The role of non-standard type inference in static program analysis has been much studied recently. Early work emphasised the efficiency of type inference algorithms and paid little attention to the correctness of the inference system. Recently more powerful inference systems have been investigated but the connection with efficient inference algorithms has been obscured. The contribution of this paper is twofold: first we show how to transform a program logic into an algorithm and, second, we introduce the notion of lazy types and show how to derive an efficient algorithm for strictness analysis. 1 Introduction Two major formal frameworks have been proposed for static analysis of functional languages: abstract interpretation and type inference. A lot of work has been done to characterise formally the correctness and the power of abstract interpretation. However the development of algorithms has not kept pace with the theoretical developments. This is now a major barrier that is preven...

A substantial amount of work has been devoted to the proof of correctness of various program analyses but much less attention has been paid to the correctness of compiler optimisations based on these analyses. In this paper we tackle the problem in the context of strictness analysis for lazy functional languages. We show that compiler optimisations based on strictness analysis can be expressed formally in the functional framework using continuations. This formal presentation has two benefits: it allows us to give a rigorous correctness proof of the optimised compiler; and it exposes the various optimisations made possible by a strictness analysis. 1 Introduction Realistic compilers for imperative or functional languages include a number of optimisations based on non-trivial global analyses. Proving the correctness of such optimising compilers can be done in three steps: 1. proving the correctness of the original (unoptimised) compiler; Correspondence regarding this paper should be ...

This thesis presents and justifies a framework for programming real-time signal processing systems. The framework extends the existing "block-diagram" programming model; it has three components: a very high-level textual language, a visual language, and the dataflow process network model of computation. The dataflow process network model, although widely-used, lacks a formal description, and I provide a semantics for it. The formal work leads into a new form of actor. Having established the semantics of dataflow processes, the functional language Haskell is layered above this model, providing powerful features---notably polymorphism, higher-order functions, and algebraic program transformation---absent in block-diagram systems. A visual equivalent notation for Haskell, Visual Haskell, ensures that this power does not exclude the "intuitive" appeal of visual interfaces; with some intelligent layout and suggestive icons, a Visual Haskell program can be made to look very like a block dia...

. In this paper we present a general framework for type-based analyses of functional programs. Our framework is a generalisation of our earlier work on strictness analysis and was inspired by Burn's logical framework. The framework is parameterised by a set of types to represent properties and interpretations for constants in the language. To construct a new analysis, the user needs only to supply a model for the types (which properties they denote) and sound rules for the constants. We identify the local properties that must be proven to guarantee the correctness of a specific analysis and algorithm. We illustrate the approach by recasting Hunt and Sand's binding time analysis in our framework. Furthermore we report on experimental results suggesting that our generic inference algorithm can provide the basis for an efficient program analyser. 1 Introduction The first explicit use of types in program analysis was by Kuo and Mishra [14]. They presented a type system for inferring stric...

We present a type inference system for the strictness analysis of lists and we show that it can be used as the basis for an efficient algorithm. The algorithm is as accurate as the usual abstract interpretation technique. One distinctive advantage of this approach is that it is not necessary to impose an abstract domain of a particular depth prior to the analysis: the lazy type algorithm will instead explore the part of a potentially infinite domain required to prove the strictness property. 1 Introduction Simple strictness analysis returns information about the fact that the result of a function application is undefined when some of the arguments are undefined. This information can be used in a compiler for a lazy functional language because the argument of a strict function can be evaluated (up to weak head normal form) and passed by value. However a more sophisticated property might be useful in the presence of lists or other recursive data structures which are pervasive in functio...

. We show that compiler optimisations based on strictness analysis can be expressed formally in the functional framework using continuations. This formal presentation has two benefits: it allows us to give a rigorous correctness proof of the optimised compiler; and it exposes the various optimisations made possible by a strictness analysis. 1 Introduction Realistic compilers for imperative or functional languages include a number of optimisations based on non-trivial global analyses. Proving the correctness of such optimising compilers can be done in three steps: 1. proving the correctness of the original (unoptimised) compiler; 2. proving the correctness of the analysis; and 3. proving the correctness of the modifications of the simple-minded compiler to exploit the results of the analysis. A substantial amount of work has been devoted to steps (1) and (2) but there have been surprisingly few attempts at tackling step (3). In this paper we show how to carry out this third step in the...

domains . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.2 Lattices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.3 Specification of analyses . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.4 Semantic correctness . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.5 Solving systems of equations . . . . . . . . . . . . . . . . . . . . . 13 1.3 This document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 Program analysis with conjunctive types 17 2.1 Strictness types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.1.1 Lindenbaum algebras . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 The strictness logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Relationship to abstract interpretation . . . . . . . . . . . . . . . . . . . . 22 2.4 A variation: binding-time analysis . . . . . . . . . . . . . . . . . . . . . . 22 3 Disjunctions and data structures: Properties 25 3.1 Axiomatisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1.1 Normal Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Abstract domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.1 Base Types, Products, Sums and Functions . . . . . . . . . . . . . 31 3.2.2 Recursive Data Structures . . . . . . . . . . . . . . . . . . . . . . 32 3.2.3 Strictness Properties of Lists . . . . . . . . . . . . . . . . . . . . . 35 4 Disjunctions and data structures: Logic 37 4.1 Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.1 Strictness Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.1.2 Proving properties for lists . . . . . . . . . . . . . . . . . . . . . . 42 4.2 Bibliographical not...

This thesis presents and justi es a framework for programming real-time signal process-ing systems. The framework extends the existing \block-diagram " programming model� it has three components: a very high-level textual language, a visual language, and the data ow process network model of computation. The data ow process network model, although widely-used, lacks a formal description, and I provide a semantics for it. The formal work leads into a new form of actor. Having established the semantics of data ow processes, the functional language Haskell is layered above this model, providing powerful features|notably polymorphism, higher-order func-tions, and algebraic program transformation|absent in block-diagram systems. A visual equivalent notation for Haskell, Visual Haskell, ensures that this power does not exclude the \intuitive " appeal of visual interfaces � with some intelligent layout and suggestive icons, a Visual Haskell program can be made to look very like ablock diagram program. Finally, the functional language is used to further extend data ow process networks, by simulating timed and dynamically-varying networks.

Interpretation versus Demand Propagation Wadler [1987] uses abstract interpretation over a four-point domain for reasoning about strictness on lists. The four points correspond to undefined list (represented by value 0), infinite lists and lists with some tail undefined (value 1), lists with at least one head undefined (value 2), and all lists (value 3). Burn's work [Burn 1987] on evaluation transformers also uses abstract interpretation on the above-mentioned domain for strictness analysis. He defines four evaluators that correspond to the four points mentioned above in the sense that the ith evaluator will fail to produce an answer when given a list with the abstract value i \Gamma 1.

We show that compiler optimisations based on strictness analysis can be expressed formally in the functional framework using continuations. This formal presentation has two benefits: it allows us to give a rigorous correctness proof of the optimised compiler; and it exposes the various optimisations made possible by a strictness analysis. These benefits are especially significant in the presence of partially evaluated data structures. 1 Introduction Realistic compilers for imperative or functional languages include a number of optimisations based on non-trivial global analyses. Proving the correctness of such optimising compilers should involve three steps: 1. proving the correctness of the original (unoptimised) compiler; 2. proving the correctness of the analysis; and 3. proving the correctness of the modifications of the simple-minded compiler to exploit the results of the analysis. A substantial amount of work has been devoted to steps (1) and (2) but there has been surprisingly ...