actions and Agents

a a:p ?! p a p ?! p0 a q ?! q 0 a p ?! p0 a q ?! q 0 a a p ?! p0 ; q ?! q 0 a p ?! p0 ; a; a 62 L a p ?! p0 a pA ?! p0

) ) ) ) ) ) ) )

a p + q ?! p0 a p + q ?! q 0 a pjq ?! p0 jq a pjq ?! pjq 0 pjq ?! p0 jq 0 a p n L ?! p0 n L f (a) p f ] ?! p0 f ] a A ?! p0 where pA is the agent expression bound to identi er A

Figure 2: The Operational Semantics of CCS. for some action a. If E is unguarded in E 0, then E is \top-level" with respect to E 0 in the sense that the initial transitions available to E also a ect the initial transitions of E 0. Examples of agents de ned in CCS appear in Figure 3. The Workbench uses transition graphs (or rooted labeled transition systems) to model processes. These graphs statically represent the operational behavior of agents; given an agent, the system generates the corresponding transition graph on the basis of the transitions available to the agent. A transition graph contains a set of nodes (corresponding to processes) with one distinguished node, the root node, and an action-indexed family of sets of edges (corresponding to transitions between processes). If an edge comes from a set labeled by a, we say that it is labeled by a. An edge a labeled by a has source n and target n0 i p?!p0 holds of the corresponding processes. The root is indicated by an unlabeled arrow. Figure 4 contains examples of transition graphs. Each node additionally carries a polymorphic information eld, the contents of which vary according to the computations being performed on the graph. For example, the algorithm for computing testing equivalence and the algorithm for computing preorders store acceptance sets and divergence information, respectively, in this eld. It should be noted that the transition graph corresponding to an agent is not necessarily nite, owing to the fact that CCS permits the de nition of processes that can create an arbitrary number of subprocesses. For example, an \unbounded counter" p can be de ned as i:(d:Nil j p). Here i stands for increment and d for decrement; when an increment occurs the counter creates a new copy of itself that then runs in parallel with d:Nil , the subprocess that can respond to decrement requests. The graph of p can be seen to have in nitely many states. Of course the Workbench cannot construct such graphs explicitly, and when presented with agents such as p the algorithm for graph generation will not terminate. In general, most interesting decision problems are undecidable on agents with in nite state spaces. 6

3.2 Transition Graphs

bufn de nes a bu er of capacity n. bufn buf0 n bufi n bufn n

= = = =

buf0 n in:buf1 n in:bufin+1 + out:bufin?1 for i = 1; : : :; n ? 1 out:bufn?1 n

cbufn de nes a compositional bu er of capacity n. cbufn = ( buf1 x1=out] j : : : j buf1 xi =in; xi+1=out] j : : : j buf1 xn?1 =in] ) n fx1 ; :; xn?1g {z } | i=1;:::;n?2

The partial bu er of capacity n, pbufn , speci es agents that behave like bu ers of capacity n, provided that no attempt is made to insert additional elements when the bu er is full.
pbufn pbuf0 n pbufi n pbufn n

= = = =

pbuf0 n in:pbufn?1 n in:pbufin+1 + out:pbufin?1 for i = 1; : : :; n ? 1 in:? + out:pbufn?1 n

J ^ M

The transition graph for bufn , the bu er of capacity n.

'$s p p p '$s '$ s '$ s&% &% s&% &% s 's $ s s s
Figure 3: Examples of Bu ers De ned in CCS.

in

out

N M

in

N

in

out

M

out

N M

in

N

out

J ^ 6

out

in

-?
out

-

in

-

The transition graph for cbuf2 , the compositional bu er of capacity 2. Figure 4: Examples of Transition Graphs. 7

J ^ 6

'$$ 's '$ s s s & %
in out in

-?
out

"

-?

out in

-

out

in

6

Figure 5: The Observation Graph for cbuf2 . As we indicated previously, several transformations on transition graphs are used in conjunction with general algorithms to yield a variety of veri cation methods. We brie y describe some of these transformations here. The transition graphs as described in Section 3.2 are synchronous in the sense that they faithfully represent actions, and hence the \timing behavior", of agents. Many veri cation methods require this information; however, others do not, and to cater for these the Workbench includes a procedure for computing observation graphs. Observation graphs are based on the notion of observations. These are de ned as follows.
" n=)n0 i a n=)n0 i

3.3 Graph Transformations

3.3.1 Observation Graphs

n?! n0 " a " n=)?!=)n0

" So =) is de ned as the" transitive and re exive closure of ?!, and =a is de ned in terms of ) a . These relations allow actions to be absorbed into visible relational products of =) and ?! actions, so that the precise amount of internal computation is obscured. a The observation graph transformation takes a graph and modi es the edges to re ect the =) " relations instead of the ?! and ?! relations. It uses well-known methods for computing a and =) the product of two relations and the transitive and re exive closure of a relation. Figure 5 indicates the nature of the transformation (for clarity, we have omitted the " -loops resulting from the re exive closure of ?!; there will be one such self-looping edge from each node). The transformation takes time that is cubic in the number of nodes in the graph. In most cases the subroutine for transitive closure accounts for more than 80 per cent of the execution time when determining observation equivalence. A variation of the observation transformation computes congruence graphs, which are used to check for observational congruence 43] and weak precongruence 54]. Intuitively, these graphs are observation graphs that record the possibility of initial -actions. To construct them, a copy of the root node is created; this new node becomes the root node of the congruence graph, and by construction it has no incoming edges. Subsequently, the observation transformation is applied as

8

before, except that for the new root node, the transitive closure of ?! is applied, rather than the transitive and re exive closure. The strong and observation equivalences and preorders distinguish agents on the basis of the exact points during their executions where nondeterministic choices are made. Accordingly, the transition graphs mentioned in Sections 3.2 and 3.3.1 faithfully record each time a agent makes such a choice. However, other relations do not require such detailed accounts of the choice structure of agents; for example, the may preorder and equivalence 29] (which coincide with trace containment and equivalence, respectively) require only information about an agent's language, or the sequences of visible actions the agent may perform. In order to compute these relations the Workbench includes an algorithm for transforming transition graphs into language-equivalent deterministic graphs (also called Dgraphs in 9]), i.e. graphs having no -derivatives and at most one a-derivative per node for any action a. As a simple example, the transition graph

3.3.2 Deterministic Graphs

R ? ?@ b ?

