The Kestrel Interactive Development System (KIDS) provides automated sup- port for the development of correct and efficient programs from formal specifications.

The Kestrel Interactive Development System (KIDS) provides knowledge-based support for the derivation of correct and efficient programs from formal specifications. We trace the use of KIDS in deriving an algorithm for solving a problem arising from the design of sonar and radar signals. This derivation illustrates algorithm design, a generalized form of deductive inference, program simplification, finite differencing optimization, partial evaluation, case analysis, and data type refinement. All of the KIDS operations are automatic except the algorithm design tactics which presently require some interaction. Dozens of programs have been derived using the KIDS environment and we believe that it could be developed to the point where it can be used for routine programming.

Program transformation is a means to formally develop efficient programs from lucid specifications. A representative sample of the diverse range of program transformation research is classified into several different approaches based upon the motivations for and styles of constructing such formal developments. Individual techniques for supporting construction of developments are also surveyed, and are related to the various approaches.

This paper describes a general approach for automatic and accurate time-bound analysis. The approach consists of transformations for building time-bound functions in the presence of partially known input structures, symbolic evaluation of the time-bound function based on input parameters, optimizations to make the overall analysis efficient as well as accurate, and measurements of primitive parameters, all at the source-language level. We have implemented this approach and performed a number of experiments for analyzing Scheme programs. The measured worst-case times are closely bounded by the calculated bounds. 1 Introduction Analysis of program running time is important for real-time systems, interactive environments, compiler optimizations, performance evaluation, and many other computer applications. It has been extensively studied in many fields of computer science: algorithms [20, 12, 13, 41], programming languages [38, 21, 30, 33], and systems [35, 28, 32, 31]. It is particularl...

Dynamic programming is an important algorithm design technique. It is used for solving problems whose solutions involve recursively solving subproblems that share subsubproblems. While a straightforward recursive program solves common subsubproblems repeatedly and often takes exponential time, a dynamic programming algorithm solves every subsubproblem just once, saves the result, reuses it when the subsubproblem is encountered again, and takes polynomial time. This paper describes a systematic method for transforming programs written as straightforward recursions into programs that use dynamic programming. The method extends the original program to cache all possibly computed values, incrementalizes the extended program with respect to an input increment to use and maintain all cached results, prunes out cached results that are not used in the incremental computation, and uses the resulting incremental program to form an optimized new program. Incrementalization statically exploits semantics of both control structures and data structures and maintains as invariants equalities characterizing cached results. The principle underlying incrementalization is general for achieving drastic program speedups. Compared with previous methods that perform memoization or tabulation, the method based on incrementalization is more powerful and systematic. It has been implemented and applied to numerous problems and succeeded on all of them. 1

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Needed narrowing is a complete operational principle for modern declarative languages which integrate the best features of (lazy) functional and logic programming. We define a transformation methodology for functional logic programs based on needed narrowing. We provide (strong) correctness results for the transformation system w.r.t. the set of computed values and answer substitutions and show that the prominent properties of needed narrowing -- namely, the optimality w.r.t. the length of derivations and the number of computed solutions -- carry over to the transformation process and the transformed programs. We illustrate the power of the system by taking on in our setting two well-known transformation strategies (composition and tupling). We also provide an implementation of the transformation system which, by means of some experimental results, highlights the benefits of our approach.

Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out, but first-order Horn logic is not well suited to application domains such as theorem proving or program synthesis where functions and predicates are the objects of computation. We explore the use of a higher-order representation language and extend EBG to a higher-order logic programming language. Variables may now range over functions and predicates, which leads to an expansion of the space of possible generalizations. We address this problem by extending the logic with the modal ⊔ ⊓ operator (indicating necessary truth) which leads to the language λ ⊔ ⊓ Prolog. We develop a meta-interpreter realizing EBG for λ ⊔ ⊓ Prolog and give some examples in an expanded version of this extended abstract which is available as a technical report [2]. 1

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. We address the problem of specializing logic programs w.r.t. the contexts where they are used. We assume that these contexts are specified by means of computable properties of the input data. We describe a general method by which, given a program P , we can derive a specialized program P 1 such that P and P 1 are equivalent w.r.t. every input data satisfying a given property. Our method extends the techniques for partial evaluation of logic programs based on Lloyd and Shepherdson 's approach, where a context can only be specified by means of a finite set of bindings for the variables of the input goal. In contrast to most program specialization techniques based on partial evaluation, our method may achieve superlinear speedups, and it does so by using a novel generalization technique. 1 Introduction Program specialization is a technique which can be used for deriving from a given program, a new and hopefully more efficient program, by exploiting the knowledge of the context in w...