This introduction tries to give the big picture on the goals, design philosophy, logical foundations, applications, and overall structure of Maude. It is written in an impressionistic, conversational style, and should be read in that spirit. The fact that occasionally some particular technical concept mentioned in passing (for example, “the Church-Rosser property”) may be unfamiliar should not be seen as an obstacle. It should be taken in a relaxed, sporting spirit: those things will become clearer in the body of the manual; here it is just a matter of gaining a first overall impression.
Maude’s language design can be understood as an effort to simultaneously maximize three dimensions:
Although simplicity and performance are natural allies, maximizing expressiveness is perhaps the key point in Maude’s language design. Languages are after all representational devices, and their merits should be judged on the degree to which problems and applications can be represented and reasoned about generally, naturally, and easily. Of course, domain-specific languages also have an important role to play in certain application areas, and can offer a useful “economy of representation” for a given area. In this regard, Maude should be viewed as a high-performance metalanguage, through which many different domain-specific languages can be developed.
Maude’s basic programming statements are very simple and easy to understand. They are equations and rules, and have in both cases a simple rewriting semantics in which instances of the lefthand side pattern are replaced by corresponding instances of the righthand side.
A Maude program containing only equations is called a functional module. It is a functional program defining one or more functions by means of equations, used as simplification rules. For example, if we build lists of quoted identifiers (which are sequences of characters starting with the character ‘’’ and belong to a sort1 Qid) with a “cons” operator denoted by an infix period,
then we can define a length function and a membership predicate by means of the operators and equations
we can apply the second equation for length to simplify the expression three times, and then apply the first equation once to get the final value s s s 0:
This is the standard “replacement of equals by equals” use of equations in elementary algebra and has a very clear and simple semantics in equational logic. Replacement of equals by equals is here performed only from left to right and is then called equational simplification or, alternatively, equational rewriting. Of course, the equations in our program should have good properties as “simplification rules” in the sense that their final result exists and should be unique. This is indeed the case for the two functional definitions given above.
In Maude, equations can be conditional; that is, they may only be applied if a certain condition holds. For example, we can simplify a fraction to its irreducible form using the conditional equationceq is the Maude keyword introducing conditional equations, NzInt is the sort of nonzero integers, and where we assume that the integer quotient (quot) and greatest common divisor (gcd) operations have already been defined by their corresponding equations.
A Maude program containing rules and possibly equations is called a system module. Rules are also computed by rewriting from left to right, that is, as rewrite rules, but they are not equations; instead, they are understood as local transition rules in a possibly concurrent system. Consider, for example, a distributed banking system in which we envision the account objects as floating in a “soup,” that is, in a multiset or bag of objects and messages. Such objects and messages can “dance together” in the distributed soup and can interact locally with each other according to specific rewrite rules. We can represent a bank account as a record-like structure with the name of the object, its class name (Account) and a bal(ance) attribute, say, a natural number. The following are two different account objects in our notation:
Accounts can be updated by receiving different messages and changing their state accordingly. For example, we can have debit and credit messages, such as
We can think of the “soup” as formed just by “juxtaposition” (with empty syntax) of objects and messages. For example, the above two objects and two messages form the soup
Note that these rules are not equations at all: they are local transition rules of the distributed banking system. They can be applied concurrently to different fragments of the soup. For example, applying both rules to the soup above we get the new distributed state:
Note that the rewriting performed is multiset rewriting, so that, regardless of where the account objects and the messages are placed in the soup, they can always come together and rewrite if a rule applies. In Maude this is specified in the equational part of the program (system module) by declaring that the (empty syntax) multiset union operator satisfies the associativity and commutativity equations:
This is not done by giving the above equations explicitly. It is instead done by declaring the multiset union operator with the assoc and comm equational attributes (see Section 4.4.1 and Section 1.1.2 below), as follows, where Configuration denotes the multisets or soups of objects and messages.multiset matching algorithm, in which the multiset union operator is matched modulo associativity and commutativity.
Again, a program involving such rewrite rules is intuitively very simple, and has a very simple rewriting semantics. Of course, the systems specified by such rules can be highly concurrent and nondeterministic; that is, unlike for equations, there is no assumption that all rewrite sequences will lead to the same outcome. For example, depending on the order in which debit or credit messages are consumed, a bank account can end up in quite different states, because the rule for debiting can only be applied if the account balance is big enough. Furthermore, for some systems there may not be any final states: their whole point may be to continuously engage in interactions with their environment as reactive systems.