As another example, the deterministic graph of cbuf2 is precisely the transition graph of buf:2 The algorithm for computing deterministic graphs is well-known from automata theory (see e.g. 33]). In general, this transformation has an exponential complexity, owing to the fact that it is theoretically possible to have a node in the deterministic graph for each subset of nodes in the original graph. Our experience, however, indicates that the number of nodes is usually smaller than the number of nodes in the original graph, owing to the collapsing of -transitions.

rr r rr
a ??@@

H j

is transformed to

?

rr r
a b ??@@

H j

R @

3.3.3 Acceptance Graphs

In addition to the language of an agent, other relations, such as the testing and failures equivalences and preorders 29, 31], require information about an agent's divergence potential and degree of nondeterminism as it attempts to execute a sequence of visible actions. The appropriate transition graphs for these relations are acceptance graphs (also called Tgraphs in 9]); these are deterministic graphs whose nodes additionally contain information regarding divergence and nondeterminism encoded as acceptance sets. The acceptance set n:acc of a node n is a set of sets of actions. Each element in the acceptance set corresponds to a stable state (a state from which no internal action is immediately possible) in the original graph, and it contains precisely the actions that are immediately possible from this stable state. For example,

H@ j a? @ ? R ? ?@ b ?

where the root node has acceptance set

rr r rr

has the acceptance graph

?

rr r
a b ??@@

H j

R @

f;; fbgg
9

n06

J ^

'$ s s s
in

-? n

out in

1

-n

2

out

n0:acc = ffingg n1 :acc = ffin; outgg n2 :acc = ffoutgg
Figure 6: The acceptance graph for cbuf2 . and each leaf node has acceptance set

Here, the two elements of the acceptance set correspond to the possibilities of the original graph to evolve unobservably along the right branch (where no further action is possible) or the middle branch (where only b is possible). Note that fag is not a member of the acceptance set of the start state set since it is not possible to reach a stable state in which only an a is possible from the start state of the original graph. As another example, Figure 6 shows the acceptance graph resulting from the transformation of cbuf2 . Acceptance sets may also be used to record divergence information. By convention, if an acceptance graph node has an empty acceptance set, then the execution of the sequence of actions leading from the root node to the present node can diverge, i.e. result in an in nite internal computation. A closely related kind of graph, the must graph (also called STgraph), is appropriate for the must equivalence and preorder ( 29]). Must graphs are like acceptance graphs, except that divergent nodes have no outgoing edges. The algorithm for generating acceptance and must graphs is described in 9].

f;g:

4 Equivalence Checking
The rst analysis procedure we present computes equivalences between two agents. As indicated in Section 2, our approach is to convert the agents to transition graphs of the appropriate type and then apply a general equivalence algorithm. The Workbench uses a general notion of equivalence between transition graphs that is based on node matching. Intuitively, two transition graphs are deemed equivalent if it is possible to match up their nodes in such a way that 1. two matched nodes have compatible information elds (the speci c notion of compatibility will depend on the equivalence being computed); 10

4.1 De nition of the Equivalence

2. if two nodes are matched and one has an a-derivative, then the other must have a matching a-derivative; and 3. the root nodes of the two transition graphs are matched. This intuitive account may be formalized as follows. Let G1 and G2 be transition graphs with node sets N1 and N2 , respectively, let N = N1 N2, and let C N N be an equivalence relation re ecting a notion of \compatibility" between information elds. implies that:

De nition 4.1 A C -bisimulation on G and G is a relation R N N such that hm; ni 2 R
1 2

a a 1. if m?!m0 then 9n0 : n?!n0 and hm0 ; n0i 2 R, and a a 2. if n?!n0 then 9m0 : m?!m0 and hm0; n0i 2 R, and 3. hm; ni 2 C .

Two transition graphs are said to be C -equivalent if there exists a C -bisimulation relating the root nodes of the transition graphs.

4.2 Derived Equivalences

Many equivalences turn out to be instances of C -equivalence combined with graph transformations. Let U denote the universal relation, i.e. U = N N . A U -bisimulation is a bisimulation in the sense of Milner 43] and U -equivalence is strong equivalence in CCS. Observation equivalence corresponds to U -equivalence on observation graphs, observation congruence to U -equivalence on congruence graphs, and trace (or may) equivalence to U -equivalence on deterministic graphs. Let A be de ned by: hm; ni 2 A exactly when m:acc and n:acc are compatible, i.e. each element of m:acc is a superset of an element of n:acc, and vice versa. Two transition graphs are must equivalent if their associated must graphs are A-equivalent, and they are testing (failures) equivalent if their associated acceptance graphs are A-equivalent 9]. As an example, recall the de nitions for bufn and cbufn (see Section 3). For each n, these two agents can be shown to be equivalent according to each of these equivalences, with the exception of strong equivalence.

4.3 The Algorithm

Our algorithm is adapted from one presented in 37]. It works by attempting to nd a C -bisimulation relating the root nodes of the transition graphs. To do so, it maintains a partitioning of the nodes in G1 and G2, the transition graphs under consideration. A partitioning is a set of blocks, where each block is a set of nodes, such that each node is contained in exactly one block. Such a partitioning naturally induces an equivalence relation on the nodes of the transition graphs: two nodes are related precisely when they belong to the same block. 11

The algorithm starts with the partition containing only one block and successively re nes this partition. It terminates when the roots of the two transition graphs end up in di erent blocks (in which case the transition graphs are not equivalent) or the induced equivalence relation on the nodes becomes a C -bisimulation (in which case the transition graphs are C -equivalent). In order to determine whether a partition needs further re nement, the algorithm examines each block in the partition. If a node in a block B1 has an a-derivative in a block B2 , then the algorithm examines all the other nodes in B1 to see whether they also have a-transitions into the same block. If some nodes do not, then the block is split into the set of those nodes that do and the set of those nodes that do not, resulting in a re ned partition. The time and space complexities of this algorithm are O(k `) and O(k + `) respectively, where k is the number of nodes, and ` is the number of transitions, in the two transition graphs. In 44] an algorithm is proposed whose worst case time complexity is O(log(k) `); however, there is not yet enough evidence to suggest that this algorithm is appreciably faster in practice. In any event, this complexity is not a limiting factor; tests with the Workbench have shown that the time consumed by this algorithm is only a small fraction of the total time spent when computing observation equivalence. Most of the time is consumed in graph construction and transformation. One nal interesting point is that the algorithm can be trivially modi ed to determine the coarsest C -bisimulation on the nodes of a single graph. This can be used to transform a graph into a C -equivalent graph which has a minimum number of states: rst compute the coarsest C -bisimulation and then collapse each block in the nal partition into a single node.

5 Preorder Checking
The second basic analysis computes preorders between two agents. This is done in a way similar to equivalence checking; after converting the agents to transition graphs we then apply a general preorder algorithm. The preorder is based upon the following generalization of the notion of equivalence introduced in Section 4.1.

5.1 De nition of the Preorder

The general preorder used by the Workbench is similar to the general equivalence discussed in Section 4.1 in that it is also based on a notion of node matching; however the requirements on matched nodes are relaxed somewhat. Intuitively, one transition graph is less than another if the states of the rst can be matched to those in the second in such a way that 1. if a state in the lesser graph is matched to a state in the greater, then the information eld of the former must be \less" than the latter (the appropriate notion of \less" will in general depend on the precise preorder being computed); 2. if a state in the lesser graph is matched to a state in the greater, and the latter has valid a-transitions, then each a-transition of the former must be matched by some a-transition of the latter (the appropriate notion of \valid" will depend on the preorder being computed); 3. if a state in the lesser graph is matched to a state in the greater, and the former has \viable" a-transitions, then each a-transition of the latter must be matched by some a-transition of the former (the appropriate notion of \viable" will depend on the preorder being computed); and 12

4. the start state of the lesser is matched to the start state of the greater. To formalize this, let G1 and G2 be transition graphs with (disjoint) node sets N1 and N2, let N = N1 N2 , and let C N N be a preorder re ecting a notion of \ordering on information elds" (a preorder is a transitive and re exive relation). Also let Pa N and Qa N be predicates over N , where a ranges over the set of actions. Intuitively, Pa and Qa capture the notions of \viable" and \valid" mentioned above; in other words, they are used to determine the states from which a-transitions must be matched.

