- Maestría profesional en Computación e Informática. Universidad de Costa Rica, 2020. (En curso)
- Licenciatura en Computación e Informática (con honores). Universidad de Costa Rica, 2020.
- Bachillerato en Ingeniería en Computación (con honores). Universidad de Costa Rica, 2018
- Administrador de Recursos Informáticos Desconcentrados (RID), Centro de Investigaciones en Tecnologías de la Información y Comunicación (CITIC), Universidad de Costa Rica. Desde 2019 al Presente
- Administrador de Recursos Informáticos Desconcentrados (RID), Programa de Investigación en Desarrollo Urbano Sostenible (ProDUS), Universidad de Costa Rica. Desde 2018 al 2020
DevOps is a set of software engineering practices that combine efforts from development and operations areas, with the aim of improving delivery time and software quality. The goal of this study is to characterize DevOps practices used by organizations that develop software. For this, we performed a systematic literature mapping covering the period 2015-2019. In total, 42 primary articles were included and analyzed. We identified and classified a total of 20 DevOps practices, 18 criteria to evaluate DevOps practices, 16 benefits and 19 challenges related to DevOps’ adoption. Our results show the need for more empirical studies in organizations, which directly address issues like evaluation criteria to assess the operation of DevOps practices.
Tipo de publicación: Journal Article
Publicado en: Revista Ibérica de Sistemas e Tecnologias de Informação
Smart agriculture is an active research field. Currently, many researchers are working on the construction of platforms directed to improve efficiency, crop processes, and data awareness. However, it is common that developers focus on data monitoring instead of the data relevance for decision making or the costs associated with the creation of monitoring platforms. In this paper, we present a classification of the data required by researchers on the construction of decision-support systems applied to smart agriculture processes. By using this classification, the user can define which data are relevant according to the characteristics of the problem that needs to be solved.We have applied the classification to data recollected in a study case conducted by the end of last year. Besides, we identify a list of agronomic and climatic variables commonly used in the construction of decision support systems. We apply the classification to this list of variables as an example for researchers. As a conclusion, this typification permits the researcher to identify data that has to be monitored and controlled, and data that does not have to be measured, the later based on the data characteristics and utility for the farmer.
Tipo de publicación: Conference Paper
Publicado en: 2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)