The above examples illustrate a general fact, namely, that Maude can express with equal ease and naturalness deterministic computations, which lead to a unique final result, and concurrent, nondeterministic computations. The first kind is typically programmed with equations in functional modules, and the second with rules (and perhaps with some equations for the “data” part) in system modules.
In fact, functional modules define a functional sublanguage2 of Maude. In a functional language true to its name, functions have unique values as their results, and it is neither easy nor natural to deal with highly concurrent and nondeterministic systems while keeping the language’s functional semantics. It is well known that such systems pose a serious expressiveness challenge for functional languages. In Maude this challenge is met by system modules, which extend the purely functional semantics of equations to the concurrent rewriting semantics of rules.3 Although certainly declarative in the sense of having a clear logical semantics, system modules are of course not functional: that is their entire raison d’être.
Besides this generality in expressing both deterministic and nondeterministic computations, further expressiveness is gained by the following features:
We briefly discuss each of these features in what follows.
Rewriting with both equations and rules takes place by matching a lefthand side term against the subject term to be rewritten. The most common form of matching is syntactic matching, in which the lefthand side term is matched as a tree on the (tree representation of the) subject term (see Section 4.7). For example, the matching of the lefthand sides for the equations defining the length and _in_ functions above is performed by syntactic matching. But we have already encountered another, more expressive, form of matching, namely, equational matching in the bank accounts example: the lefthand side__ as its top operator, but, thanks to its assoc and comm equational attributes, it is matched not as a tree, but as a multiset. Therefore, the match will succeed provided that the subject multiset contains instances of the terms < I : Account | bal : M > and credit(I, N) in which the variable I is instantiated the same way in both terms, regardless of where those instances appear in the multiset, that is, modulo associativity and commutativity.
In general, a binary operator declared in a Maude module can be defined with any4 combination of equational attributes of: associativity, commutativity, left-, right-, or two-sided identity, and idempotency. Maude then generates an equational matching algorithm for the equational attributes of the different operators in the module, so that each operator is matched modulo its equational attributes. This manual will illustrate with various examples the expressive power afforded by this form of equational matching (see Section 4.8).
In Maude the user can specify each operator with its own syntax, which can be prefix, postfix, infix, or any “mixfix” combination. This is done by indicating with underscores the places where the arguments appear in the mixfix syntax. For example, the infix list cons operator above is specified by _._, the (empty syntax) multiset union operator by __, and the if-then-else operator by if_then_else_fi. In practice, this improves readability (and therefore understandability) of programs and data. In particular, for metalanguage uses, in which another language or logic is represented in Maude, this can make a big difference for understanding large examples, since the Maude representation can keep essentially the original syntax. The combination of user-definable syntax with equations and equational attributes for matching leads to a very expressive capability for specifying any user-definable data. It is well known that any computable data type can be equationally specified . Maude gives users full support for this equational style of defining data which is not restricted to syntactic terms (trees) but can also include lists (modulo associativity), multisets (modulo associativity and commutativity), sets (adding an idempotency equation), and other combinations of equational attributes that can then be used in matching. This great expressiveness for defining data is further enhanced by Maude’s rich type structure, as explained below.
Maude has two varieties of types: sorts, which correspond to well-defined data, and kinds, which may contain error elements. Sorts can be structured in subsort hierarchies, with the subsort relation understood semantically as subset inclusion. For example, for numbers we can have subsort inclusionskind (say the “number kind”) which is interpreted semantically as the set containing all the well-formed numerical expressions for the above number systems as well as error expressions such as, for example, 4 + 7/0. This allows support for partial functions in a total setting, in the sense that a function whose application to some arguments has a kind but not a sort should be considered undefined for those arguments (but notice that functions can also map undefined to defined results, for example in the context of error recovery). Furthermore, operators can be subsort-overloaded, providing a useful form of subtype polymorphism. For example, the addition operation _+_ is subsort overloaded and has typings for each of the above number sorts. A further feature, greatly extending the expressive power for specifying partial functions, is the possibility of defining sorts by means of equational conditions. For example, a sequential composition operation _;_ concatenating two paths in a graph is defined if and only if the target of the first path coincides with the source of the second path. In Maude this can be easily expressed with the “conditional membership” (see Section 4.3):
Maude supports a powerful form of generic programming that substantially extends the parameterized programming capabilities of OBJ3 . The analogous terminology to express these capabilities in higher-order type theory would be parametric polymorphism and dependent types. But in Maude the parameters are not just types, but theories, including operators and equations that impose semantic restrictions on the parameterized module instantiations. Thus, whereas a parametric LIST module can be understood just at the level of the parametric type (sort) of list elements, a parameterized SORTING module has the theory TOSET of totally ordered sets as its parameter, including the axioms for the order predicate, that must be satisfied in each correct instance for the sorting function to work properly. Types analogous to dependent types are also supported by making the parameter instantiations depend on specific parametric constants in the parameter theory, and by giving membership axioms depending on such constants. For example, natural numbers modulo n (see Section 22.7), and arrays of length n, can be easily defined this way. The fact that entire modules, and not just types, can be parametric provides even more powerful constructs. For example, TUPLE[n] (see Section 21.3.1) is a “dependent parameterized module” that assigns to each natural number n the parameterized module of n-tuples (together with the tupling and projection operations) with n parameter sorts.