De nition 5.1 A parameterized prebisimulation between G and G is a relation R N N
1 2

such that hm; ni 2 R implies that: a a 1. if n 2 Pa then if m?!m0 then 9n0 : n?!n0 and hm0; n0 i 2 R ], and a a 2. if m 2 Qa then if n?!n0 then 9m0 : m?!m0 and hm0; n0i 2 R ], and 3. hm; ni 2 C . The parameterized preorder is de ned by: G1 vP ;Q G2 if there exists a parameterized prebisimuC lation relating the roots of the two transition graphs. Note that when Pa = Qa = N and C is an equivalence relation, then a parameterized prebisimulation is just a C -bisimulation.

5.2 Derived Preorders

Many preorders turn out to be instances of the parameterized preorder combined with graph transformations. Let U denote the universal relation on N and + a the local convergence predicate on a; n+ a holds if n is not globally divergent and cannot be triggered by means of an a-action to reach a globally divergent state. For details of this predicate see 52, 54]. The bisimulation divergence preorder 52, 54] results by setting:

Pa = N; Qa = fnjn+ ag and C = fhm; nij for all a: m+ a ) n+ ag:
This de nes the strong version of the divergence preorder. The weak version, < , where -actions are not observable, can be obtained from the corresponding observation graphs. The may, must and testing preorders require the transformation of graphs into deterministic, must, and acceptance graphs, respectively. Then these relations are the following instances of the general preorder 9]. { The may preorder: Pa = N , Qa = ;, and C = U . { The must preorder: Pa = ;, Qa = fmjm:acc 6= ;g, and hm; ni 2 C holds exactly when either m:acc = ;, or both m:acc and n:acc are nonempty and each element in n:acc is a superset of some element in m:acc. { The testing preorder: Pa = N , Qa = fmjm:acc 6= ;g, and C is de ned as for the must preorder. 13

A preorder can be regarded as a speci cation-implementation relation in which P < Q is interpreted as \Q is closer to an implementation than P ". This interpretation is based upon regarding divergent states as being underspeci ed. For example, ? can be seen as the totally unspeci ed state that allows any process as a correct implementation. We shall sometimes call processes containing divergent nodes partial speci cations, in contrast to complete speci cations that do not possess any divergent nodes. Partial speci cations are interesting in their own right, since a system designer might want to allow freedom for the implementation. In addition, the divergence preorder yields a compositional proof technique for bisimulation equivalences that enables partial speci cations to be used in proofs that implementations are equivalent to complete speci cations (cf. 14, 15, 39, 54]). The key observation underlying this technique is that some processes, although not equivalent, may be used interchangeably in certain contexts. As an example, assume that we have a transport protocol with sender entity sender and receiver entity receiver interconnected through ports L with a medium medium. To verify this protocol, we might want to establish its observational equivalence to a complete service speci cation service:
service

(senderjreceiverjmedium)nL

(1)

Now assume that we have already proved the protocol correct when medium is replaced by the partial bu er of capacity n pbufn , for some n:
service < (senderjreceiverjpbufn )nL:

(2)

We may then proceed to prove that

for any particular m > n. Since \j" and \ n L" contexts preserve < , we therefore obtain: (senderjreceiverjpbufn )nL < (senderjreceiverjbufm )nL: Together with (2) and the transitivity of < , this enables us to conclude the following.
service < (senderjreceiverjbufm )nL

pbufn < bufm

(3) (4)

It is easy to see that the preorder < coincides with for complete speci cations. In fact, whenever the left-hand side process is completely speci ed then so is the right-hand side process, and the processes are equivalent. Thus the completeness of service yields:
service

(senderjreceiverjbufm )nL

and therefore establishes all bu ers of capacity greater than n as correct implementations of the medium. The algorithm for computing the parameterized preorder works by attempting to nd a parameterized prebisimulation relating the roots of the transition graphs. In contrast to Section 4.3, however, preorders cannot be represented by partitions. We obtain an appropriate representation by annotating every node n with a set of nodes considered to be \greater" than n. 14

5.3 The Algorithm

::= tt j

jX j : j _ j ^ j ) j hai j a] j h:i j :] j B arg-list j X: j X:

Figure 7: The syntax of formulas In principle, the preorder algorithm proceeds in the same way as the equivalence algorithm. It starts by considering all states to be indistinguishable, i.e. every node is annotated with the set of all nodes N . Then it successively reduces the annotation of each node until the root node of G2 no longer is in the annotation of the root node of G1 (in which case G1 vP ;Q G2) or the annotations 6 C determine a C -prebisimulation (in which case G1 vP ;Q G2). The reduction of the annotation of a C node proceeds according to two rules. First, if the node has an a-derivative n then each node in its annotation that satis es Pa must also have an a-derivative that is in the annotation of n; nodes not meeting this condition are deleted from the annotation. Second, if the node satis es Qa and a node n in its annotation has an a-derivative n0 then the node must have an a-derivative having n0 in its annotation; otherwise, n is deleted from the annotation as well. The time and space complexities of this algorithm are O(k4 `) and O(k2 + `), respectively, where k is the number of states, and ` is the number of transitions, in the two transition graphs. In 16] an algorithm is proposed whose worst case time complexity is O(`2). The implementation of this algorithm is underway. The loss of e ciency compared with the equivalence algorithm is due to the fact that we cannot use the same compact representation of behavioral relations as in Section 4.3.

6 Model Checking
The Workbench also supports a veri cation method based on model checking 5, 6, 7, 20, 53], in which speci cations are written in an expressive modal logic based on the propositional (modal) mu-calculus. The system can automatically check whether an agent meets such a speci cation. The Workbench actually uses two logics, an interface logic and a system logic. The former is a \syntactically sugared" version of the latter that also provides for user-de ned propositional constructors, called macros. The model checker establishes that a node in a graph enjoys a property in the interface logic by rst translating the property into the system logic, which is simpler to analyze. We shall only describe the interface logic here.

