Abstract
Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.

Abstract
This paper proposes a set of metrics for the assessment of the complexity of models defining the business logic of spreadsheets. This set can be considered the first step in the direction of building a quality standard for spreadsheet models, that is still to be defined. The computation of concrete metric values has further been integrated under a well-established model-driven spreadsheet development environment, providing a framework for the analysis of spreadsheet models under spreadsheets themselves.

Abstract
This paper presents a framework for the analysis of software artifacts. We revise and propose techniques that aid in the manipulation and combination of target-language specific tools, and in handling and controlling the results of such tools. We also propose to integrate under our framework techniques that are capable of performing language independent analyses. The final result of our work is an analysis environment that is modular and flexible and that allows easy and elegant implementations of complex analysis suites. We finally conduct a proof of concept for our framework by analyzing a well-known, widely used open-source software package.

Abstract
The analytical methods based on evaluation models of interactive systems were proposed as an alternative to user testing in the last stages of the software development due to its costs. However, the use of isolated behavioral models of the system limits the results of the analytical methods. An example of these limitations relates to the fact that they are unable to identify implementation issues that will impact on usability. With the introduction of model-based testing we are enable to test if the implemented software meets the specified model. This paper presents an model-based approach for test cases generation from the static analysis of source code.

Abstract
Spreadsheets are being used with many different purposes that range from toy applications to complete information systems. In any of these cases, they are often used as data repositories that can grow significantly. As the amount of data grows, it also becomes more difficult to extract concrete information out of them. This paper focuses on the problem of spreadsheet querying. In particular, we propose an expressive and composable technique where intuitive queries can be defined. Our approach builds on a model-driven spreadsheet development environment, and queries are expressed referencing entities in the model of a spreadsheet instead of in its actual data. Finally, the system that we have implemented relies on Google's query function for spreadsheets.

Abstract