The bank accounts example illustrates a general point, namely, that in Maude it is very easy to support objects and distributed object interactions in a completely declarative style with rewrite rules. Although such object systems are just a particular style of system modules in which object interactions (through messages or directly between objects) are expressed by rewriting, Maude provides special support for object-based programming and for fair execution of object-based applications (see Chapter 8). Furthermore, the Full Maude extension provides special syntax in object-oriented modules (see Chapter 22). Such modules directly support object-oriented concepts like objects, messages, classes, and multiple class inheritance. Moreover, the support for communication with external objects (see Section 9) allows Maude objects to interact by message passing with internet sockets and, through them, with all kinds of other external objects, such as files, databases, graphical user interfaces, sensors, robots, and so on. All this is achieved without compromising Maude’s declarative nature: interaction with normal Maude objects and with external objects can both be programmed with rewrite rules. Using internet sockets as external objects, it is also easy to develop distributed implementations in Maude, where a “soup” of objects and messages is not realized just as a multiset data structure in a single sequential machine, but as a “distributed soup,” with objects and messages in different machines or in transit.
This is a very important feature of Maude. Intuitively, it means that Maude programs can be metarepresented as data, which can then be manipulated and transformed by appropriate functions. It also means that there is a systematic causal connection between Maude modules themselves and their metarepresentations, in the sense that we can either first perform a computation in a module and then metarepresent its result, or, equivalently, we can first metarepresent the module and its initial state and then perform the entire computation at the metalevel. Finally, the metarepresentation process can itself be iterated giving rise to a very useful reflective tower. Thanks to Maude’s logical semantics (more on this in Section 1.2), all this is not just some kind of “glorified hacking,” but a precise form of logical reflection with a well-defined semantics (see Chapter 17 and [33, 34]). There are many important applications of reflection. Let us mention just three:
Achieving expressiveness in all the ways described above without sacrificing performance is a nontrivial matter. Successive Maude implementations have been advancing this goal while expanding the set of language features. More work remains ahead, but it seems fair to say that Maude, although still an interpreter, is a high-performance system that can be used for many non-toy applications with competitive performance and with many advantages over conventional code. Without in any way trying to extrapolate a specific experience into a general conclusion, a concrete example from the Maude user’s trenches may illustrate the point. A formal tool component to check whether a trace of events satisfies a given linear temporal logic (LTL) formula was written in Maude at NASA Ames by Grigore Roşu in about one page of Maude code. The component had a trivial correctness proof—the Maude module was based on the equational definition of the LTL semantics for the different connectives. This replaced a similar component having about 5,000 lines of Java code that had taken over a month to develop by an experienced colleague. The Java tool used a translation of LTL formulas into Büchi automata (the usual method to efficiently model check an LTL formula) and run about three times more slowly than the Maude code. It would have been very difficult to prove the correctness of the Java tool and, having a better and clearly correct alternative in the Maude implementation, this was never done.
Generally and roughly speaking, the current Maude implementation can execute syntactic rewriting with typical speeds from half a million to several million rewrites per second, depending on the particular application and the given machine. Similarly, associative and associative-commutative equational rewriting with term patterns used in practice6 can be performed at the typical rate of one hundred thousand to several hundred thousand rewrites per second.
