There is a wide variety of techniques that increase application performance by alleviating one or more of the most important problems with today's processors. In this work, the execution time, speedup and efficiency of the linear Apriori algorithm are shown as well as parallel with the use of OpenMP. By identifying the frequent elements of transactional databases, in processing 5 thousand records the time improves in 42,078 seconds of the algorithm with openMP compared to the sequential algorithm, in the execution 8 processor cores were used.

Agile approaches consider the software architecture as an emergent feature that can have an intended structure but could be not documented. To controlling the architectural erosion, an architectural conformance analysis could be performed, but considering the source code as a primordial input. This work presents a framework that supports the development of applications for architectural conformance analysis based on rules and measures. The framework uses aggregated graphs, MapReduce operations, and an external DSL; and has been evaluated over studies related to software architecture, and projects like the test suite of Red Hat WildFly and WordPress for Android. The gotten results favor the use of the framework for executing a continuous architectural analysis from source code.