Um Modelo de Gerenciamento de Riscos para Projetos de Software com Equipes Distribuídas
DOI:
https://doi.org/10.5753/isys.2020.536Keywords:
Gerenciamento de Riscos, Identificação de Riscos, Resposta aos Riscos, Predição de Riscos, Desenvolvimento Distribuído de SoftwareAbstract
Nos últimos anos observa-se um avanço em direção à globalização dos negócios refletindo-se na área de sistemas de informação. O desenvolvimento distribuído de software (DDS) surge como uma alternativa que proporciona presença global às empresas, aproximando suas equipes dessa nova realidade. O DDS se beneficia por comportar equipes localizadas em diferentes locais geográficos, mas pode também contribuir para maior ocorrência de riscos em projetos, devido à maior complexidade de coordenação e comunicação entre os seus integrantes. O gerenciamento adequado dos riscos, através da análise dos históricos dos projetos desenvolvidos, reduz o risco quanto a desvios do planejamento em relação a tempo, custo e qualidade do projeto. Entendendo os benefícios da prática correta da disciplina de gestão de riscos, este artigo propõe um modelo, intitulado Átropos, para auxiliar as equipes a identificar e acompanhar riscos em diferentes momentos do ciclo de vida dos projetos. A avaliação do trabalho foi conduzida através de um estudo de caso em uma equipe distribuída de software, com o uso de uma base de dados histórica de projetos, com 463 projetos desenvolvidos durante os anos de 2017 e 2018.
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Sulaman, Sardar Muhammad; Weyns, Kim; Höst, Martin. (2013). A review of research on risk analysismethodos for IT systems. roceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering. Doi: 10.1145/2460999.2461013
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Ambler, S. (2012). Agility at Scale Survey: Results from the summer 2012 DDJ State of the IT Union Survey. Disponível em: http://www.ambysoft.com/surveys/stateOfITUnion201209.html. Acesso: 22/09/2018.
Audy, Jorge; Prikladnicki, Rafael. (2007). Desenvolvimento Distribuído de Software. 1 ed. Elsevier. Rio de Janeiro, Brasil, 232pp.
Barbosa, J. L. V. (2015). Ubiquitous Computing: Applications and Research Opportunities (Invited Talk). VI IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, p.1–8. Disponível: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7435625. Acesso: 22/09/2018.
Bradner, E.; Mark, G.; Hertel, T. D. (2005). Team size and technology fit: participation, Awareness,and rapport in distributed teams, In: IEEE Transactions on Professional Communication, vol. 48, no. 1, pp. 68-77. Doi: 10.1109/TPC.2004.843299
Calefato, F.; Ebert, C. (2019). Agile Collaboration for Distributed Teams. In: IEEE Software, v. 36, p. 72–78.
Dey, Anind K. (2001). Understanding and Using Context, In: Personal and Ubiquitous Computing, Vol. 5, pp. 4-7. Doi: 10.1007/s007790170019.
Engeström, Y. (2014). Learning by expanding: An activity theoretical approach to developmental research. Ed. Cambridge University Press, 338pp.
Engeström, Y.; Sannino, A. (2011). Discursive manifestations of contradictions in organizational change efforts: A methodological framework, In: Journal of Organizational Change Management, v. 24, n. 3, p. 368–387.
Fan, Zhi-Ping; Yong-Hai, Li; Zhang, Yao. (2015). Generating project risk response strategies based on CBR: A case study, In: Expert Systems with Applications. pp 2870–2883. Doi: 10.1016/j.eswa.2014.11.034.
Fang, Chao; Marle, Franck. (2012). A simulation-based risk network model for decision support in project risk management, In: Decision Support Systems. pp 635–644. Doi: 10.1016/j.dss.2011.10.021.
Filippetto, Alexsandro; Barbosa, Jorge; Francisco, Rosemary; Klein, Amarolinda. (2016). A Project Management Model based on an Activity Theory Ontology, In: Conferência Latinoamericana de Informática.
Gerhardt, Tatiana Engel; Silveira, Denise Tolfo. (2009). “Métodos de Pesquisa”, 1 ed. UFRGS, Porto Alegre, Brasil, 120pp.