These figures must be qualified by the observation that, until recently, the cost of an associative or associative-commutative rewriting step depended polynomially on the size of the subject term, even with the most efficient algorithms. In practice this meant that this kind of rewriting was not practical for large applications, in which the lists or multisets to be rewritten could have millions of elements. This situation has been drastically altered by a recent result of Steven Eker  providing new algorithms for associative and associative-commutative rewriting that, for patterns typically encountered in practice, can perform one step of associative rewriting in constant time, and one associative-commutative rewriting step in time proportional to the logarithm of the subject term’s size. Maude supports equational rewriting with these new algorithms.
The reason why the Maude interpreter achieves high performance is that the rewrite rules are carefully analyzed and are then semicompiled into efficient matching and replacement automata  with efficient matching algorithms. One important advantage of semicompilation is that it is possible to trace every single rewriting step. More performance is of course possible by full compilation. Maude has an experimental compiler for a subset of the language which can typically achieve a fivefold speedup over the interpreter.
Four other language features give the user different ways of optimizing the performance of his/her code. One is profiling, allowing a detailed analysis of which statements are most expensive to execute in a given application (see Section 20.1.5). Another is evaluation strategies (see Section 4.4.7), giving the user the possibility of indicating which arguments and in which order to evaluate before simplifying a given operator with the equations. This can range from “no arguments” (a lazy strategy) to “all arguments” (an eager bottom-up strategy) to something in the middle (like evaluating the condition before simplifying an if-then-else expression). Evaluation strategies control the positions in which equations can be applied. But what about rules? The analogous feature for rules is that of frozen argument positions; that is, declaring certain argument positions in an operator with the frozen attribute (see Section 4.4.9) blocks rule rewriting anywhere in the subterms at those positions. A fourth useful feature is memoization (see Section 4.4.8). By giving an operator the memo attribute, Maude stores previous results of function calls to that symbol. This allows trading off space for time, and can lead in some cases to drastic performance improvements.
One nagging question may be reflection. Is reflection really practical from a performance perspective? The answer is yes. In Maude, reflective computations are performed by descent functions that move metalevel computations to the object level whenever possible (see Section 17.6). This, together with the use of caching techniques, makes metalevel computations quite efficient. A typical metalevel computation may perform millions of rewrites very efficiently at the object level, paying a cost (linear in the size of the term) in changes of representation from the metalevel to the object level and back only at the beginning and at the end of the computation.
The foundations of a house do not have to be inspected every day: one is grateful that they are there and are sound. This section describes the logical foundations of Maude in an informal, impressionistic style, not assuming much beyond a cocktail party acquaintance with logic and mathematics. The contents of this section may be read in two ways, and at two different moments:
Readers with a more pragmatic interest may safely skip this section, but they may miss some of the fun.
Maude is a declarative language in the strict sense of the word. That is, a Maude program is a logical theory, and a Maude computation is logical deduction using the axioms specified in the theory/program. But which logic? There are two, one contained in the other. The seamless integration of the functional world within the broader context of concurrent, nondeterministic computation is achieved at the language level by the inclusion of functional modules as a special case of system modules. At the mathematical level this inclusion is precisely the sublogic inclusion in which membership equational logic [105, 17] is embedded in rewriting logic [101, 19].
A functional module specifies a theory in membership equational logic. Mathematically, we can view such a theory as a pair (Σ,E ∪ A). Σ, called the signature, specifies the type structure: sorts, subsorts, kinds, and overloaded operators. E is the collection of (possibly conditional) equations and memberships declared in the functional module, and A is the collection of equational attributes (assoc, comm, and so on) declared for the different operators. Computation is of course the efficient form of equational deduction in which equations are used from left to right as simplification rules.
Similarly, a system module specifies a rewrite theory, that is, a theory in rewriting logic. Mathematically, such a rewrite theory is a 4-tuple ℛ = (Σ,E ∪ A,ϕ,R), where (Σ,E ∪ A) is the module’s equational theory part, ϕ is the function specifying the frozen arguments of each operator in Σ, and R is a collection of (possibly conditional) rewrite rules. Computation is rewriting logic deduction, in which equational simplification with the axioms E ∪ A is intermixed with rewriting computation with the rules R.
We can of course view an equational theory (Σ,E ∪A) as a degenerate rewrite theory of the form (Σ,E ∪ A,ϕ∅,∅), where ϕ∅(f) = ∅, that is, no argument of f is frozen, for each operator f in the signature Σ. This defines a sublogic inclusion from membership equational logic (MEqLogic) into rewriting logic (RWLogic) which we can denote
In Maude this corresponds to the inclusion of functional modules into the broader class of system modules. However, Maude’s inclusion is more general: the user can give the desired freezing information for each operator in the signature of a functional module, not just the ϕ∅ above.
