Abstract
The complexity of interactions governing the coordination of loosely-coupled services, which forms the core of current software, brought behavioural issues up to the front of architectural concerns. This paper takes such a challenge seriously by lifting typical behaviour modelling techniques to the specification of both types and instances of architectural patterns in which the later ones are connected by ports that behave according to a water flow metaphor. A specific language is introduced for this purpose as well as a translator to mCRL2 so that the simulation and analysis techniques available in the corresponding toolset can be used to reason about (the behavioural layer of) software architectures. The approach is illustrated in a few examples. Copyright 2011 ACM.

Abstract
What sort of component coordination strategies emerge in a software integration process? How can such strategies be discovered and further analysed? How close are they to the coordination component of the envisaged architectural model which was supposed to guide the integration process? This paper introduces a framework in which such questions can be discussed and illustrates its use by describing part of a real case-study. The approach is based on a methodology which enables semi-automatic discovery of coordination patterns from source code, combining generalized slicing techniques and graph manipulation.

Abstract
Over the last decade component-based software development arose as a promising paradigm to deal with the ever increasing complexity in software design, evolution and reuse. Shacc is a prototyping tool for component-based systems in which components are modelled coinductively as generalized Mealy machines. The prototype is built as a Haskell library endowed with a graphical user interface developed in Swing. © 2011 Springer-Verlag.

Abstract
Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in bioinformatics and association rules mining communities to evaluate the extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioin-formatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique - statistical tests - to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. Copyright © SIAM.

Abstract
Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

Abstract
Model-Driven Engineering (MDE) is a Software Engineering approach based on model transformations at different abstraction levels. It prescribes the development of software by successively transforming models from abstract (specifications) to more concrete ones (code). Alloy is an increasingly popular lightweight formal specification language that supports automatic verification. Unfortunately, its widespread industrial adoption is hampered by the lack of an ecosystem of MDE tools, namely code generators. This paper presents a model transformation between Alloy and UML Class Diagrams annotated with OCL. The proposed transformation enables current UML-based tools to also be applied to Alloy specifications, thus unleashing its potential for MDE. © 2011 Springer-Verlag.