The process of writing model transformations is a complex and error-prone one. Thus, efficient techniques and tools for validating model transformations are needed. One of them is model transformation testing. The generation of test cases for model transformations is mainly based on metamodel and rules coverage criteria. In this paper, we propose to treat model transformation testing as a bi-level optimization problem to combine the generation of test cases with mutation testing. In our adaptation, the upper-level problem generates a set of test cases that maximizes the coverage of metamodels and errors introduced by the lower level to the transformation rules. The lower level maximizes the number of generated errors in the rules that cannot be detected by the test cases produced by the upper level. The main advantage of our bi-level formulation is that the evaluation of test cases is not limited to the coverage of metamodels, but it allows evaluating their ability to detect errors. The statistical analysis of our experiments on different transformation mechanisms confirms the outperformance of our bi-level proposal compared with state-of-the-art model transformation testing techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Code smells represent design situations that can affect the maintenance and evolution of software. They make the system difficult to evolve. Code smells are detected, in general, using quality metrics that represent some symptoms. However, the selection of suitable quality metrics is challenging due to the absence of consensus in identifying some code smells based on a set of symptoms and also the high calibration effort in determining manually the threshold value for each metric. In this article, we propose treating the generation of code-smell detection rules as a bilevel optimization problem. Bilevel optimization problems represent a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, only the optimal solutions to the lower-level problem become possible feasible candidates to the upper-level problem. In this sense, the code-smell detection problem can be treated as a bilevel optimization problem, but due to lack of suitable solution techniques, it has been attempted to be solved as a single-level optimization problem in the past. In our adaptation here, the upper-level problem generates a set of detection rules, a combination of quality metrics, which maximizes the coverage of the base of code-smell examples and artificial code smells generated by the lower level. The lower level maximizes the number of generated artificial code smells that cannot be detected by the rules produced by the upper level. The main advantage of our bilevel formulation is that the generation of detection rules is not limited to some code-smell examples identified manually by developers that are difficult to collect, but it allows the prediction of new code-smell behavior that is different from those of the base of examples. The statistical analysis of our experiments over 31 runs on nine open-source systems and one industrial project shows that seven types of code smells were detected with an average of more than 86% in terms of precision and recall. The results confirm the outperformance of our bilevel proposal compared to state-of-art code-smell detection techniques. The evaluation performed by software engineers also confirms the relevance of detected code smells to improve the quality of software systems.

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.