Developers may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and--or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns in a whole software system may be infeasible because of the required parsing time and of the subsequent needed manual validation. Detecting anti-patterns on subsets of a system could reduce costs, effort, and resources. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently some limitations: they require extensive knowledge of anti-patterns, they have limited precision and recall, and they cannot be applied on subsets of systems. To overcome these limitations, we introduce SVMDetect, a novel approach to detect anti-patterns, based on a machine learning technique---support vector machines. Indeed, through an empirical study involving three subject systems and four anti-patterns, we showed that the accuracy of SVMDetect is greater than of DETEX when detecting anti-patterns occurrences on a set of classes. Concerning, the whole system, SVMDetect is able to find more anti-patterns occurrences than DETEX.

Verification of softwarehardware hybrid systems is difficult because of the inter process communication, concurrency and synchronization and the configuration of processors. Multi-Processor Systems on Chip (MPSoC) are examples of such hybrid systems, targeted for embedded systems. We focus on the verification of MPSoC software (application running on MPSoC) and the system consisting of the application running on the MPSoC platform. We aim at generating test cases for verifying the software while overcoming the limitations of previous approaches. Metaheuristic approaches get blocked in local optima and constraint programming approaches are slow on complex programs, to satisfy given coverage criteria. We propose to combine metaheuristic and constraint programming solvers in a novel way: executing both the solvers sequentially and splitting and reducing the individual input variable domains. We also propose to generate test cases from the functional test objectives to check the proper functioning of the software running on the hardware platform. We report the results of the preliminary experiments.