ABSTRACT The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. Sequential codes need to be converted to parallel versions to optimize the use of these resources. Maintaining a local infrastructure for the execution of distributed computing, through desktop grids, for example, has been replaced in favor of cloud platforms that abstract the complexity of these local infrastructures. Unfortunately, the cost of accessing these resources could leave out various studies that could be carried by a simpler infrastructure. In this article, we present a platform for distributing computer simulations on resources available on a local network using container virtualization that abstracts the complexity needed to configure these execution environments and allows any user can benefit from this infrastructure. Simulations could be developed in any programming language (such as Python, Java, C, and R) and with specific execution needs within reach of the scientific community in a general way. We will present results obtained in running simulations that required more than 1000 runs with different initial parameters and various other experiments that benefited from using the platform.

This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of a classical artificial neural network. Each vertex of this complete graph represents a possible synaptic weight set in the$w$-dimensional search space, where$w$is the number of weights of the neural network. To know the number of iterations requireda priorito obtain the solutions is one of the main advantages of the procedure. Another advantage is that the proposed method does not stagnate in local minimums. Thus, it is possible to use the quantum walk search procedure as an alternative to the backpropagation algorithm. The proposed method was employed for a$XOR$problem to prove the proposed concept. To solve this problem, the proposed method trained a classical artificial neural network with nine weights. However, the procedure can find solutions for any number of dimensions. The results achieved demonstrate the viability of the proposal, contributing to machine learning and quantum computing researches.

During the software testing process many test suites can be generated in order to evaluate and assure the quality of the products. In some cases the execution of all suites cannot fit the available resources (time, people, etc). Hence, automatic Test Case (TC) selection could be used to reduce the suites based on some selection criterion. This process can be treated as an optimization problem, aiming to find a subset of TCs which optimizes one or more objective functions (i.e., selection criteria). The majority of search-based works focus on single-objective selection. In this light, we developed mechanisms for functional TC selection which considers two objectives simultaneously: maximize requirements coverage while minimizing cost in terms of TC execution effort. These mechanisms were implemented by deploying multi-objective techniques based on Particle Swarm Optimization (PSO). Due to the drawbacks of original binary version of PSO we implemented five binary PSO algorithms and combined them with a multi-objective versions of PSO in order to create new optimization strategies applied to TC selection. The experiments were performed on two real test suites, revealing the feasibility of the proposed strategies and the differences among them.

Although software testing is a central task in the software lifecycle, it is sometimes neglected due to its high costs. Tools to automate the testing process minor its costs, however they generate large test suites with redundant Test Cases (TC). Automatic TC Selection aims to reduce a test suite based on some selection criterion. This process can be treated as an optimization problem, aiming to find a subset of TCs which optimizes one or more objective functions (i.e., selection criteria). The majority of search-based works focus on single-objective selection. In this light, we developed a mechanism for functional TC selection which considers two objectives simultaneously: maximize requirements' coverage while minimizing cost in terms of TC execution effort. This mechanism was implemented as a multi-objective optimization process based on Particle Swarm Optimization (PSO). We implemented two multi-objective versions of PSO (BMOPSO and BMOPSO-CDR). The experiments were performed on two real test suites, revealing very satisfactory results (attesting the feasibility of the proposed approach). We highlight that execution effort is an important aspect in the testing process, and it has not been used in a multi-objective way together with requirements coverage for functional TC selection.