program accesses resources in a valid manner. For example, a memory region that has been allocated should be eventually deallocated, and after the deallocation, the region should no longer be accessed. A file that has been opened should be eventually closed. So far, most of the methods to analyze this kind of property have been proposed in rather specific contexts (like studies of memory management and verification of usage of lock primitives), and it was not so clear what is the essence of those methods or how methods proposed for individual problems are related. To remedy this situation, we formalize a general problem of analyzing resource usage as a resource usage analysis problem, and propose a type-based method as a solution to the problem.

We show how some classical static analyses for imperative programs, and the optimizing transformations which they enable, may be expressed and proved correct using elementary logical and denotational techniques. The key ingredients are an interpretation of program properties as relations, rather than predicates, and a realization that although many program analyses are traditionally formulated in very intensional terms, the associated transformations are actually enabled by more liberal extensional properties.

Type-based analysis is an approach to static analysis of programs that has been studied for more than a decade. A type-based analysis assumes that the program type checks, and the analysis takes advantage of that. This paper examines the state of the art of type-based analysis, and it surveys some of the many software tools that use type-based analysis. Most of the surveyed tools use types as discriminators, while most of the theoretical studies use type and effect systems. We conclude that type-based analysis is a promising approach to achieving both provable correctness and good performance with a reasonable effort.

This article presents a hybrid method of partial evaluation (PE), which is exactly as precise as naive online PE and nearly as efficient as state-of-the-art offline PE, for a statically typed call-by-value functional language.

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This paper presents a hybrid method of partial evaluation (PE), which combines the power of online PE and the efficiency of offline PE, for a typed strict functional language. We begin with a naive online partial evaluator, and make it ecient without sacrificing its power. To this end, we (1) use state (instead of continuation) for let-insertion, (2) take a so-called cogen approach, and (3) decrease unnecessary computations - such as unnecessary let-insertions and unused values/expressions - with a type-based use analysis, which subsumes various monovariant binding-time analyses. Our method yields the same residual programs as the naive online partial evaluator, modulo inlining of redundant let-bindings. We implemented and compared our method and existing methods, both online and offline. Experiments show that our method is at least twice as fast as any ot...

We demonstrate that the problem of identifying redundant arguments of function symbols, i.e. parameters which can be replaced by any expression without changing the associated semantics, boils down to proving the validity of a particular class of inductive theorems in the equational theory of confluent, sufficiently complete term rewrite systems (TRSs). Hence, existing results for proving inductive theorems can be exploited, which solve the problem in many interesting cases where previously developed methods fail to recognize and remove redundancies.

The application of automatic transformation processes during the formal development and optimization of programs can introduce encumbrances in the generated code that programmers usually (or presumably) do not write. An example is the introduction of redundant arguments in the functions defined in the program. Redundancy of a parameter means that replacing it by any expression does not change the result. In this work, we provide a method for the analysis and elimination of redundant arguments in term rewriting systems as a model for the programs that can be written in more sophisticated languages.

In this paper we present two type inference systems for detecting useless-code in higher-order typed functional programs. Type inference can be performed in an efficient and complete way, by reducing it to the solution of a system of constraints. We also give a useless-code elimination algorithm which is based on a combined use of these type inference systems. The main application of the technique is the optimization of programs extracted from proofs in logical frameworks, but it could be used as well in the elimination of useless-code determined by program transformations. Capsule Review Dead code is a subterm M of a term t = C[M] which does not matter to computation, that is, which may be replaced by any other term of the same type without altering the observational behaviour of the program: C[M] =obs C[N] for any N with the type of M. Dead-code may be replaced by dummy variable, or even completely removed, in order to optimize a program. The interest in dead-code by the authors started as a by-product of a proof system which generated programs meeting a specification out of proofs that the specification is satisfiable.

Parallel programming is becoming an important cornerstone of general computing. In addition, type systems have significant impact on program analysis. In this paper, we demonstrate an automated typebased system that soundly detects parallelizability of sequential functional programs. Our type inference system discovers the parallelizability property of a sequential program in a modular fashion, by exploring a ring structure among the program’s operators. It handles self-recursive functions with accumulating parameters, as well as a class of non-linear mutual-recursive functions. Programs whose types are inferred to be parallelizable can be automatically transformed to parallel code in a mutumorphic form – a succint model for parallel computation. Transforming into such a form is an important step towards constructing efficient data parallel programs.