CV Lattes
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
I graduated in Computer Science from the Universidade Federal de Goiás (2010), and obtained my master's and doctorate degrees in Computer Science and Computational Mathematics from the Universidade de São Paulo (ICMC-USP) in 2013 and 2017, respectively. Between July and December 2016, I worked in a research internship abroad program in collaboration with the University of Oulu (Finland) and the Blekinge Institute of Technology (Sweden). I am currently working as a Assistant Professor at the Departamento de Computação e Eletrônica - CEUNES - UFES. I have spent my time on teaching, self-knowledge, and innovation. I currently hold the position of Innovation Coordinator at the CEUNES campus, linked to the UFES Innovation Superintendence. Since 2019, I have been teaching the subject Innovation and Entrepreneurship, where I was able to contribute as a mentor [directly or indirectly] in more than 35 innovation projects. Since 2018, I have also been teaching the subject Computing and Society, where I work on the aspects of the individual [and organizations] that are capable of promoting a more effective and empathetic society. At the beginning of my career, I researched Software Defect Prediction, Complex Network Theory, Software Testing, Evolutionary Algorithms and Metaheuristics. (Source: Lattes Curriculum)
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Software testing is considered one of the most important activities in the process of building a software since it adds more quality to the product generated. Advances on software testing research aim to develop concepts, methods and techniques that reduce the costs of implementation and generation of the test suite, and improve its efficiency in identifying defects. This suite is constru…
Defect prediction models can be a good tool to organize the test resources of a project. However, not all companies maintain an appropriate dataset of defects, forcing them to build it from known external projects. This approach, called Cross-project Defect Prediction (CPDP), solves the lack of defect data, although introduces heterogeneity on data. This heterogeneity can compromises the …
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