Mestrado em Ciência da Computação (INF)

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Navegando Mestrado em Ciência da Computação (INF) por Por Orientador "Camilo Júnior, Celso Gonçalves"

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    FreeTest 2.0: uma evolução do método FreeTest para a melhoria no processo de teste de software em micro e pequenas empresas
    (Universidade Federal de Goiás, 2017-08-11) Louzada, Jailton Alkimin; Camilo Júnior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Vincenzi, Auri Marcelo Rizzo; Rodrigues, Cássio Leonardo
    The Information Technology (I.T) market is growing. In the age of information, world economies have been investing more and more in the services market. Within this competitive scenario, software testing is an important component for raising the quality of software developed in Brazil and in the global competitiveness cenario. However, Small and medium-sized enterprises (SMEs) have limited resources for investments in software testing maturity processes, tools and models in their business. Faced with this, this work has as main proposal to produce an apparatus to improve the process of software testing for SMEs. Objective: As the main objectives, this study proposed a more update version of the FreeTest 1.0 process, as well as practical instructions on how to implement the activities suggested in the process, all this formatted in a new process and a wizard respectively. Methodology: In order to fulfill the objectives of this work, the FreeTest 2.0 process was created as an improvement of the FreeTest Method process, focused mainly on Agile techniques, DevOps and aligned with the SMEs ecosystem. And the FreeTest Wizard, which consists of a deployment guide that supports the implementation of the process in a didactic, dynamic and flexible way. Another contribution of this work was the creation of support tools to disseminate this knowledge and content management, in this case the creation of a web platform, distributed free of charge and in the format "as a Service". Finally, the results and conclusions can be seen in the final chapter of this work.
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    Uma abordagem evolucionária para o teste de instruções select SQL com o uso da análise de mutantes
    (Universidade Federal de Goiás, 2013-08-02) Monção, Ana Claudia Bastos Loureiro; Rodrigues, Cássio Leonardo; Camilo Júnior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Leitão Júnior, Plínio de Sá; Rodrigues, Cássio Leonardo; Souza, Jerffeson Teixeira de
    Software Testing is an important area of Software Engineering to ensuring the software quality. It consists of activities that involve long time and high costs, but need to be made throughout the process of building software. As in other areas of software engineering, there are problems in the activities of Software Testing whose solution is not trivial. For these problems, several techniques of optimization and search have been explored trying to find an optimal solution or near optimal, giving rise to lines of research textit Search-Based Software Engineering (SBSE) and textit Search-Based Software Testing (SBST). This work is part of this context and aims to solve the problem of selecting test data for test execution in SQL statements. Given the number of potential solutions to this problem, the proposed approach combines techniques Mutation Analysis for SQL with Evolutionary Computation to find a reduced data set, that be able to detect a large number of defects in SQL statements of a particular application. Based on a heuristic perspective, the proposal uses Genetic Algorithms (GA) to select tuples from a existing database (from production environment) trying to reduce it to a set of data relevant and effective. During the evolutionary process, Mutation Analysis is used to evaluate each set of test data selected by the AG. The results obtained from the experiments showed a good performance using meta-heuristic of Genetic Algorithms, and its variations.
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    PupRN: um método para diagnóstico de anormalidades oftalmológicas em recém-nascido baseado na dinâmica pupilar
    (Universidade Federal de Goiás, 2021-07-29) Silva, Marcos Vinicius Ribeiro; Camilo Júnior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Naves, Eduardo Lázaro Martins; Rosa, Thierson Couto
    Vision is one of the human senses that help development since birth, being of paramount importance for cognitive, social, and motor skills. The World Health Organization (WHO) points out that the number of children with ophthalmic abnormalities should increase by about 200 million between 2000 and 2050. Dynamic pupillometry is an exam that captures immutable pupillary behavior, such as its change in involuntary size, aiming to diagnose eye disorders and diseases. Since these pathologies being severe in children and the potential of pupillometry analysis for their diagnosis, this work proposes a method for diagnosing ophthalmic abnormalities using machine learning techniques and intelligent algorithms. Thus, the method autonomously extracts pupillary information from pupillometry exams and applies a classifier model to distinguish newborns between normal and altered clinical conditions within the ophthalmological context. This model intends to be a trial screening method that could help health professionals diagnose newborns' ophthalmological abnormalities. In addition, an annotated benchmark, which was manually developed in this study, is available and presents the context and highlights the obstacles in working with pupillometry exams in newborns. The algorithms proposed by this work were evaluated and compared with the ElSe and ExCuSe algorithms, state-of-the-art algorithms in the subject of pupillary tracking applied to the scope of this study. In conclusion, it presented a classifier model capable of differentiating newborns with diseased diagnosis in the ophthalmic field with an accuracy close to 81% under the available dataset.
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    Novel fitness functions using source code checkpoints for search-based program repair
    (Universidade Federal de Goiás, 2020-01-16) Souza, Eduardo Faria de; Camilo Júnior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Leitão Júnior, Plínio de Sá; Barros, Márcio de Oliveira
    Software maintenance, especially bug fixing, is one of the most expensive problems in software practice. Bugs have a global impact in terms of cost and time, and they also reflect negatively on a company’s brand. GenProg is a method for Automated Program Repair based on an evolutionary approach. It aims to generate bug repairs with neither human intervention nor a need for special instrumentation or source code annotations. Its canonical fitness function evaluates each variant as the weighted sum of the test cases that a modified program passes. However, it evaluates individuals with distinct genetic material with the same observed fitness score (plateaus). We proposed five novel fitness functions, four of which use a dynamic analysis technique called intermediate code checkpoints to collect variable’s state data, while the other uses a random component. They aim to increase the granularity of the fitness gleaning more data from test case execution. We evaluate the proposed fitness functions with mature open-source projects with thousands of lines of code from the IntroClass and ManyBugs benchmark. We found that our proposed fitness functions minimize plateaus, increasing the differentiation capabilities of the search, and they also find more repairs than the baseline, including repairs not previously found.