Abstract not found

We take the term X/O impedance mismatch to describe the difficulty of the OO paradigm to accommodate XML processing by means of recasting it to typed OO programming. In particular, given XML types (say, XML schemas), it is notoriously difficult to map them automatically to object types (say, object models) that (i) reasonably compare to native object types typically devised by OO developers; (ii) fully preserve the intent of the original XML types; (iii) fully support round-tripping of arbitrary, valid XML data; and (iv) provide a general and convenient programming model for XML data hosted by objects. We reveal the X/O impedance mismatch in particular detail. That is, we survey the relevant differences between XML and objects in terms of their data models and their type systems. In this process, we systematically record and assess Xto-O mapping options. Our illustrations employ XSD (1.0) as the XML-schema language of choice and C# (1.0–3.0) as the bound of OO language expressiveness.

Feature-oriented design improves reusability of objectoriented classes. Features are increments in program functionality and correspond to feature modules. Programs composed from feature modules can conflict to applications they interact with. In this paper, we introduce refactoring feature modules. Refactoring feature modules are a new combination of feature-oriented design and refactorings that adjust members and classes that are composed from feature modules. We show that refactoring feature modules reduce conflicts between applications and encourage software reuse.

Abstract. Whatever programming paradigm for data processing we choose, data has the tendency to live on the other side or to eventually end up there. The major paradigms for data processing are Cobol, object, relational and XML; each paradigm offers many facets and many versions; each paradigm provides specific forms of data models (object models, relational schemas, XML schemas, etc.). Each data-processing application depends on a horde of interrelated data models and artifacts that are derived from data models (such as data-access layers). Such conglomerations of data models are challenging due to paradigmatic impedance mismatches, performance requirements, loose-coupling requirements, and others. This ubiquitous problem calls for a good understanding of techniques for mappings between data models, actual data, and operations on data. This tutorial lists and discusses mapping scenarios, mapping techniques, impedance mismatches and research challenges regarding mappings.