Another important fact is that each Maude module specifies not just a theory, but also an intended mathematical model. This is the model the user has intuitively in mind when writing the module. For functional modules such models consist of certain sets of data and certain functions defined on such data, and are called algebras. For example, the intended model for a NAT module is the natural numbers with the standard arithmetic operations. Similarly, a module LIST-QID may specify a data type of lists of quoted identifiers, and may import NAT and BOOL as submodules to specify functions such as length and _in_. Mathematically, the intended model of a functional module specifying an equational theory (Σ,E ∪ A), with Σ the signature defining the sorts, subsorts, and operators, E the equations and memberships, and A the equational attributes like assoc, comm, and so on, is called the initial algebra of such a theory and is denoted TΣ∕E∪A.
In a similar way, a system module specifying a rewrite theory ℛ = (Σ,E ∪ A,ϕ,R) has an initial model, denoted 𝒯ℛ, which in essence is an algebraic (labeled) transition system.7 The states and data of this system are elements of the underlying initial algebra TΣ∕E∪A. The state transitions are the (possibly complex) concurrent rewrites possible in the system by application of the rules R. For our bank accounts example, these transitions correspond to all the possible concurrent computations that can transform a given “soup” of account objects and messages into another soup. Again, this is the model the programmer of such a system has in mind.
How do the mathematical models associated with Maude modules and the computations performed by them fit together? Very well, thanks. This is the so-called agreement between the mathematical semantics (the models) and the operational semantics (the computations). In this introduction we must necessarily be brief; see Sections 4.6 and 4.7 and  for the whole story in the case of functional modules, and Section 5.3 and  for the case of system modules. Here is the key idea: under certain executability conditions required of Maude modules, both semantics coincide. For functional modules we have already mentioned that the equations should have good properties as simplification rules, so that they evaluate each expression to a single final result. Technically, these are called the Church-Rosser and termination assumptions. Under these assumptions, the final values, called the canonical forms, of all expressions form an algebra called the canonical term algebra. By definition, the results of operations in this algebra are exactly those given by the Maude interpreter: this is as computational a model as one can possibly get. For example, the results in the canonical term algebra of the operations
Suppose that a functional module specifies an equational theory (Σ,E ∪ A) and satisfies the Church-Rosser and termination assumptions. Let us then denote by CanΣ∕E∪A the associated canonical term algebra. The coincidence of the mathematical and operational semantics is then expressed by the fact that we have an isomorphism
In other words, except for a change of representation, both algebras are identical.
For system modules, the executability conditions center around the notion of coherence between rules and equations (see  and Section 5.3). The equational part E ∪A should be Church-Rosser and terminating as before. A reasonable strategy (the one adopted in Maude by the rewrite command, see Chapter 5) is to first apply the equations to reach a canonical form, and then do a rewriting step with a rule in R. But is this strategy complete? Couldn’t we miss rewrites with R that could have been performed if we had not insisted on first simplifying the term to its canonical form with the equations? Coherence guarantees that this kind of incompleteness cannot happen (see Section 5.3).
The observations in the previous section about the agreement between mathematical and operational semantics in Maude programs are of enormous importance for reasoning about them and verifying their correctness. The key point is that there are three different uses of Maude modules:
Note that the distinction between uses (1) and (2) is, for the most part, in the eyes of the beholder. In fact, there is a seamless integration of specifications and code. The same Maude module can simultaneously be viewed as an executable formal specification and as a program. Furthermore, certain kinds of formal requirements needed for verification in (3) can be expressed at the Maude level, either in Maude theories (see Section 6.3.1), or by including some nonexecutable statements in a Maude module giving them the nonexec attribute (see Section 4.5.3). This can be very useful in several ways. For example, we may include lemmas that we have proved about a module, either in theories or as nonexecutable statements in the module itself. Similarly, we may begin with some nonexecutable specifications in a Maude theory, and then refine them using views (see Section 6.3.2) into the desired Maude module satisfying them.
