Abstract

Abstract
Selecting the right set of descriptors for the annotation of a specific dataset can be a hard problem in research data management. Considering a dataset in an arbitrary domain, an application profile is complex to build because of the abundance of metadata standards, ontologies and other descriptor sources available for different domains. We propose to partially automate the process of data description by generating application profile recommendations based on a research data asset knowledge base. Our approach builds on existing technologies for exploring linked data and results in a process which can be tightly coupled with the research workflow, giving researchers more control over the description of their data. Preliminary experiments show that we can build on state-of-the-art technologies for search indexes, graph databases and triple stores to explore existing sources of linked data for our profile generation.

Abstract

Abstract
High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Personal Software Process (PSP) and the Team Software Process (TSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. Currently, there are several tools that automate data collection and produce performance charts for manual analysis in the context of the PSP/TSP, but practically no tool support exists for automating the data analysis and the recommendation of improvement actions. Manual analysis of this performance data is problematic because of the large amount of data to analyze and the time and expertise required. Hence, we propose in this paper a performance model and a tool (named PSP PAIR) to automate the analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and recommend improvement actions. The work presented is limited to the analysis of the time estimation performance of PSP developers, but is extensible to other performance indicators and development processes.

Abstract
This paper proposes a new approach to reduce the effort of building formal models representative of the structure and behaviour of Graphical User Interfaces (GUI). The main goal is to automatically extract the GUI model with a dynamic reverse engineering process, consisting in an exploration phase, that extracts information by interacting with the GUI, and in a model generation phase that, making use of machine learning techniques, uses the extracted information of the first step to generate a state-machine model of the GUI, including guard conditions to remove ambiguity in transitions. © 2012 IEEE.

Abstract
Model-driven engineering approaches aim at avoiding productivity, model quality and model maintenance problems that arise when models are used for documentation only, by generating executable applications from models. However, in many cases, the level of detail of the models needed to generate complete applications is too much or only effective for specific domains. For those cases where it is not practical to build complete models and generate complete applications from them, we propose a lightweight approach, applicable at different levels (unit, integration and system testing), that combines partial application generation from structural models with test generation from partial behavioral models. To demonstrate the approach, we developed a plug-in that adds to the code generation capabilities of an existing UML modeling tool, the capability of generating executable tests from sequence diagrams acting also as parameterized test scenarios, including some novel features as compared to existing model-based testing tools.