Strategic programming is a generic programming idiom for processing compound data such as terms or object structures. At the heart of the approach is the separation of two concerns: basic dataprocessing computations vs. traversal schemes. Actual traversals are composed by passing the former as arguments to the latter. Traversal schemes can be defined by the strategic programmer using a combinator style that relies on primitives for layered traversal. In this paper, we take a look at strategic programming from an aspect-oriented programming perspective. Throughout the paper, we compare strategic programming with adaptive programming, which is a well-established aspectual approach to the traversal of object structures. We start from the observation that aspect-oriented programming terms, e.g., crosscutting, join point, and advice can be instantiated for aspectual traversal approaches.

We present a new approach, called strategies, to the task of traversing object structures. In our approach traversals are defined using a high-level directed graph description, which is compiled into a dynamic road map to assist run-time traversals. The complexity of the compilation algorithm is polynomial in the size of the strategy graph and the class graph of the given application. The implementation allows for dynamically adding and modifying the existing traversal strategy. A prototype of the system has been developed and is being successfully used. Previous approaches to traversal specifications were less general (corresponding to either a series-parallel or a tree graph), and their compilation algorithms were of exponential complexity in some cases. In an additional result we show that this bad behavior is inherent to the static traversal code generated by previous implementations, where traversals are carried out by invoking methods without parameters. Research supported by D...