There is, however, no need for all the properties that we wish to formally verify in (3) to be in the logic of Maude, that is, to be statements in membership equational logic or in rewriting logic. More generally, properties can be expressed, for example, as arbitrary first-order logic formulas, or as temporal logic formulas. An interesting issue is then to explain precisely what it means for a Maude module, defined in membership equational logic or in rewriting logic, to satisfy a formula in one of those logics. Here is where the Maude initial model semantics explained in Section 1.2 becomes crucial. Such a semantics means that what a Maude module denotes is a specific mathematical model, namely, the initial one. Satisfaction of any property, expressed as some kind of formula, means satisfaction of that formula in the initial model. This is an important observation, even when the formula in question is expressed in Maude’s native logic. Let us explain this idea in more detail.
Consider, for example, that we have defined natural number addition in a Maude functional module with Peano notation, so that zero is represented as the constant 0, and there is a successor function s_ so that, for example, 2 is represented as s s 0. Natural number addition can then be defined by the equations
The initial model of these equations is precisely the algebra of the natural numbers with zero, successor, and the usual addition function. For example, using the canonical term algebra representation (see Section 1.2), when we add s s 0 and s s 0 in this algebra we obtain the result s s s s 0.
Consider now two relevant properties of natural number addition, namely, associativity and commutativity. These properties are precisely described by the respective equations
where we have used the nonexec attribute to emphasize that these equations are not part of our natural number addition module, and are not meant to be executed (in fact, if executed the first equation would loop). They may, for example, be stated in a separate Maude theory as properties we wish to verify.
The first thing to observe is that the above associativity and commutativity equations are not provable by equational deduction, that is, they do not follow by replacing equals by equals from the two equations defining the addition function. They are in fact inductive properties of the addition function. Therefore, in order to prove them, using for example Maude’s inductive theorem prover (ITP), we need to use a stronger proof method, namely, Peano induction. But for any equational specification, being an inductive property and being a property satisfied by its initial model are one and the same thing . Therefore, what we mean when we say that our natural number addition module satisfies the associativity and commutativity equations is precisely that its initial model does.
Of course, associativity and commutativity are properties expressible in Maude’s native logic (in fact, just in its equational sublogic). But the case of arbitrary first-order formulas is entirely similar. Consider, for example, the property that any even number is the sum of two odd numbers, which can be expressed as the first-order formula
Let us assume, for argument’s sake, that we had also defined the odd and even predicates in our Maude natural number module. What does it mean for our module to satisfy the above formula? Just as before, it exactly means that the initial model denoted by our Maude specification satisfies the formula. The point is that membership equational logic is a sublogic of many-kinded first-order logic with equality (MKFOL=) that we can represent with a sublogic inclusion
Therefore, our initial model is also a first-order logic model, and it is perfectly clear what it means for it to satisfy a first-order formula.
In a similar way, if we have a Maude system module and choose an initial state for it, we may be interested in verifying that it satisfies a given temporal logic formula. Defining satisfaction in this case is not as direct as for first-order formulas, because we do not have a sublogic inclusion from rewriting logic into temporal logic. However, the meaning of satisfaction in this case is also fairly straightforward. The point is that to such a system module, that is, to a rewrite theory in which we have defined some atomic state predicates equationally, we can naturally associate a Kripke structure (see Section 12.2). Since Kripke structures are the standard models of temporal logic, satisfaction of the given temporal logic formula exactly means that the Kripke structure associated to the module satisfies the formula. In fact, such a Kripke structure and the initial model of the rewrite theory are intimately related, so that the initial model can be used to define the corresponding Kripke structure. As explained in Chapters 11 and 12, if our system module is such that the set of states reachable from the initial state is finite, we can use Maude’s search command and Maude’s model checker for linear temporal logic (LTL) as decision procedures to verify, respectively, the satisfaction of invariants and of LTL properties.
Besides being able to use Maude’s inductive theorem prover (ITP) to verify inductive properties of functional modules, and the above-mentioned built-in support for verifying invariants and LTL formulas through the search command and Maude’s LTL model checker, we can use the following Maude tools to formally verify other properties:
These tools are integrated in what is called the Maude Formal Environment (MFE), available at https://github.com/maude-team/MFE. Furthermore, if we are dealing with rewriting logic specifications of real-time and hybrid systems, we can use the Real-Time Maude tool to both simulate such specifications and to perform search and model-checking analysis of their LTL properties [119, 120].
In summary, therefore, Maude supports three seamlessly integrated tasks: programming, executable formal specification, and formal analysis and verification. For analysis and verification purposes, the Maude interpreter itself is the first and most obvious tool. It is in fact a high-performance logical engine that can be used to prove certain kinds of logical facts about our theories. For example, we can use the Maude interpreter as a decision procedure for equational deduction if the desired theory has good properties. Similarly, as already mentioned, we can use it also to verify invariants and LTL properties of finite-state system modules. More generally, we can use other tools in Maude’s formal environment, such as the ITP, MTT, CRC, ChC, and SCC tools (or Real-Time Maude for real-time systems) to formally verify a variety of other properties.