6.1 The Logic

The interface logic includes traditional propositional constants and connectives together with modal operators and mechanisms for de ning recursive propositions. The formulas are described by the grammar in Figure 7. In this description X ranges over variables, a over actions, B over user15

de ned macro identi ers, and arg-list over lists of actions and/or formulas that B requires in order to produce a proposition. There is a restriction placed on in X: and X: that requires any free occurrences of X to appear positively, i.e. in the scope of an even number of negations. The formulas are interpreted with respect to nodes in transition graphs. tt and hold of every node and no node, respectively. X is interpreted with respect to an environment binding variables to propositions; n satis es X if it satis es the formula to which X is bound in the environment. : holds of a node n if does not hold of n, 1 _ 2 holds of n if either 1 or 2 does, 1 ^ 2 holds of n if both 1 and 2 do, and 1 ) 2 holds of n if, whenever 1 holds of n, then 2 does as well. The modal constructors hai, a], h:i and :] make statements about the edges leaving a node. A node n satis es hai if it has an a-derivative satisfying , while n satis es a] if all its a-derivatives satisfy . In the case that n has no such derivatives, n trivially satis es a] . In h:i and :] , the \." should be thought of as a \wild-card" action; n satis es h:i if it satis es hai for some a, while it satis es :] if it satis es a] for all a. A macro can be thought of as a \function" that accepts some number of arguments, which may be either actions or formulas, and returns a proposition. A formula B arg-list is then interpreted as the proposition returned by B in response to arg-list. Formulas of the type X: and X: are recursive formulas; they correspond to certain kinds of in nite conjunctions and disjunctions in the following sense. Let 0 be the proposition tt, and de ne i+1 to be the proposition i=X ], namely, the proposition obtained by substituting i for free occurrences of X in . Then X: corresponds to the in nite conjunction V1 i . Dually let i=0 ^ 0 be the proposition , and let ^ i+1 be de ned as ^ i=X ]. Then X: may be interpreted as the in nite disjunction W1 ^ i. i=0 The recursive proposition constructors add tremendous expressive power to the logic (cf. 20, 50]). For example, they allow the description of invariance (or safety) and eventuality (or liveness) properties. However, the formulas are in general unintuitive and di cult to understand. We have found that the most e ective way to use the model checker is to choose a collection of intuitively well-understood operators and then \code up" these operators as macros. For instance, it is possible to de ne the operators of the temporal logic CTL 6] as macros. Examples include the following.
AU1 AU2

AG AF

= = = =

X:( X:( X:( X:(

^ :]X ) _ (h:itt ^ :]X )) _ ( ^ :]X )) _ ( ^ h:itt ^ :]X ))

AG holds of n if holds of every node reachable (via some sequence of transitions) from n, while AF holds of n if along every sequence of transitions leaving n, some state n0 satis es . AU1 holds of n if, along every maximal path of nodes starting at n, is true until a state is reached where is true. AU2 is the same as AU1 , except that here additionally is required to hold eventually. AU1 corresponds to the CTL \weak" until operator, while AU2 corresponds to the CTL \strong" until operator (over all paths). It is also possible to write formulas expressing properties that are useful in describing fairness constraints; many of these involve the use of mutually recursive greatest and least xed point formulas 20].
16

6.2 The Algorithm

The algorithm for determining whether a node satis es a system logic formula works on sequents of the form H ` n 2 , where n is a node, is a formula, and H is a set of hypotheses, or assumptions of the form n0 : X: 0. The (informal) interpretation of this sequent is that under the assumptions H , n satis es . The procedure is tableau-based, meaning that it attempts to build a top-down \proof" of H ` n 2 . The method used comes from 7]; we shall not describe it here. Another tableau-based approach appears in 53], while a semantics-based algorithm is given in 20]; an automated proof system for a subset of the logic is presented in 38]. Applying the algorithm to graphs generated by the di erent graph transformations yields different notions of satisfaction. For instance, checking propositions against observation graphs causes the modal operators to be insensitive to -actions; one should also note that the observation graph transformation causes information about the eventuality properties of an agent to be lost, owing to the fact that every state in these graphs has an "-transition to itself. Accordingly, every property of the form AF will be false for every state that does not satisfy . As an example, it is possible to show that cbufn , for particular n, is deadlock-free as follows. De ne the macro Deadlock by Deadlock = :h:itt This proposition is true of states that cannot perform any actions. Using the model checker, one can establish that cbufn satis es the formula

AG(:Deadlock )
where AG is the macro de ned above; this formula states that it is always the case that cbufn is not deadlocked. It is also possible to show that cbufn is live, i.e. always capable of eventually engaging in either an in or an out, by automatically verifying that cbufn satis es the following formula. AG((AF hinitt) _ (AF houtitt)) The algorithm in general has complexity that is exponential in the size of the formula being checked, although for special classes of formulas it is well-behaved. A linear-time algorithm for a particular subclass of this logic has been proposed in 17] and will be incorporated in future versions of the Concurrency Workbench.

7 Using the Concurrency Workbench
In this section we illustrate the main analysis procedures of the Workbench by verifying a very simple communications protocol. We then discuss a more substantial example in which we use the Workbench to debug a faulty implementation of the Alternating Bit Protocol. The section concludes with a discussion of performance aspects of the system. In the next three subsections we analyze the following simple communications protocol in order to illustrate how one uses the veri cation facilities implemented in the Workbench. The service speci cation of the protocol requires that any message sent must be received before a second message may 17

7.1 A Simple Protocol

be sent. The protocol speci cation requires two processes, a sender and a receiver, and a medium that connects them. After sending a message via the medium to the receiver, the sender blocks until the receipt of an acknowledgement enables it to send a new message. An acknowledgement is sent from the receiver as soon as it receives a message. It is also sent via the medium. After sending the acknowledgement the receiver is ready to receive another message. What follows is an edited account of an actual session with the Workbench. We show how the above protocol may be formalized and veri ed in di erent frameworks using the equivalence, preorder and model checking features of the system. When the Workbench is rst invoked, the following appears on the screen.
Welcome to the Concurrency Workbench! (V 5.0). Command:

The system employs a \command loop"; it accepts commands from the user, prints its responses on the screen, and prompts for the next command. It is possible to de ne processes and propositions, and to invoke the analysis procedures this way.

7.2 De ning Agents and Checking Equivalence

In order to verify the communications protocol, we rst must enter its service speci cation into the Workbench. For equivalence checking, we formalize speci cations and implementations as processes. Processes can be entered using the command bi (for bind identi er). In response to this command the system then prompts us for the corresponding arguments.
Command: bi Identifier: SERVICE Agent: send.'receive.SERVICE Command:

In fact, the command above de nes a process SERVICE that is capable of performing an in nite sequence of strictly alternating send and 'receive actions. The ' is Workbench notation for action complementation. The protocol speci cation can be formalized as the parallel combination of three processes: one for the sender, one for the receiver, and one for the medium. The sender initially waits for a message to send, after which it passes the message to the medium using the channel from and then awaits an acknowledgement on the channel ack_to. When the medium receives a message along its channel from it makes it available on its channel to, and when it receives an acknowledgement on its channel ack_from it makes it available on its channel ack_to. When the receiver gets a message on channel to, it announces that the message is available for receipt and then sends an acknowledgement along the channel ack_from. The corresponding processes in the Workbench are de ned using the following commands.
Command: bi Identifier: SENDER

18

Agent: send.'from.ack_to.SENDER Command: bi Identifier: MEDIUM Agent: from.'to.MEDIUM + ack_from.'ack_to.MEDIUM Command: bi Identifier: RECEIVER Agent: to.'receive.'ack_from.RECEIVER Command: bi Identifier: PROTOCOL Agent: (SENDER | MEDIUM | RECEIVER)\{from, to, ack_from, ack_to}

