Bridging the gap between design and implementation of components libraries

Object-oriented design is usually driven by three main reusability principles: step-by-step design, design for reuse and design with reuse. However, these principles are just partially applied to the subsequent object-oriented implementation, often due to efficienc y constraints, yielding to a gap between design and implementation. In this paper we provide a solution for bridging this gap for a concrete framework, the one of designing and implementing container-like component libraries, such as STL, Booc h Components, etc. Our approach is based on a new design pattern together with its corresponding implementation. The proposal enhances the same principles that drive the design process: step-by--step implementation (adding just what is needed in every step), implementation with reuse (component implementations are reused while library implementation progresses and component hierarchies grow) and implementation for reuse (intermediate component implementations can be reused in many different points o f the hierarchy). We use our approach in two different manners: for building a brand-new container-like component library, and for reengineering an existing one, Booch Components in Ada95.Postprint (published version

Improving design and implementation of OO container-like component libraries

Object-oriented design is usually driven by three main reusability principles: step-by-step design, design for reuse and design with reuse. However, these principles tend to be just partially applied to the subsequent object-oriented implementation, often because they conflict with other quality criteria (remarkably, efficiency). So, there is a gap between design and implementation: due to these conflicts developers use to give up design level abstractions during the implementation. In this paper we present a framework for bridging this gap for a concrete domain, the design and implementation of object-oriented container-like component libraries, such as JCF, STL, Booch Components, LEDA, etc. At the core of the framework we propose a new design pattern called emph{Shortcut} together with its corresponding implementation. The Shortcut pattern, introduced in a generic base class container, provides a secure and efficient access to items in a container decoupled from the implementation details of concrete containers. Shortcut enhances applying the same principles that drive the design process to the implementation process of these libraries: step-by-step implementation, implementation with reuse and implementation for reuse without penalising other quality criteria. Our framework not only supports the design and implementation of new libraries but also the reengineering of existing ones to overcome some of their drawbacks. We show by a case study, reengineering the Booch Components in Ada95, the application and benefits of our framework.Postprint (published version

Adding efficient and reliable access paths to the JCF

The Java Collections Framework (JCF) is the standard Java library for representing and manipulating collections (i.e., objects that represent a group of objects, such as sets, lists, etc.). Although JCF provides adequate functionality for many purposes, it does not offer any mechanism for accessing directly the objects stored in collections apart from the standard Java references. This absence is a crucial functionality exhibited by many other widespread Java and non-Java collection libraries. In this paper, we carry out a reengineering process on the JCF to add this kind of alternative access paths, which we give the name of shortcuts. This process relies on a framework called Shortcut-Based Framework, which has been defined as library- independent. We present this framework and then we show how it may be tailored to the specific case of the JCF. The resulting JCF with shortcuts library is fully compatible with the original one (i.e., programs using the original JCF are not required to be modified), and exhibits good behaviour with respect to efficiency, reliability and internal quality. As an additional benefit of the framework, we mention that it can be applied to other collection libraries, as we have done before with an Ada95 one.Postprint (published version

Shortcuts: abstract "pointers''

In this work we present the specification and the implementation of a new abstract data type (ADT) called STORE. This new ADT allows the storage of a given collection of elements offering an abstract mechanism that supplies a direct access to them, alternative to the method defined by the standard operations of usual ADTs. The interest of the new mechanism stems from the efficiency of pointers, while avoiding the loss of modularity that usually occurs when pointers are used. The implementation of the operations offered by the new ADT is done by derivation from the equational specification. The representation chosen for the implementation of the new ADT makes the efficiency previously mentioned possible, even when the representation strategy requires the movement of the elements.Postprint (published version

Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms

Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches. Constraint-based and score-based algorithms have been shown to learn the same structures when conditional independence and goodness of fit are both assessed using entropy and the topological ordering of the network is known (Cowell, 2001). In this paper, we investigate how these three classes of algorithms perform outside the assumptions above in terms of speed and accuracy of network reconstruction for both discrete and Gaussian Bayesian networks. We approach this question by recognising that structure learning is defined by the combination of a statistical criterion and an algorithm that determines how the criterion is applied to the data. Removing the confounding effect of different choices for the statistical criterion, we find using both simulated and real-world complex data that constraint-based algorithms are often less accurate than score-based algorithms, but are seldom faster (even at large sample sizes); and that hybrid algorithms are neither faster nor more accurate than constraint-based algorithms. This suggests that commonly held beliefs on structure learning in the literature are strongly influenced by the choice of particular statistical criteria rather than just by the properties of the algorithms themselves.Comment: 27 pages, 8 figure

Ciberacoso: propuesta de intervención

Treball Final del Màster Universitari en Intervenció i Mediació Familiar (Pla de 2014). Codi: SBD016. Curs acadèmic 2014-201