Business modeling and requirements in RUP: a dependency analysis of activities, tasks and work products

Most artifacts developed during the requirements engineering process relate themselves in different ways. In order to understand in detail how they affect each other during the software development process, it is relevant to iden-tify their interdependencies. This paper presents a systematization of the existing interdependencies between the different elements of the Rational Unified Process (RUP) in the Business Modeling and Requirements disciplines. This work, which highlights knowledge about the different interdependencies and traceability of RUP elements, is useful to avoid unconscious decisions during software the de-velopment process and also, to detect potential problems due to the violation of the existing interdependencies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e a Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Traceability mappings as a fundamental instrument in model transformations

Technological importance of traceability mappings for model transformations is well-known, but they have often been considered as an auxiliary element generated during the transformation execution and providing accessory information. This paper argues that traceability mappings should instead be regarded as a core aspect of the transformation definition, and a key instrument in the transformation management. We will show how a transformation can be represented as the result of execution of a metamodel mapping, which acts as a special encoding of the transformation definition. Since mappings enjoy Boolean operations (as sets of links) and sequential composition (as sets of directed links), encoding transformations by mappings makes it possible to define these operations for transformations as well, which can be useful for model transformation reuse, compositional design, and chaining

Using semantic web to establish traceability links between heterogeneous artifacts

Semantic Web enables the users of the World Wide Web (WWW) to create non-traditional data repositories. The data can be linked in a flat hierarchy structure that allows the extensibility of data without the need for changing the structure itself. The linked data along with other rules can be used to infer or extract other data. We propose a semantic web technique that employs the Resource Description Framework (RDF) for building a trace links taxonomy. The taxonomy can be utilized to link heterogeneous artifacts coming from different domains of expertise. This technique allows users to refer to any trace link type in the taxonomy using a unique Uniform Resource Identifier (URI). The taxonomy can also be integrated to a traceability framework using the Open Service for Lifecycle Collaboration (OSLC) in order to accommodate the traceability of heterogeneous artifacts. We present validation criteria for validating the taxonomy requirements and validate the solution through a set of test cases. A simple case study is used in order to provide meaningful results

Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed

Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic intercellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text-mining engine that structures and standardizes knowledge of immune intercellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method is able to distinguish between incoming and outgoing interactions, and it includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. By leveraging the breadth of this network, we predicted and experimentally verified previously unappreciated cell-cytokine interactions. We also built a global immune-centric view of diseases and used it to predict cytokine-disease associations. This standardized knowledgebase (http://www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology