Atribuições e Competências Individuais do Scrum Master: Um Estudo Exploratório
Resumo
A necessidade de entregas rápidas que agreguem o máximo valor de negócio possível ao produto tem ganhado destaque no atual cenário de desenvolvimento de software. No contexto de processos de gerenciamento ágil de projetos baseados no Scrum, um dos papeis essenciais é o do Scrum Master. Torna-se importante, portanto, compreender como este papel está sendo percebido pelos praticantes de desenvolvimento de software, a fim de verificar elementos que estão além do que é proposto no Scrum. Com este intuito, este artigo apresenta um estudo exploratório de natureza qualitativa, realizado para identificar as principais atribuições e competências inerentes ao papel do Scrum Master sob o ponto de vista de profissionais que atuam em equipes de desenvolvimento no setor público. A pesquisa identificou 20 diferentes atribuições do Scrum Master com relação ao Product Owner, ao Time e à organização, e também, 12 competências consideradas importantes para o papel.
Palavras-chave:
Scrum Master, Scrum, Metodologia Ágil, Competências, Atribuições
Referências
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Ahmed, K.; El Tazi, N.; Hossny, A. H. Sentiment Analysis Over Social Networks: An Overview.
Altrabsheh, N., Cocea, M., & Fallahkhair, S. 2014. Learning sentiment from students’ feedback for real-time interventions in classrooms. In: Adaptive and Intelligent Systems. Springer International Publishing. p. 40-49.
Araújo, M. et al. 2013. Métodos para análise de sentimentos no Twitter. In: Proceedings of the 19th Brazilian symposium on Multimedia and the Web (WebMedia'13).
Bosco, C.; Patti, V.; Bolioli, A. 2013. Developing corpora for sentiment analysis: The case of irony and senti-tut. IEEE Intelligent Systems, No. 2, p. 55-63.
Cambria, E.; Hussain, A. 2015. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Springer. Vol 1. Disponível em:
Cambria, E; Livingstone A.; Hussain A. (2012) The Hourglass of Emotions. In: LNCS, Vol 7403. p. 114- 157. Springer. Disponível em:
Cambria, E.; Grassi, M.; Hussain A.; Havasi C. 2012. Sentic Computing for social media marketing. Springer.
Cervi, E. U., & Massuchin, M. G. 2012. Redes sociais como ferramenta de campanha em disputas subnacionais: análise do Twitter nas eleições para o governo do Paraná em 2010. Sociedade e Cultura, Vol. 15, No. 1.
Chamlertwat, W. et al. 2012. Discovering Consumer Insight from Twitter via Sentiment Analysis. J. UCS, Vol. 18, No. 8, p. 973-992.
de Oliveira, Aletheia Machado. Redes Sociais Virtuais, Blog, Wiki e Moocs, como parte de uma arquitetura pedagógica. 2016. Rehutec, Vol 5, No. 1, p12.
Duwairi, R. M. 2015. Sentiment analysis for dialectical Arabic. In:Information and Communication Systems (ICICS), 2015 6th International Conference on. IEEE, p. 166-170.
Huangfu, Y. et al. 2015. An improved sentiment analysis algorithm for Chinese news. In: Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on. IEEE. p. 1366-1371.
Kechaou, Z.; Ben Ammar, M.; Alimi, A. M. 2011. Improving e-learning with sentiment analysis of users' opinions. In: Global Engineering Education Conference (EDUCON), 2011 IEEE. IEEE, p. 1032-1038.
Ortigosa, A.; Martín, J. M.; Carro, R. M. 2014. Sentiment analysis in Facebook and its application to elearning. Computers in Human Behavior, Vol. 31, p. 527-541.
Liu, B. 2012. Sentiment analysis and opinion mining.Synthesis lectures on human language technologies, Vol. 5, No. 1, p. 1-167.
Mendes Gomes L et al. 2015. Facebook vs moodle: Surveying university students on the use of learning management systems to support learning activities outside the classroom. In: Information Systems and Technologies (CISTI), 2015 10th Iberian Conference on. IEEE, p. 1-4.
Montoyo, A.; MartíNez-Barco, P. ; Balahur, A. 2012. Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments.Decision Support Systems, Vol. 53, No. 4, p. 675-679.
Moreno, A. et al. 2011. Feeling bad on Facebook: Depression disclosures by college students on a social networking site. Depression and anxiety, Vol. 28, No. 6, p. 447-455.
Nasukawa, T.; Yi, J. 2003. Sentiment analysis: Capturing favorability using natural language processing. In:Proceedings of the 2nd international conference on Knowledge capture. ACM. p. 70-77.
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science, Vol. 240, No. 4857, p. 1285-1293.
Thelwall, M.; Buckley K.; Paltoglou G. 2012. Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology. Vol. 63, No. 1, p. 163-173.
Wang, H. et al. 2012. A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations. Association for Computational Linguistics, p. 115-120.
Waters, R. D. et al. 2009. Engaging stakeholders through social networking: How nonprofit organizations are using Facebook. Public relations review, Vol. 35, No. 2, p. 102-106.
Publicado
17/05/2016
Como Citar
BERNARDO, João Helis J. A.; DIAS JR, José Jorge Lima; CUNHA, José Adson O. G..
Atribuições e Competências Individuais do Scrum Master: Um Estudo Exploratório. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 12. , 2016, Florianópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 470-477.
DOI: https://doi.org/10.5753/sbsi.2016.5996.
