Towards a Grounded Theory for a Development Process Model for Machine Learning Based Systems
Resumo
The software industry has experienced the integration of artificial intelligence capabilities into applications, facing new challenges regarding software development. Despite research and industry contributions providing lessons learned and best practices, no study proposed a reference process for developing this type of software, and practitioners still struggle to establish a working process. Through a Grounded Theory study involving practitioners with experience in machine learning (ML) projects, this paper presents an emerging theory of how ML-based systems are developed. The reported results comprise key elements of a reference development process with its respective phases and activities.
Palavras-chave:
Machine Learning Systems, Software Development Process, Software Engineering, Intelligent Software Engineering
Referências
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Kathy Charmaz. 2006. Constructing grounded theory: A practical guide through qualitative analysis. SAGE Publications, London.
Charles Hill, Rachel Bellamy, Thomas Erickson, and Margaret Burnett. 2016. Trials and tribulations of developers of intelligent systems: A field study. In 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 162–170.
Rukayya Sunusi Alkassim Ilker Etikan, Sulaiman Abubakar Musa. 2015. Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics 5, 1 (2015), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Mirko Perkusich, Lenardo Chaves e Silva, Alexandre Costa, Felipe Ramos, Renata Saraiva, Arthur Freire, Ednaldo Dilorenzo, Emanuel Dantas, Danilo Santos, Kyller Gorgônio, et al. 2020. Intelligent software engineering in the context of agile software development: A systematic literature review. Information and Software Technology 119 (2020), 106241.
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Klaas-Jan Stol, Paul Ralph, and Brian Fitzgerald. 2016. Grounded theory in software engineering research: a critical review and guidelines. In Proceedings of the 38th International Conference on Software Engineering. 120–131.
Anselm Strauss and Juliet Corbin. 1990. Basics of qualitative research. Sage publications.
ClaesWohlin and Aybüke Aurum. 2015. Towards a decision-making structure for selecting a research design in empirical software engineering. Empirical Software Engineering 20, 6 (2015), 1427–1455.
Tao Xie. 2018. Intelligent software engineering: Synergy between AI and software engineering. In International Symposium on Dependable Software Engineering: Theories, Tools, and Applications. Springer, 3–7.
Kathy Charmaz. 2006. Constructing grounded theory: A practical guide through qualitative analysis. SAGE Publications, London.
Charles Hill, Rachel Bellamy, Thomas Erickson, and Margaret Burnett. 2016. Trials and tribulations of developers of intelligent systems: A field study. In 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 162–170.
Rukayya Sunusi Alkassim Ilker Etikan, Sulaiman Abubakar Musa. 2015. Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics 5, 1 (2015), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Mirko Perkusich, Lenardo Chaves e Silva, Alexandre Costa, Felipe Ramos, Renata Saraiva, Arthur Freire, Ednaldo Dilorenzo, Emanuel Dantas, Danilo Santos, Kyller Gorgônio, et al. 2020. Intelligent software engineering in the context of agile software development: A systematic literature review. Information and Software Technology 119 (2020), 106241.
Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich. 2017. Data management challenges in production machine learning. In Proceedings of the 2017 ACM International Conference on Management of Data. 1723–1726.
Nayan B. Ruparelia. 2010. Software Development Lifecycle Models. SIGSOFT Softw. Eng. Notes 35, 3 (May 2010), 8–13. https://doi.org/10.1145/1764810.1764814
Klaas-Jan Stol, Paul Ralph, and Brian Fitzgerald. 2016. Grounded theory in software engineering research: a critical review and guidelines. In Proceedings of the 38th International Conference on Software Engineering. 120–131.
Anselm Strauss and Juliet Corbin. 1990. Basics of qualitative research. Sage publications.
ClaesWohlin and Aybüke Aurum. 2015. Towards a decision-making structure for selecting a research design in empirical software engineering. Empirical Software Engineering 20, 6 (2015), 1427–1455.
Tao Xie. 2018. Intelligent software engineering: Synergy between AI and software engineering. In International Symposium on Dependable Software Engineering: Theories, Tools, and Applications. Springer, 3–7.
Publicado
28/09/2021
Como Citar
MEIRELES, André; CARVALHO, Rainara M.; RIQUE, Thiago; CAVALCANTE, Marizangela B. P.; PERKUSICH, Mirko; ALMEIDA, Hyggo; PERKUSICH, Angelo.
Towards a Grounded Theory for a Development Process Model for Machine Learning Based Systems. In: WORKSHOP BRASILEIRO DE ENGENHARIA DE SOFTWARE INTELIGENTE (ISE), 1. , 2021, Joinville.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 19-24.
DOI: https://doi.org/10.5753/ise.2021.17278.
