We extend the definition of natural semantics to include simply typed λ-terms, instead of first-order terms, for representing programs, and to include inference rules for the introduction and discharge of hypotheses and eigenvariables. This extension, which we call extended natural semantics, affords a higher-level notion of abstract syntax for representing programs and suitable mechanisms for manipulating this syntax. We present several examples of semantic specifications for a simple functional programming language and demonstrate how we achieve simple and elegant manipulations of bound variables in functional programs. All the examples have been implemented and tested in λProlog, a higher-order logic programming language that supports all of the features of extended natural semantics.

We show how improving values (Burton, 1991) can be extended to handle both upper and lower bounds. The result is a new data type, called improving intervals. We give a simple implementation of improving intervals that uses a list of successively tighter bounds to represent a value. A program using improving intervals can be evaluated as a parallel program using speculative evaluation. The utility of improving intervals is demonstrated through two programs: parallel alpha-beta and parallel branch-and-bound

Pattern matching in modern functional programming languages is tied to the representation of data. Unfortunately, this is incompatible with the philosophy of abstract data types.

Two proposals have been made to generalize pattern matching to a broader class of types. The laws mechanism of Miranda allows pattern matching with non-free algebraic data types. More recently, Wadler proposed the concept of views as a more general solution, making it possible to define arbitrary mappings between a physical implementation and a view supporting pattern matching. Originally, it was intended to include views in the new standard lazy functional programming language Haskell.

Laws and views each offer important advantages, particularly with respect to data abstraction. However, if not used with great care, they also introduce serious problems in equational reasoning. As a result, laws have been removed from Miranda and views were not included in the final version of Haskell.

We propose a third approach which unifies the laws and views mechanisms while avoiding their problems. Philosophically, we view pattern matching as a bundling of case recognition and component selection functions instead of a method for inverting data construction. This can be achieved by removing the implied equivalence between data constructors and pattern constructors. In practice, we allow automatic mapping into a view but not out of the view. We show that equational reasoning can still be used with the resulting system. In fact, equational reasoning is easier, since there are fewer hidden traps.

Values belonging to lazy data types have the advantage that sub-components can be accessed without evaluating the values as a whole: unneeded components remain unevaluated. A disadvantage is that often a large amount of space and time is required to handle lazy data types properly. Many special constructor cells are needed to ‘glue’ the individual parts of a composite object together and to store it in the heap. We present a way of representing data in functional languages which makes these special constructor cells superfluous. In some cases, no heap at all is needed to store this data. To make this possible, we introduce a new kind of data type: (partially) strict non-recursive data types. The main advantage of these types is that an efficient call-by-value mechanism can be used to pass arguments. A restrictive subclass of (partially) strict non-recursive data types, partially strict tuples, is treated more comprehensively. We also give examples of important classes of applications. In particular, we show how partially strict tuples can be used to define very efficient input and output primitives. Measurements of applications written in Concurrent Clean which exploit partially strict tuples have shown that speedups of 2 to 3 times are reasonable. Moreover, much less heap space is required when partially strict tuples are used.

We describe the design, implementation and use of a new kind of profiling tool that yields valuable information about the memory use of lazy functional programs. The tool has two parts: a modified functional language implementation which generates profiling information during the execution of programs, and a separate program which converts this information to graphical form. With the aid of profile graphs, one can make alterations to a functional program which dramatically reduce its space consumption. We demonstrate this in the case of a genuine example - the first to which the tool was applied - for which the results are strikingly successful.

List terminators are discussed as a new form of qualifier in list comprehensions for early termination of a list. The semantics of list terminators is expressed in terms of an optimal translation of list comprehensions (cf. Wadler, 1987) because it makes direct use of a continuation list.