The use of the restriction operator \{from, to, ack_from, ack_to} in the de nition of PROTOCOL ensures that the channels listed are \internal", i.e. not accessible to processes other than the sender and receiver. To view the process de nitions entered so far, we may now invoke the pe (print environment) command.
PROTOCOL = (SENDER | MEDIUM | RECEIVER)\{from, to, ack_from, ack_to} RECEIVER = to.'receive.'ack_from.RECEIVER MEDIUM = from.'to.MEDIUM + ack_from.'ack_to.MEDIUM SENDER = send.'from.ack_to.SENDER SERVICE = send.'receive.SERVICE

In the equivalence checking veri cation method, we show the correctness of this protocol by establishing that it is semantically equivalent to the service speci cation. We do so by invoking the equivalence checking commands of the Workbench. In this example, we use the commands eq to determine whether the PROTOCOL and SERVICE are observationally equivalent and testeq to determine if they are testing equivalent. In each case, the Workbench prompts the user for the necessary process arguments.
Command: eq Agent: PROTOCOL Agent: SERVICE true Command: testeq Agent: PROTOCOL Agent: SERVICE true

The responses show that the two processes are observationally equivalent and testing equivalent.

7.3 Preorder Checking

To illustrate the use of preorders in the Workbench we employ the technique described at the end of Section 5.2 to prove another version of the protocol correct. We modify the de nition of the 19

medium in the protocol speci cation; the new de nition is partial, re ecting the fact that there may be semantically di erent implementations for the medium that still lead to a correct overall implementation of the service speci cation. As in Section 5.2 we then show how to use the preorder to deduce that the protocol is still correct when the speci cation of the medium is replaced by any of its implementations (i.e. any agent that it precedes in the preorder). The new medium speci cation, and the protocol obtained by substituting this speci cation for the old medium, are given as follows.
Command: bi Identifier: PARTIAL_MEDIUM Agent: from.('to.PARTIAL_MEDIUM + ack_from.@) + ack_from.('ack_to.PARTIAL_MEDIUM + from.@) Command: bi Identifier: PARTIAL_PROTOCOL Agent: (SENDER | PARTIAL_MEDIUM | RECEIVER)\{from, to, ack_from, ack_to}

The symbol @ is Workbench notation for the agent ?. Intuitively, after receiving a message on the channel from, PARTIAL_MEDIUM may either deliver it on its channel to, or receive a message on its channel ack_from, in which case its behavior is unspeci ed. The medium behaves \dually" in response to acknowledgements. It should be noted that PARTIAL_PROTOCOL is both observationally equivalent and testing equivalent to SERVICE; this may be checked using the equivalence checking commands illustrated above. We now de ne an implementation of this medium speci cation. It consists of two one-place bu ers running in parallel: one for messages, and one for acknowledgements.
Command: bi Identifier: NEW_MEDIUM Agent: MESSAGE_BUFFER | ACK_BUFFER Command: bi Identifier: MESSAGE_BUFFER Agent: from.'to.MESSAGE_BUFFER Command: bi Identifier: ACK_BUFFER Agent: ack_from.'ack_to.ACK_BUFFER

Using the preorder checker, it is now possible to show that the protocol implementation
NEW_PROTOCOL = (SENDER | NEW_MEDIUM | RECEIVER)\{to, from, ack_to, ack_from}

is observationally equivalent to SERVICE. We rst check that the partial medium is less than NEW_MEDIUM in the bisimulation preorder using the command wpr (weak preorder).
Command: wpr Agent: PARTIAL_MEDIUM Agent: NEW_MEDIUM true

20

One can also verify that PARTIAL_PROTOCOL never reaches an underspeci ed state using, for example, the model checking facility. Therefore NEW_PROTOCOL is equivalent to PARTIAL_PROTOCOL, and hence to SERVICE. Notice that we could substitute other implementations of PARTIAL_MEDIUM for NEW_MEDIUM and still conclude that the result is observationally equivalent to SERVICE; additional analysis of SENDER and RECEIVER is unnecessary. To illustrate the model checking facility of the Workbench, we show how the service speci cation of the protocol de nition may be recast as formulas in the temporal logic CTL in the Workbench, and then use the model checker to establish that PROTOCOL (as de ned in Section 7.2) satis es these formulas. The model checking facility is also useful in establishing that systems enjoy speci c properties of interest, such as deadlock-freedom and freedom from divergence. We rst de ne some propositional macros using the Workbench command bmi (bind macro identi er).
Command: bmi Identifier: AG Enter parameters one at a time, terminating with "end". Proposition parameters must begin with an uppercase letter, action parameters otherwise. Parameter: P Parameter: end Body: max(X. P &

7.4 Model Checking

.]X)

In a similar way, we can de ne the macros Can and Can't; the results of these de nition can then be displayed using the pmi (print macro environment) command as follows.
Command: pme Name: Parameters: Body: Name: Parameters: Body: Name: Parameters: Body: Can't a ~(Can a) Can a min(X.<a>T | <t>X) AG P max(X. P &

.]X)

Intuitively, a state (process) satis es AG P if every state reachable from the argument state satis es P, while a state satis es Can a if a may occur as the next visible action. Can't is the negation of Can; a state satis es Can't a if, no matter how much internal computation is 21

performed, an a is impossible. Here max is the maximum xed point operator, while min is the minimum xed point operator. In formulas, | is the disjunction operator and & the conjunction operator, while T represents the proposition tt. The internal action is represented by t in the Workbench. These macros may now be used to de ne formulas in the Workbench. The bpi (bind propositional identi er) command can now be used to enter the four formulas that the service speci cation comprises. This results in the following proposition environment, which can be viewed by means of the ppe (print propositional environment) command.
SERVICE1 SERVICE2 SERVICE3 SERVICE4 = = = = AG ((Can send) | (Can 'receive)) AG ( send](Can 'receive) & 'receive](Can send)) AG ( send](Can't send) & 'receive](Can't 'receive)) Can send

The rst formula states that the protocol is always in a state where either a send or a 'receive may happen. The second says that it is always the case that after a send, the process can 'receive, and vice versa, while the third prohibits two consecutive send's or 'receive's from happening without an intervening visible action. The nal formula stipulates that a send must initially be possible. Taken together, these formulas specify a process that engages an in nite alternating sequence of send's and 'receive's, beginning with a send. To verify the protocol against this speci cation, one invokes the command csp (check strong proposition) for the conjunction of the four formulas. The result is the following.
Command: csp Agent: PROTOCOL Proposition: SERVICE1 & SERVICE2 & SERVICE3 & SERVICE4 true

This establishes the correctness of the protocol speci cation in the logical framework.