Our previous discussion of the programming, executable specification, and formal verification uses of Maude makes clear that we can distinguish two different levels of formal specification: a system specification level, and a property specification one. In a system specification we are after an unambiguous specification of a given system and how it actually works. Ideally this specification should be both formal and executable, and should therefore provide an executable mathematical model of the system we are interested in. This is exactly what Maude modules provide.
By contrast, when specifying properties of a system we are not necessarily after an executable model of our system. Instead, we assume it, as either already given or to be developed later, and specify such properties in a typically nonexecutable manner: for example in first-order logic, higher-order logic, or some temporal logic. That is, the properties we specify have an intended model, namely the system design captured by a system specification, and we are interested in verifying by different methods that the intended model satisfies the properties stated in our property specification. In the context of Maude, such property specifications can be given in a variety of ways:
We can then use Maude itself and its formal tool environment to try to verify that a given system specified as a Maude module satisfies the desired properties.
Since Maude system specifications should be both formal and executable, Maude native logics, namely, membership equational logic and its rewriting logic extension, should be computational logics, that is, logics in which computation and deduction coincides, and simple enough to allow a high-performance implementation as a declarative programming language. This is what the Maude implementation provides. Of course, as mentioned in Section 1.2 and further explained in Sections 4.6 and 5.3, Maude modules should be theories that satisfy some reasonable executability requirements, making possible not only their efficient execution, but also the already-mentioned coincidence between mathematical and operational semantics.
However, not all computational logics are equally expressive. For example, equational logics (in either first-order or higher-order versions) are very well suited to specify deterministic systems under the Church-Rosser assumption, but poorly equipped to specify concurrent and highly nondeterministic systems. The whole point of extending membership equational logic to rewriting logic is to seamlessly integrate the specification of deterministic systems, through equational specifications in functional modules, and of concurrent and nondeterministic systems, through rewriting logic specifications in system modules, within the same language. Experience has shown that this makes rewriting logic a very expressive semantic framework for system specification. Here we only mention some relevant areas:
Furthermore, other application areas can be naturally supported in appropriate extensions of rewriting logic and Maude. For example, real-time and hybrid systems can be specified as real-time rewrite theories. Such specification can be executed and analyzed in the Real-Time Maude tool [119, 120]. Similarly, probabilistic systems can be specified as probabilistic rewrite theories, and can be simulated in PMaude and analyzed in the VeStA tool [88, 2].
The fact that in a computational logic computation and deduction coincide, so that they are like two sides of the same coin, can be used in two ways: we can use the logic as a semantic framework to specify different computational entities as just explained; or we can use it as a logical framework to represent many other logics in it. That is, if our computational logic has good representational features, it can be used as a universal logic which can faithfully express the inference systems of many other logics.
Since the logic is computational and presumably has an efficient implementation, this is not just a purely theoretical exercise: we can use such an implementation to mechanize deduction in any logic that we can faithfully represent inside our logical framework. Experience has shown that rewriting logic has very good properties as a logical framework in precisely this sense. An important practical consequence is that it becomes quite easy to use Maude to develop a variety of formal tools for different logics. The point is that any such tool has an associated inference system, so it is just a matter of representing such an inference system as a rewrite theory and guiding the application of the inference rules with suitable strategies (see Section 17.7). In addition, since such formal tools often manipulate and transform not only formulas but also theories, Maude’s reflective capabilities, which allow manipulating theories as data, become enormously useful [30, 31].
Reflection and the existence of initial models (and therefore of induction principles for such models) have one further important consequence, namely, that rewriting logic has also good properties as a metalogical framework. A metalogical framework is a logical framework in which we can not only represent and simulate many other logics: we can also reason within the framework about the metalogical properties of the logics thus represented. As explained in , this is exactly what can be done in rewriting logic using Maude and Maude’s inductive theorem prover (ITP).
We call Core Maude the Maude 2 interpreter implemented in C++ and providing all of Maude’s basic functionality. Part I explains in detail all the aspects of Core Maude, including its syntax and parsing, functional and system modules, module hierarchies, module parameterization with theories and module instantiation with views, its suite of predefined modules, the model-checking capabilities, object-based programming, reflection, and metalanguage uses.
