We extend positive supercompilation to handle negative as well as positive information. This is done by instrumenting the underlying unfold rules with a small rewrite system that handles constraints on terms, thereby ensuring perfect information propagation. We illustrate this by transforming a naively specialised string matcher into an optimal one. The presented algorithm is guaranteed to terminate by means of generalisation steps.

"Classical" partial deduction, within the framework by Lloyd and Shepherdson, computes partial deduction for separate atoms independently. As a consequence, a number of program optimisations, known from unfold/fold transformations and supercompilation, cannot be achieved. In this paper, we show that this restriction can be lifted through (polygenetic) specialisation of entire atom conjunctions. We present a generic algorithm for such partial deduction and discuss its correctness in an extended formal framework. We concentrate on novel control challenges specific to this "conjunctive" partial deduction. We refine the generic algorithm into a fully automatic concrete one that registers partially deduced conjunctions in a global tree, and prove its termination and correctness. We discuss some further control refinements and illustrate the operation of the concrete algorithm and/or some of its possible variants on interesting transformation examples.

Functional logic languages combine the operational principles of the most important declarative programming paradigms, namely functional and logic programming. Inductively sequential programs admit the definition of optimal computation strategies and are the basis of several recent (lazy) functional logic languages. In this paper, we define a partial evaluator for inductively sequential functional logic programs. We prove strong correctness of this partial evaluator and show that the nice properties of inductively sequential programs carry over to the specialization process and the specialized programs. In particular, the structure of the programs is preserved by the specialization process. This is in contrast to other partial evaluation methods for functional logic programs which can destroy the original program structure. Finally, we present some experiments which highlight the practical advantages of our approach. 1 Introduction Functional logic languages combine the operational p...

We present an algorithm for inverse computation in a first-order functional language based on the notion of a perfect process tree. The Universal Resolving Algorithm (URA) introduced in this paper is sound and complete, and computes each solution, if it exists, in finite time. The algorithm has been implemented for TSG, a typed dialect of S-Graph, and shows some remarkable results for the inverse computation of functional programs such as pattern matching and the inverse interpretation of While-programs.

We have recently defined a framework for Narrowing-driven Partial Evaluation (NPE) of functional logic programs. This method is as powerful as partial deduction of logic programs and positive supercompilation of functional programs. Although it is possible to treat complex terms containing primitive functions (e.g. conjunctions or equations) in the NPE framework, its basic control mechanisms do not allow for effective polygenetic specialization of these complex expressions. We introduce a sophisticated unfolding rule endowed with a dynamic narrowing strategy which permits flexible scheduling of the elements (in conjunctions) which are reduced during specialization. We also present a novel abstraction operator which carefully considers primitive functions and is the key to achieving accurate polygenetic specialization. The abstraction operator extends some recent partitioning techniques defined in the framework of conjunctive partial deduction. We provide experimental results obtained from an implementation using the INDY system which demonstrate that the control refinements produce better specializations.

Functional logic languages with a complete operational semantics are based on narrowing, a unification-based goal-solving mechanism which subsumes the reduction principle of functional languages and the resolution principle of logic languages. Needed narrowing is an optimal narrowing strategy and the basis of several recent functional logic languages. In this paper, we define a partial evaluator for functional logic programs based on needed narrowing. We prove strong correctness of this partial evaluator and show that the nice properties of needed narrowing carry over to the specialization process and the specialized programs. In particular, the structure of the specialized programs provides for the application of optimal evaluation strategies. This is in contrast to other partial evaluation methods for functional logic programs which can change the original program structure in a negative way. Finally, we present some experiments which highlight the practical advantages of our approach.

The aim of this work is the development and application of a partial evaluation procedure for rewriting-based functional logic programs. Functional logic programming languages unite the two main declarative programming paradigms. The rewriting-based computational model extends traditional functional programming languages by incorporating logical features, including logical variables and built-in search, into its framework. This work is the first to address the automatic specialisation of these functional logic programs. In particular, a theoretical framework for the partial evaluation of rewriting-based functional logic programs is defined and its correctness is established. Then, an algorithm is formalised which incorporates the theoretical framework for the procedure in a fully automatic technique. Constraint solving is used to represent additional information about the terms encountered during the transformation in order to improve the efficiency and size of the residual programs. ...

We survey fundamental concept in inverse programming and present the Universal Resolving Algorithm (URA), an algorithm for inverse computation in a first-order, functional programming language. We discusst he principles behind the algorithm, including a three-step approach based on the notion of a perfect process tree, and demonstrate our implementation with several examples. We explaint he idea of a semantics modifier for inverse computation which allows us to perform inverse computation in other programming languages via interpreters.

This paper presents an application of partial evaluation (program specialization) techniques to concurrent programs. The language chosen for this investigation is a very simple CSP-like language. A standard binding-time analysis for imperative languages is extended in order to deal with the basic concurrent constructs (synchronous communication and nondeterministic choice). Based on the binding-time annotations, a specialization transformation is defined and proved correct. In order to maintain a simple and clear presentation, the specialization algorithm addresses only the data transfer component of the communication; partial evaluation, the way it is defined here, always generates residual synchronizations. However, a simple approximate analysis for detecting and removing redundant synchronizations from the residual program (i.e. synchronizations whose removal does not increase the nondeterminism of a program) can be performed. The paper also addresses pragmatic concerns such as im...

The paper describes the internal structure of HOSC, an experimental supercompiler dealing with programs written in a higher-order functional language. A detailed and formal account is given of the concepts and algorithms the supercompiler is based upon.