7.5 Debugging a Protocol

In this section we develop a version of the well-known Alternating Bit Protocol 1]; although apparently correct, the system is faulty, and we show how to debug the system with the help of utilities supplied by the Workbench. The following analysis highlights the dramatic e ect that oftenunstated assumptions have on the correctness of concurrent systems; in this case the correctness of the protocol hinges subtly on the communication medium having a very speci c property that is not mentioned in the informal description of the protocol. It is just such considerations that motivate the need for automated veri cation tools. The Alternating Bit Protocol 1] is designed to ensure the reliable transfer of data over faulty communications lines; it is an example of a sliding window protocol (with window size one). In the protocol, sending and receiving processes alternate between two states in response to the receipt of messages (in the case of the receiving process) and acknowledgements (in the case of the sending process). Senders and receivers may also time out while waiting for acknowledgements and messages, respectively. A full account of the protocol may be found in 1]. Here we formalize a version of this protocol consisting of one sender and one receiver, with the sender sending data values consisting of a single bit over the given medium to the receiver. The general structure of our system appears in Figure 8. The sender receives the bits it is to send via 22

s00 send0 s10 s01 Sender s11 Medium

r00 r10 r01 r11 Receiver rec0

send1

rack0 rack1

sack0 sack1

rec1

Figure 8: The network structure for the Alternating Bit Protocol.
SPEC ’rec0 ’rec1

send0

send1

SPEC = send0.’rec0.SPEC + send1.’rec1.SPEC

Figure 9: The speci cation of the Alternating Bit Protocol. the send0 and send1 channels from the users of the protocol; intuitively, users wishing to send a \0" will use send0, while users wishing to send a \1" will use send1.1 Similarly, the receiver will deliver the values sent to it on ports rec0 or rec1, depending on whether the value is 0 or 1. The medium conveys messages containing two bits|the data bit and a \sequence bit"|from the sender to the receiver and acknowledgements containing a sequence bit from the receiver to the sender. The speci cation of the protocol is that every message that is sent is correctly received; the formalization of this appears in Figure 9. We now turn to the formalizations of the medium, sender and receiver. In each case, we give the corresponding nite-state machine as well as a listing of the associated Workbench code (obtained using the pe command). As is usual in the development of communication protocols, we assume that the medium to be used is given. In this case, the medium is unreliable; it may internally decide either to deliver a message that has been given to it or to lose it. This is modeled by the choice involving actions that appears after every s action; either the medium delivers the message (by
1 This encoding of message values into port names is a standard technique for mimicking message passing in pure CCS.

23

τ ’rack0

τ ’rack1

τ

sack0

sack1

τ

Medium

τ

τ

τ

τ

s10

s00

s01

s11

’r10 τ τ ’r00 ’r01 τ τ

’r11

Medium =

s00.(t.’r00.Medium + t.Medium) + s10.(t.’r10.Medium + t.Medium) + s01.(t.’r01.Medium + t.Medium) + s11.(t.’r11.Medium + t.Medium) + sack0.(t.’rack0.Medium + t.Medium) + sack1.(t.’rack1.Medium + t.Medium)

Figure 10: The medium. 24

allowing an r action), or it returns to its initial state (meaning that the message was lost). (Recall that t is Workbench notation for .) Figure 10 contains the formalization of the medium. The sender accepts bits to be sent (by responding to the appropriate sendi actions) and uses the medium to deliver them to the receiver. In order to detect when the medium has lost a message, the sender also appends a one-bit \sequence number" to each message it sends out. After giving a message to the medium, the sender awaits an acknowledgement from the receiver (via the medium) containing the same sequence number; if this happens, then the sequence number is incremented (modulo 2), and the sender is ready for the next value. However, if the sender receives an acknowledgement with the wrong sequence number, then it resends the same message (with the same sequence number) and awaits the appropriate acknowledgement. The sender may also time out (by executing a t action) while awaiting an acknowledgement, in which case it resends the last message it sent and waits for the acknowledgment. In our implementation, the sender uses the medium to send data value i with sequence number j by executing the action 'sij and awaits an acknowledgement with sequence number j by executing the action rackj . In states S_0 (the start state), S00 and S10 the sequence number used is 0; in S_1, S01 and S11 it is 1. The sender appears in Figure 11. The receiver awaits messages from the medium with a particular sequence number. If the sequence number of the message matches the one it expects, it makes the data value in the message available by executing the appropriate 'reci action and sends out an acknowledgement containing the sequence number; it then increments the sequence number (modulo 2) that it expects of the next message. If the sequence numbers do not match, then the receiver sends out an acknowledgement of the sequence number that it received and then waits for the sender to \resend". The receiver may also time out while waiting for a message, in which case it performs the same actions as when it receives a message with the wrong sequence number. In our implementation, the medium receives data value i with sequence number j by executing action rij , and it sends an acknowledgement with sequence number j by executing 'sackj . The receiver is given in Figure 12. We now begin analyzing the protocol. The rst thing we do is assemble the sender, medium and receiver into one unit, and restrict all actions involving the medium to be local, by executing the following.
Command: bi Identifier: ABP Agent: (S_0 | Medium | R0) \{r00, r10, r01, r11, s00, s10, s01, s11, rack0, rack1, sack0, sack1}

Even though none of the components contains more than 13 states, the resulting system has 181. To check the correctness of ABP we compare it with SPEC using the eq command; the time taken for this on a Sun SparcStation with 16 MB of memory is about one minute, which includes the construction of the nite-state machines from the CCS speci cations.
Command: eq Agent: ABP Agent: SPEC false

So ABP is incorrect! This might seem surprising, since the correctness of Alternating Bit Protocol is well accepted; and, in fact, for slightly di erent de nitions of the faulty Medium the implementation 25

S_0 send0 send1

S00

S10

rack1

τ

’s00

’s10

τ

rack1

rack0 S_1 send0 S01

rack0

send1 S11

rack0

τ

’s01

’s11

τ

rack0

rack1

rack1

S_0 S00 S10 S_1 S01 S11

= = = = = =

send0.S00 + send1.S10 ’s00.(rack0.S_1 + rack1.S00 ’s10.(rack0.S_1 + rack1.S00 send0.S01 + send1.s11 ’s01.(rack1.S_0 + rack0.S01 ’s11.(rack1.S_0 + rack0.S11

+ t.S00) + t.S00) + t.S01) + t.S11)

Figure 11: The sender. 26

R0 r00 r01, r11,τ ’sack1 r10

’rec0

’rec1

’sack0 R1 r01 r00, r10,τ ’sack0 r11

’sack1

’rec0

’rec1

R0 = r00.’rec0.’sack0.R1 + r10.’rec1.’sack0.R1 + r01.’sack1.R0 + r11.’sack1.R0 + t.’sack1.R0 R1 = r01.’rec0.’sack1.R0 + r11.’rec1.’sack1.R0 + r00.’sack0.R1 + r10.’sack0.R1 + t.’sack0.R0

Figure 12: The receiver.

