Miguel Goulão

Domain-Specific Languages (DSLs) developers aim to narrow the gap between the level of abstraction used by domain users and the one provided by the DSL, in order to help taming the increased complexity of computer systems and real-world problems. The quality in use of a DSL is essential for its successful adoption. We illustrate how a usability evaluation process can be weaved into the development process of a concrete DSL - FlowSL - used for specifying humanitarian campaign processes lead by an international Non-Governmental Organization. FlowSL is being developed following an agile process using Model-Driven Development (MDD) tools, to cope with vague and poorly understood requirements in the beginning of the development process.

Goal-oriented Requirements Engineering approaches have become popular in the Requirements Engineering community as they provide expressive model elements for requirements elicitation and analysis. However, as a common challenge, they are still struggling when it comes to managing the accidental complexity of their models. In this paper, we provide a set of metrics, which are formally specified and have tool support, to measure and analyze the complexity of goal models, in particular i* models. The aim is to identify refactoring opportunities to improve the modularity of those models, and consequently reduce their complexity. We evaluate these metrics by applying them to a set of well-known case studies from industry and academia. Our results allow the identification of refactoring opportunities in the evaluated models.

Helping users to find useful contacts or potentially interesting subjects is a challenge for social and productive
networks. The evidence of the content produced by users must be considered in this task, which may be simplified by the use of the meta-data associated with the content, i.e., the categorization supported by the network – descriptive keywords, or tags. In this paper we present a model that enables keyword discovery
methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid content analysis, and move towards a generic approach to the identification of relevant interests and, eventually,
contacts. The evaluation of the model and methods is executed by two experiments that perform frequency and classification analyses over the Flickr network. The results show that we can efficiently recommend keywords to users.