Google Cloud Natural Language API Documentation | Google Cloud Natural Language API | Google Cloud ([S.d.]). Disponível: https://cloud.google.com/natural-language/docs/classifying-text. Acesso: 09/02/2019.
Jiang, J.; Wang, Z.; Yin, M. (2017). Research on impact of team conflict upon employee satisfaction in geographically distributed software development team. In: 3rd International Conference on Information Management (ICIM), Chengdu, pp. 136-139. Doi: 10.1109/INFOMAN.2017.7950362
Joia, L.; Soler, A.; Bernat, G.; Rabechini, R. (2013). Gerenciamento de riscos em projetos. Ed. FGV, São Paulo, Brasil, 172pp.
Knappmeyer, M.; Kaini, S. L.; Reetz, E. S.; Backer, N.; Tonies, R. (2013). Survey of Context Provisioning Middleware. In: IEEE Communications Surveys & Tutorials. pp. 1492–1519. Doi: 10.1109/SURV.2013.010413.00207.
Leontiev, A. N. (2019). Activity, Consciousness, and Personality. [s.l.] Prentice-Hall. Disponível: http://www.marxists.org/archive/leontev/works/1978/index.htm. Acesso: 08/01/2019.
PMBOK. (2017). Project Management Institute. Um Guia do Conhecimento em Gerenciamento de Projetos (Guia PMBOK), 6th ed. Newtown Square, PA, EUA, 762pp.
Pressman, Roger S.; Maxim, Bruce R. (2016). Engenharia de Software – Uma Abordagem Profissional, 8 ed. Grupo A Educação, São Paulo, Brasil, 968pp.
Project Management Institute. (2016) Practice Standard for Project Risk Management. Newtown Square, PA, EUA, 116pp.
Qazi, Abroon; Quigley, John; Dickson, Alex; Kirytopoulos, Konstantinos. (2016). Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in contruction projects, In: International Journal of Project Management. Doi: 10.1016/j.ijproman.2016.05.008.
Rosa, João H. da; Barbosa, Jorge L. V.; Ribeiro, Giovane D. (2016). ORACON: An Adaptive Model for Context Prediction, In: International Journal of Expert Systems with Applications. Pp. 56-70. Doi: 10.1016/j.eswa.2015.09.016.
Shrivastava, S. and Rathod, U. (2014). Categorization of risk factors for distributed agile projects. In: IEEE Software. Doi: 10.1016/j.infsof.2014.07.007.
Sommerville, Ian. (2015). “Engenharia de Software”, 10 ed. Pearson Addison-Wesley, São Paulo, Brasil, 552pp.
Sulaman, Sardar Muhammad; Weyns, Kim; Höst, Martin. (2013). A review of research on risk analysismethodos for IT systems. roceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering. Doi: 10.1145/2460999.2461013
Tianyin, P. (2011). Development of software project risk management model review. In IEEE Software. Doi: 10.1109/AIMSEC.2011.6011139.
Vygotsky, L. S. (2015). A formação social da mente. 7. ed. Martins Fontes, São Paulo, Brasil, 224pp: Martins Fontes.
Yang, Kai-Cheng; Varol, Onur; Davis, Clayton A.; Ferrara, Emilio; Flammini, Alessandro; Menczer, Filippo. (2019). Arming the public artificial intelligence to counter social bots, In: Human Behavior and Emerging Technologies. Doi: 10.1002/hbe2.115.
Zhang, Yao. (2016). Selecting risk response strategies considering project risk interdependence, In: International Journal of Project Management. Doi: 10.1016/j.ijproman.2016.03.001.
Zhang, Yao; Fan, Zhi-Ping. (2014). An optimization method for selecting project risk response strategies, In: International Journal of Project Management. pp 412–422. Doi: 10.1016/j.ijproman.2013.06.006.
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Published
2020-01-06
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Filippetto, A. S., Lima, R., & Barbosa, J. (2020). Um Modelo de Gerenciamento de Riscos para Projetos de Software com Equipes Distribuídas. ISys - Revista Brasileira De Sistemas De Informação, 13(1), 114–143. https://doi.org/10.5753/isys.2020.536
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