27

just described is observationally equivalent to SPEC. As we shall see, the correctness of the protocol in fact depends on a property of media that Medium does not have, and xing the system will entail changing our implementations of the sender and receiver to circumvent this problem. In order to repair ABP, our rst task is to locate the source of the faults in the system.2 In general, the following strategy is useful in locating reasons why two systems are observationally inequivalent. 1. Check that the two processes have the same sorts (i.e. are capable of exactly the same actions). Often, errors arise because of typos in action names; these kinds of errors would be caught in this step. 2. Check whether the two processes are language equivalent. If they are not, then one can use the various state space exploration commands to isolate the source of the inconsistency. 3. Check for deadlocks in the implementation. Often, if two systems are language equivalent but not observationally equivalent, then the reason has to do with the potential for deadlock in one system. To apply this strategy to debug ABP, we rst compute the sorts of SPEC and ABP using the command so.
Command: so Agent: SPEC {'rec0, send0, 'rec1, send1} Command: so Agent: ABP {send1, send0, 'rec1, 'rec0}

In this case, the two processes are capable of exactly the same actions. We now check for language (or may) equivalence; in this case, the result takes approximately a minute to compute on the same workstation.
Command: mayeq Agent: ABP Agent: SPEC true

So there are no problems here, either. We now check for deadlocks. First, we use the model checker to determine if the system can deadlock. To do so, we de ne the proposition Deadlock (with the bpi , or \bind propositional identi er" command) and then examine whether the system is deadlock-free. In this case, the answer is computed in approximately 10 seconds.
Command: bpi Identifier: Deadlock Proposition: ~<.>T

Future versions of the Workbench will have error diagnostic facilities based on techniques developed in 8], thus considerably easing the task of locating the faults in systems.
2

28

Command: csp Agent: ABP Proposition: AG(~Deadlock) false

So the system is not deadlock-free! To locate speci c deadlocked states in ABP, we use the command fd, which outputs a list of all deadlocked states together with a sequence of actions leading from the start state to the deadlocked state. The (partial) result in this case is the following; it takes approximately 90 seconds.
Command: fd Agent: ABP ... ---send0 t t t t---> (S00 | 'r00.Medium | 'sack1.R0) \{r00, r10, r01, r11, s00, s10, s01, s11, rack0, rack1, sack0, sack1} ...

In fact, there are 17 deadlocked states altogether (where a deadlocked state may engage in t actions but is never capable of performing any observable actions). In the above case, and in fact in the other cases as well, the source of the deadlock comes from collisions on the medium. In the given deadlocked state, the medium wishes to deliver a message to the receiver (by executing 'r00), while the receiver wishes to send an acknowledgement (by executing 'sack1). As the medium makes no provisions for handling these collisions, deadlock results. Note that if the medium were to allow sends to \overwrite" messages awaiting delivery, then ABP would be correct; it is this property on which the correctness of the system, as it stands, relies. Fixing the ABP can be done in one of two ways: either a new medium can be designed (this is not always feasible), or the sender and receiver can be altered so that they do not depend on the resolution of collisions by the medium. We follow the latter approach by by requiring the sender and receiver to \ ush" the medium (by receiving and discarding any messages from the medium) before sending a message. The corrected agents appear in Figures 13 and 14. The new ABP system now contains 212 states, and it is observationally equivalent to SPEC. The Concurrency Workbench was originally conceived as a prototype tool designed to support the development and analysis of di erent veri cations methods for distributed systems. Accordingly, a main emphasis during the implementation of the system was on exibility, which we achieved by using a very high-level programming language (Standard ML) that permitted us to develop parameterized versions of the various analyses using higher-order functions. Of course this versatility has a performance penalty, as we indicate in this section; the Workbench is slower than other, more specialized tools for certain analyses. On the other hand, the modular structure of the Workbench makes it easy to replace existing modules with new ones, and as the interlanguage mechanisms 29

7.6 Performance

S_0 send0 rack0, rack1 S00 S10 send1 rack0, rack1

rack1

τ

’s00

’s10

τ

rack1

rack0 S_1 send0 rack0, rack1 S01

rack0

send1 rack0, rack1 S11

rack0

τ

’s01

’s11

τ

rack0

rack1

rack1

S_0 S00 S10 S_1 S01 S11

= = = = = =

send0.S00 + send1.S10 ’s00.(rack0.S.1 + rack1.S00 ’s10.(rack0.S.1 + rack1.S00 send0.S01 + send1.S11 ’s01.(rack1.S.0 + rack0.S01 ’s11.(rack1.S.0 + rack0.S11

+ t.S00) + rack0.S00 + rack1.S00 + t.S00) + rack0.S10 + rack1.S10 + t.S01) + rack0.S01 + rack1.S01 + t.S11) + rack0.S11 + rack1.S11

Figure 13: Corrected version of the sender. 30

R0 r00 r01, r11,τ R0" r00,r10 r01,r11 ’sack1 r10

’rec0 R0’

’rec1 r00,r10 r01,r11 ’sack1

’sack0 R1 r01 r00, r10,τ R1" r00,r10 r01,r11 , R1 r00,r10 r01,r11 ’sack0 r11

’rec0

’rec1

R0 R0’ R0" R1 R1’ R1"

= = = = = =

r00.’rec0.R0’ + r10.’rec1.R0’ ’sack0.R1 + r00.R0’ + r10.R0’ ’sack1.R0 + r00.R0" + r10.R0" r01.’rec0.R1’ + r11.’rec1.R1" ’sack1.R0 + r00.R1’ + r10.R1’ ’sack0.R1 + r00.R1" + r10.R1"

+ + + + + +

r01.R0" r01.R0’ r01.R0" r00.R1" r01.R1’ r01.R1"

+ + + + + +

r11.R0" + t.R0" r11.R0’ r11.R0" r10.R1"+ t.R1" r11.R1’ r11.R1"

Figure 14: Corrected version of the receiver.

31

n
4 5 6 7

States Transitions Observation graph Minimized in SCHEDn in SCHEDn transitions states 117 283 2,009 64 285 831 7,346 160 669 2,287 25,949 384 1,533 9,307 89,534 896 Table 1: Size of SCHEDn for some n before, during and after minimization.

for ML become more sophisticated, it will be possible to include very e cient routines written in lower-level languages in the system. In order to provide a avor of the performance of the automatic analyses in the Workbench, we have chosen the task of minimizing transition graphs with respect to observation equivalence. The agents are taken from Chapter 5 of 43], and are as follows: A = a:c:(b:d:A + d:b:A) D = d:A SCHED n = (A f1 ] j D f2] j j D fn])nfc1; : : :cn g where fi denotes the relabeling ai=a; bi=b; ci=c; ci?1=d]. The idea is that SCHEDn represents a scheduler for n customers: it will start each customer in turn (through the actions ai ), and each customer must signal termination (through bi) before it can be started again. The scheduler is composed of n \cyclers" (A and D); each cycler maintains communication with one customer on ports a and b, and all cyclers are linked into a ring through ports c and d. For a further explanation of this example see 43]. We will consider SCHEDn for n = 4; : : :; 7. (For n < 4 the scheduler is fairly trivial, and for n > 7 the state graph is too large for the Workbench.) The size of these schedulers is indicated in Table 1 (this information, and the running times of the Workbench, is collected from 21]). The column \States in SCHEDn" gives the number of distinct states that the Workbench constructs in the transition graph. Note that agents such as D and d:A are considered distinct here, even though they intuitively represent the same state. The column \Transitions in SCHEDn" give the number of transitions in the graph. The Workbench computes the minimization by rst transforming the corresponding graph to an observation graph, and subsequently applying the general minimization algorithm. In \Observation graph transitions" we list the number of transitions in the observation graph for SCHEDn (the states remain the same). In \Minimized states" we give the number of states in the nal minimized graph. The execution time of the Concurrency Workbench on a Sun Sparcstation with 8Mb memory is 10, 70, 400, and 2000 CPU seconds for 4, 5, 6 and 7 customers respectively. Other, more specialized tools| e.g. AUTO system 48] or the system for deciding branching bisimulation reported in 27]| have been reported to be up to several hundred times faster, if one compares the gures given here with those in 27]. It should be noted that this comparison can only yield a gross estimate of the relative e ciencies of the tools, since the timing information was collected on di erent machines; moreover, in the case of the Workbench, the timing information takes into account the time to construct the nite-state machine from the CCS expression, while the others do not. The fact remains, however, that the other tools are signi cantly more e cient at computing observation 32

