David Fernandez-Amoros

A review of quality evaluation of digital libraries based on users’ perceptions

In the past two decades, the use of digital libraries (DLs) has grown significantly. Accordingly, questions about the utility, usability and cost of DLs have started to arise, and greater attention is being paid to the quality evaluation of this type of information system. Since DLs are destined to serve user communities, one of the main aspects to be considered in DL evaluation is the user’s opinion. The literature on this topic has produced a set of varied criteria to judge DLs from the user’s perspective, measuring instruments to elicit users’ opinions, and approaches to analyse the elicited data to conclude an evaluation. This paper provides a literature review of the quality evaluation of DLs based on users’ perceptions. Its main contribution is to bring together previously disparate streams of work to help shed light on this thriving area. In addition, the various studies are discussed, and some challenges to be faced in the future are proposed.

A bibliometric analysis of 20 years of research on software product lines

Context: Software product line engineering has proven to be an efficient paradigm to developing families of similar software systems at lower costs, in shorter time, and with higher quality.Objective: This paper analyzes the literature on product lines from 1995 to 2014, identifying the most influential publications, the most researched topics, and how the interest in those topics has evolved along the way.Method: Bibliographic data have been gathered from ISI Web of Science and Scopus. The data have been examined using two prominent bibliometric approaches: science mapping and performance analysis.Results: According to the study carried out, (i) software architecture was the initial motor of research in SPL; (ii) work on systematic software reuse has been essential for the development of the area; and (iii) feature modeling has been the most important topic for the last fifteen years, having the best evolution behavior in terms of number of published papers and received citations.Conclusion: Science mapping has been used to identify the main researched topics, the evolution of the interest in those topics and the relationships among topics. Performance analysis has been used to recognize the most influential papers, the journals and conferences that have published most papers, how numerous is the literature on product lines and what is its distribution over time.

A fuzzy linguistic model to evaluate the quality of Library 2.0 functionalities

Abstract Libraries incessantly undergo change determined by evolving user needs. These are often induced by the emergence of previously unavailable tools. Web 2.0 represents an example of such a need-shifting technology, which has led to an embrace of new user interactivity services for many library websites, thus coined Library 2.0. This paradigm shift calls for new evaluation models to include the implementation of Web 2.0 technologies. The aim of this paper is to present such a model, and to evaluate the quality of Library 2.0 functionalities, measuring the quality of the 2.0 services offered through the websites based on user perception. We adopt fuzzy linguistic modeling to represent user perception, and apply aggregation operations to linguistic labels in order to evaluate the quality of the new services. Furthermore, our model subsumes the LibQUAL+ methodology, allowing for the identification of specific 2.0 functionalities in need of improvement and of those outstandingly satisfied by the system.

Understanding the role of conceptual relations in Word Sense Disambiguation

In this article, we concentrate in conceptual relations as a source of information for Word Sense Disambiguation (WSD) systems. We start with a review the most relevant research in the field, then we implement our own algorithm. As a starting point we have chosen the conceptual density algorithm of Agirre and Rigau. We generalize the original algorithm, parameterizing many aspects. This new algorithm obtains a relative improvement of 24% in terms of precision and recall. We also offer comparative evaluation of our system with respect to the participants in the SENSEVAL-2 disambiguation competition. We conclude that conceptual relations provide a source of information that is insufficient by itself to achieve good disambiguation results, but can, however, be a very accurate heuristic in a combined system.

Improving the accuracy of COPLIMO to estimate the payoff of a software product line

Software product line engineering pursues the efficient development of families of similar products. COPLIMO is an economic model that relies on COCOMO II to estimate the benefits of adopting a product line approach compared to developing the products one by one. Although COPLIMO is an ideal economic model to support decision making on the incremental development of a product line, it makes some simplifying assumptions that may produce high distortions in the estimates (e.g., COPLIMO takes for granted that all the products have the same size). This paper proposes a COPLIMO reformulation that avoids such assumptions and, consequently, improves the accuracy of the estimates. To support our proposal, we present an algorithm that infers the additional information that our COPLIMO reformulation requires from feature diagrams, which is a widespread notation to model the domain of a product line.

Augmenting measure sensitivity to detect essential, dispensable and highly incompatible features in mass customization

Mass customization is the new frontier in business competition for both manufacturing and service industries. To improve customer satisfaction, reduce lead-times and shorten costs, families of similar products are built jointly by combining reusable parts that implement the features demanded by the customers. To guarantee the validity of the products derived from mass customization processes, feature dependencies and incompatibilities are usually specified with a variability model. As market demand grows and evolves, variability models become increasingly complex. In such entangled models it is hard to identify which features are essential, dispensable, highly required by other features, or highly incompatible with the remaining features. This paper exposes the limitations of existing approaches to gather such knowledge and provides efficient algorithms to retrieve that information from variability models.

A LITERATURE REVIEW ON FEATURE DIAGRAM PRODUCT COUNTING AND ITS USAGE IN SOFTWARE PRODUCT LINE ECONOMIC MODELS

In software product line engineering, feature diagrams are a popular means to represent the similarities and differences within a family of related systems. In addition, feature diagrams implicitly model valuable information that can be used in economic models to estimate the cost savings of a product line. In particular, this paper reviews existing proposals on computing the total number of products modeled with a feature diagram and, given a feature, the number of products that implement it. The paper also reviews the economic information that can be estimated when such numbers are known. Thus, this paper contributes by bringing together previously-disparate streams of work: the automated analysis of feature diagrams and economic models for product lines.

Exemplar driven development of software product lines

The benefits of following a product line approach to develop similar software systems are well documented. Nevertheless, some case studies have revealed significant barriers to adopt such approach. In order to minimize the paradigm shift between conventional software engineering and software product line engineering, this paper presents a new development process where the products of a domain are made by analogy to an existing product. Furthermore, this paper discusses the capabilities and limitations of different techniques to implement the analogy relation and proposes a new language to overcome such limitations.

A Scalable Approach to Exact Model and Commonality Counting for Extended Feature Models

A software product line is an engineering approach to efficient development of software product portfolios. Key to the success of the approach is to identify the common and variable features of the products and the interdependencies between them, which are usually modeled using feature models. Implicitly, such models also include valuable information that can be used by economic models to estimate the payoffs of a product line. Unfortunately, as product lines grow, analyzing large feature models manually becomes impracticable. This paper proposes an algorithm to compute the total number of products that a feature model represents and, for each feature, the number of products that implement it. The inference of both parameters is helpful to describe the standardization/parameterization balance of a product line, detect scope flaws, assess the product line incremental development, and improve the accuracy of economic models. The paper reports experimental evidence that our algorithm has better runtime performance than existing alternative approaches.

A Science Mapping Analysis of the Literature on Software Product Lines

To compete in the global marketplace, manufacturers try to differentiate their products by focusing on individual customer needs. Fulfilling this goal requires companies to shift from mass production to mass customization. In the context of software development, software product line engineering has emerged as a cost effective approach to developing families of similar products by support high levels of mass customization. This paper analyzes the literature on software product lines from its beginnings to 2014. A science mapping approach is applied to identify the most researched topics, and how the interest in those topics has evolved along the way.