Abstract
Dublin Core schemas are the core metadata models of most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the needs of different communities with the so-called Dublin Core Application Profiles. DCAPs rely on the agreement within user communities, in a process mainly driven by their evolving needs. In this paper, we propose a complementary automated process, designed to help curators and users discover the descriptors that better suit the needs of a specific research group. We target the description of datasets, and test our approach using Dendro, a prototype research data management platform, where an experimental method is used to rank and present DC Terms descriptors to the users based on their usage patterns. In a controlled experiment, we gathered the interactions of two groups as they used Dendro to describe datasets from selected sources. One of the groups had descriptor ranking on, while the other had the same list of descriptors throughout the whole experiment. Preliminary results show that 1. some DC Terms are filled in more often than others, with different distribution in the two groups, 2. selected descriptors were increasingly accepted by users in detriment of manual selection and 3. users were satisfied with the performance of the platform, as demonstrated by a post-study survey.

Abstract

Abstract
This work describes SituMan (Situation Manager), a mobile system that makes use of the sensors commonly included in most mobile platforms and a fuzzy inference engine to attempt to infer user context and environment. Such "situation" information, has been used to enhance the behaviour of MoodBuster, another mobile application used in the scope of the mental health domain to collect Ecological Momentary Assessments (EMA). EMA has been used in psychotherapy to minimize the effects of recall bias in the assessment of patient mood, as well as in the recollection of other experiences and behaviours. SituMan can enhance the user experience in the scope of EMA by prompting users in the desired situation, instead of at random or fixed-times, thus reducing obtrusiveness. It can also provide new insight to mental health professionals by summarizing the situations experienced by the patient, further allowing correlation of situation information with patient mood within the same time frame.

Abstract
High-maturity software development processes, such as the Team Software Process and the accompanying Personal Software Process (PSP), can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, there is a lack of tool support for automating that type of analysis, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality, and productivity, to enable the automated (tool based) analysis of performance data produced by PSP developers, namely, identify and rank performance problems and their root causes. A PSP data set referring to more than 30000 projects was used to validate and calibrate the model. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
In a growing number of domains, such as ambient-assisted living (AAL) and e-health, the provisioning of end-to-end services to the users depends on the proper interoperation of multiple products from different vendors, forming a digital ecosystem. To ensure interoperability and the integrity of the ecosystem, it is important that candidate products are independently tested and certified against applicable interoperability requirements. Based on the experience acquired in the AAL4ALL project, we propose in this paper a model-based approach to systematize, automate and increase the assurance of such testing and certification activities. The approach encompasses the construction of several models: a feature model, an interface model, a product model, and unit and integration test models. The abstract syntax and consistency rules of these models are specified by means of metamodels written in UML and Alloy and automatically checked with Alloy Analyzer. Using the model finding capabilities of Alloy Analyzer, integration tests can be automatically generated from the remaining models, through the composition and instantiation of unit tests. Examples of concrete models from the AAL4ALL project are also presented.

Abstract
The growing dependence of our society on increasingly complex software systems makes software testing ever more important and challenging. In many domains, several independent systems, forming a distributed and heterogeneous system of systems, are involved in the provisioning of end-to-end services to users. However, existing test automation techniques provide little tool support for properly testing such systems. Hence, we propose an approach and toolset architecture for automating the testing of end-to-end services in distributed and heterogeneous systems, comprising a visual modeling environment, a test execution engine, and a distributed test monitoring and control infrastructure. The only manual activity required is the description of the participants and behavior of the services under test with UML sequence diagrams, which are translated to extended Petri nets for efficient test input generation and test output checking at runtime. A real world example from the Ambient Assisted Living domain illustrates the approach.