The deforestation of a functional program is a transformation which gets rid of intermediate data structures constructions that appear when two functions are composed. The descriptional composition, initially introduced by Ganzinger and Giegerich, is a deforestation method dedicated to the composition of two attribute grammars. This article presents a new functional deforestation technique, called symbolic composition, based on the descriptional composition mechanism, but extending it. An automatic translation from a functional program into an equivalent attribute grammar allows symbolic composition to be applied, and then the result can be translated back into a functional program. This yields a source to source functional program transformation. The resulting deforestation method provides a better deforestation than other existing functional techniques. Symbolic composition, that uses the declarative and descriptional features of attribute grammars is intrinsically more powerful th...

The functional programming community is paying increasing attention to static structure-based transformations. For example, generic control operators, such as fold, have been introduced in functional programming to increase the power and applicability of a particular kind of static transformation, called deforestation, which prevents the construction of useless intermediate data structures in function composition. This is achieved by making the structure of the data more explicit in program specifications. We argue that one of the original concepts of Attribute Grammars is precisely to make data structures explicit in program specifications. Furthermore, there exists a powerful static deforestation-like transformation in their context. In this paper, we present similarities between deforestation methods, on the one hand with the functional approach, and on the other hand with the Attribute Grammars approach. In order to gain a grasp of these similarities, we first make a simple compar...