Towards a Taxonomy of Aspect-Oriented Programming.

As programs continue to increase in size, it has become increasingly difficult to separate concerns into well localized modules, which leads to code tangling- crosscutting code spread throughout several modules. Thus, Aspect-Oriented Programming (AOP) offers a solution to creating modules with little or no crosscutting concerns. AOP presents the notion of aspects, and demonstrates how crosscutting concerns can be taken out of modules and placed into a centralized location. In this paper, a taxonomy of aspect-oriented programming, as well as a basic overview and introduction of AOP, will be presented in order to assist future researchers in getting started on additional research on the topic. To form the taxonomy, over four-hundred research articles were organized into fifteen different primary categories coupled with sub-categories, which shows where some of the past research has been focused. In addition, trends of the research were evaluated and paths for future exploration are suggested

Theory and Practice of Object Systems 3(2):75-85, 1997. Class-graph Inference for Adaptive Programs

Software generators can adapt components to changes in the architectures in which the components operate. The idea is to keep the architecture description separate and let the software generator mix it with specifications of each component. Adaptation is done by regeneration: when the architecture changes, the components are regenerated. A software component will usually be written with a particular architecture in mind. This raises the question: how much has it committed to the particular structure of that architecture? To put it in a nutshell: How flexible is a given software component? In this paper we study this question in the setting of Lieberherr’s adaptive programming. Lieberherr uses class graphs as the architecture and so-called adaptive programs as the software components. We present a polynomial-time class-graph inference algorithm for adaptive programs. The algorithm builds a representation of the set of class graphs with which a given adaptive program ca