We consider the problem of specializing constraint logic programs w.r.t. constrained queries. We follow a transformational approach based on rules and strategies. The use of the rules ensures that the specialized program is equivalent to the initial program w.r.t. a given constrained query. The strategies guide the application of the rules so to derive an efficient specialized program. In this paper we address various issues concerning the development of an automated transformation strategy. In particular, we consider the problems of when and how we should unfold, replace constraints, introduce generalized clauses, and apply the contextual constraint replacement rule. We propose a solution to these problems by adapting to our framework various techniques developed in the field of constraint programming, partial evaluation, and abstract interpretation. In particular, we use: (i) suitable solvers for simplifying constraints, (ii) well-quasi-orders for ensuring the termination...

this paper we assume that a system makes transitions from states to states and its evolution can be formalized using a computation tree which is dened as follows. Given a system S and its initial state s 0 , the root of the computation tree for S is s 0 , and every node s i of the computation tree for S has a child node s j i there exists in S a transition from state s i to state s j , called a successor state of s i . The set of all states of a system may be nite or innite. We assume that in every system for every state s i there exists at least one successor state

The goal of automated verification is the definition of a logical framework where hardware or software systems can be formally specified and formal proofs about their properties can be given in a fully automatic way. This involves defining formalisms for encoding systems and the properties of interest. During the last years many logic-based techniques have been developed for automatically verifying properties of systems, the most successful of them being model checking [3]. The success of model checking is mostly due to the use of a particular data structure, Binary Decision Diagrams, which provide a very compact symbolic representation of a possibly very large, but finite, set of states. In order to overcome this finiteness restriction, some effort is now being put into the integration of abstraction and deduction techniques with model checking [15]. Recent papers also demonstrate the usefulness of (constraint) logic programming as a basis for the verification of finite...