tools includes a preorder or model checker, and as we indicated above, there are a number of ways to improve the speed of the Workbench while maintaining the modular and exible structure of the system.

equivalence than the present version of the Workbench. On the other hand, neither of the other

8 Other Features of the Workbench
The Workbench includes other facilities for examining the behavior of agents. In addition, as a result of its modular structure it is relatively easy to extend. This section describes some of these facilities and extensions. The Workbench includes a variety of ways of analyzing the state space of an agent. In addition to the commands for nding deadlocked states in a system, there are various procedures for computing transitions and derivatives. These types of analyses are traditionally found in automatic veri cation tools and will not be discussed further in this paper. The equation-solving feature of the Workbench 45] is used to solve equations of type (AjX )nL = B where A; B and L are given. The method is useful within a top-down or stepwise-re nement strategy: if a speci cation, B , and parts, A, of an implementation are known, solving such an equation amounts to constructing a speci cation of the missing submodules. The method works by successively transforming equations into simpler equations, in parallel with the generation of a solution. These transformations can be performed automatically by the system according to certain heuristics, or the user can apply them interactively. The tool has been used for the generation of a receiver in a communications protocol, where the overall service, the medium, and the sender are known. Two extensions to the system have been implemented and are being investigated. In the rst, the model of computation has been extended to include a restricted form of value passing. In its \pure" form, CCS does not provide for the association of values to communication actions, although it is possible to encode the passing of values by associating a unique name to an action/value pair (cf. Section7.5). In the case of in nite value domains, however, this leads to syntactically in nite agents. In 36], an alternative encoding is proposed, in which the in nitely many data values are represented schematically. Using the resulting transitional semantics, bisimulation equivalences can be de ned in such a way as to correspond exactly to the bisimulation equivalences in full CCS. This result entails a decision procedure for data-independent agents, i.e. agents which communicate data values but do not perform any computations or tests on the values. The decision procedure has been implemented as an extension to the Workbench 41] and is exponential in the size of the agent|in fact, the problem has been shown to be NP-hard. An interface has also been built between the Workbench and the Extended Model Checker 6] (EMC), which is a tool for checking the satis ability of temporal logic (CTL) formulas. EMC views 33

8.1 State Space Analysis

8.2 Equation Solving

8.3 Experimental Extensions

processes somewhat di erently than the other analysis procedures in the Workbench do; there are no communication actions, and states are labeled by atomic propositions. EMC has successfully been applied to veri cation of nontrivial pieces of hardware. The integration with the Workbench was achieved by de ning a translation from labeled transition graphs to the type of structures analyzed by EMC 35]. Another extension is the Lunsen system 23]. Lunsen is an imperative language with primitives for communication between parallel processes. A compiler translates Lunsen code into CCS in the format acceptable to the Workbench, making it possible to use the Workbench for automatic analysis of Lunsen programs. Our experience indicates that many applications, notably distributed algorithms such as mutual exclusion algorithms, are more naturally formulated in Lunsen than in CCS.

9 Conclusion
In this paper we have presented an overview of the Concurrency Workbench. We have shown that it is possible to provide a number of tools for deducing the correctness of processes based on a variety of di erent process semantics while maintaining a conceptually simple core. This has been achieved by maintaining a strict separation between the semantic models of processes and the procedures used to analyze them. One bene t of this modularization is that the system is relatively easy to extend. There are a number of directions for future work on the Workbench. Internally, the speed of a number of routines could be improved, in particular since the implementation language of the system (Standard ML) now supports more constructs for e cient programming (e.g. bit arrays) than was the case when the core of the system was implemented. In addition, more e cient algorithms for preorder checking 16] and model checking 17] have been discovered, and these should be implemented in the system. Another area of investigation would involve developing techniques for reducing the size of transition graphs that the system computes when veri cation routines are invoked. One promising approach is to use equational reasoning as a \rewriting mechanism" to minimize the number of new states created during the generation of a graph. Another involves the use of compositional analysis. The parallel composition of two agents usually entails a combinatorial explosion in the size of the state space of the resulting agent as a function of the state spaces of its components. One means of coping with this is to verify the parallel components separately in a way that implies the correctness of the composite process. The preorder has been investigated in this respect 14, 26, 54]. It is also conceivable that the model checker could be extended to check formulas compositionally using methods developed in 5, 52, 56]. There are also several ways in which the functionality of the Workbench could be extended and improved. As an example, other equivalences and preorders, including GSOS equivalence and the 2 -bisimulation preorder (also called ready simulation) 2, 40], turn out to be instances of the general 3 relations that we examine, and adding these relations to the Workbench is one avenue we plan to pursue. Another involves the computation of distinguishing formulas 30, 8]. At present, when agents are found not to be equivalent, no indication is given as to why. One way to convey such information is to give a formula in the mu-calculus satis ed by one agent but not by the other. A technique for generating such formulas for partition-re nement-based bisimulation algorithms has been proposed in 8]; work is also underway on generating similar diagnostic information for the testing equivalences. A graphical interface is also under development; this tool will permit users to 34

design systems graphically, with the tool then generating the appropriate CCS descriptions. Finally, it would be interesting to develop machinery for verifying systems expressed in formalisms other than CCS. A joint project with INRIA-Sophia Antipolis is being undertaken to develop a front-end generator for the Workbench (and other automated tools) that would make it possible to parameterize the system with respect to the process algebra/programming language used to build agents. Work is also underway on automated techniques for reasoning about actions with priority, probabilistic processes and real-time systems (cf. 4, 10, 18, 24, 34, 49]).

Acknowledgements
We would like to thank Matthew Hennessy, Robin Milner and Colin Stirling for initiating and overseeing the Workbench project. We are also grateful to Lennart Beckman, Jo Blishen, Patrik Ernberg, Lars-ake Fredlund, Michael Mendler, Kevin Mitchell, Fredrik Orava, Bjorn Pehrsson, and David Walker for many helpful suggestions concerning the implementation of the Workbench and the development of this report.

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