Full Maude is an extension of Maude, written in Maude itself, that endows the language with an even more powerful and extensible module algebra than that available in Core Maude. As in Core Maude, modules can be parameterized and instantiated with views, but in addition views can also be parameterized. Full Maude also provides generic modules for n-tuples. Object-oriented modules (which can also be parameterized) support notation for objects, messages, classes, and inheritance.
Full Maude itself can be used as a basis for further extensions, by adding new functionality. It is possible both to change the syntax or the behavior of existing features, and to add new features. In this way Full Maude becomes a common infrastructure on top of which one can build tools, such as, e.g., the Church-Rosser and coherence checkers, as well as environments for other languages, such as, e.g., the Real-Time Maude tool for specifying and analyzing real-time systems [119, 120], and the Maude MSOS tool for modular structural operational semantics .
The present manual documents Maude 3, and explains Maude’s basic concepts in a leisurely and mostly informal style. The material is basically presented following a “grammatical” order; for example, all features related with operators are discussed together. Concepts are introduced by concrete examples, that may be fragments of modules. The complete module examples are available in the official webpage http://maude.cs.illinois.edu. We follow the convention of naming each file in that site as the module it contains, or if a file contains several modules, as the main one. In most cases, the rewriting or search commands and the corresponding outputs are also included in the files.
The manual is divided in three parts: Part I is devoted to Core Maude, Part II is devoted to Full Maude, and Part III is a reference manual. Here is a brief summary of what can be found in the remaining chapters:
Most of the material in this manual also appears in the book All About Maude: A High-Performance Logical Framework, published by Springer as volume 4350 in the series Lecture Notes in Computer Science .
The book contains many additional examples and explanations, as well as information on applications and tools.
We gratefully acknowledge the permission given by Springer to distribute this manual on the web.
Languages are living organisms. The lifeblood provided by experienced users is key to their growth and their improvement. We have benefited much from colleagues who have used different alpha versions of Maude; we cannot mention them all, but Luis Aguirre, Christiano Braga, Feng Chen, Grit Denker, Santiago Escobar, Azadeh Farzan, Joe Hendrix, Merrill Knapp, Nirman Kumar, Óscar Martín, Miguel Palomino, Peter Ölveczky, José Quesada, Adrián Riesco, Dilia Rodriguez, Grigore Roşu, Ralf Sasse, Koushik Sen, Ambarish Sridharanarayanan, Mark-Oliver Stehr, Prasanna Thati, and Alberto Verdejo deserve special thanks for their creative uses of Maude and their suggestions for improving the language. Thanks to Christiano Braga, Peter Mosses, Peter Ölveczky, Miguel Palomino, Sylvan Pinsky, Isabel Pita, Adrián Riesco, Dilia Rodriguez, Manuel Roldán, Mark-Oliver Stehr, Antonio Vallecillo, and Alberto Verdejo for their comments on previous versions of this document.
As already mentioned, Maude’s historical precursor is the OBJ3 language . The OBJ3 experience has greatly influenced the Maude design and philosophy, and we are grateful to all our former OBJ colleagues for this. Joseph Goguen should be mentioned in particular, because of his enormous influence in all aspects of OBJ; and Tim Winkler for having implemented a state-of-the-art OBJ3 system with such great skill.
Two other rewriting logic languages, ELAN  and CafeOBJ , have provided a rich stimulus to the design of Maude. Although our language design solutions have often been different, we have all been wrestling with a similar problem: how to best obtain efficient language implementations of rewriting-based languages. We have benefited much from the ELAN and CafeOBJ experience, and from many discussions with their main designers and implementers: Claude and Hélène Kirchner, Marian Vittek, Pierre-Etienne Moreau, Kokichi Futatsugi, Râzvan Diaconescu, Ataru Nakagawa, Toshimi Sawada, and Makoto Ishisone.
Bringing a new language design to maturity requires a long-term research effort and substantial resources. We are not there yet, but much has been advanced since the early design phases. Perhaps the longest, most sustained support has come from the US Office for Naval Research (ONR) through a series of contracts. We are most grateful to Dr. Ralph Wachter at ONR for his continued encouragement at every step of the way. The US Defense Advance Research Projects Agency (DARPA), the US National Science Foundation (NSF), and the Spanish Ministry for Education and Science (MEC) have also contributed important resources to the development of Maude, its foundations, and its applications.