Qualidade de Software para Engenheiros de IA: Um Estudo Inicial da Realidade Brasileira
Resumo
This article presents an analysis of the Brazilian reality regarding software quality for Artificial Intelligence (AI). The study seeks to investigate the software quality assurance strategies adopted during the lifecycle of AI/ML components, covering the development, integration and maintenance phases. We conducted a survey with 40 participants, and the results indicate that the Brazilian industry faces several quality issues in the development of AI systems, including accuracy, performance, interpretability and scalability. In addition, challenges related to maintainability, documentation, reuse and code quality are identified.
Palavras-chave:
Qualidade de software, Inteligência Artificial, Machine Learning
Referências
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019. Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 291–300.
Jacques Bughin, Eric Hazan, Sree Ramaswamy, Michael Chui, Tera Allas, Peter Dahlstrom, Nicolaus Henke, and Monica Trench. 2017. Artificial intelligence: The next digital frontier? (2017).
Paulo Jorge Borges da Costa. 2022. Garantia de qualidade de software (SQA). Ph. D. Dissertation.
Michael Felderer and Rudolf Ramler. 2021. Quality assurance for AI-based systems: overview and challenges. arXiv preprint arXiv:2102.05351 (2021).
Valentina Golendukhina, Valentina Lenarduzzi, and Michael Felderer. 2022. What is software quality for AI engineers? towards a thinning of the fog. In Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI. 1–9.
Mark Kasunic. 2005. Designing an effective survey.
Wang L. Zhang Z. Zhang B. Song, X. 2020. Research on the Application of Machine Learning and Artificial Intelligence in Modern Enterprise Software. In Proceedings of the 6th International Conference on Education, Language, Art and Inter-cultural Communication (ELAIC 2020). Atlantis Press, 18–23.
Mojsilović A. Varshney, K. R. 2018. Towards trustworthy AI systems: Designing AI systems that people can understand and trust. In IEEE Computer Society. IEEE, 26–35.
Jacques Bughin, Eric Hazan, Sree Ramaswamy, Michael Chui, Tera Allas, Peter Dahlstrom, Nicolaus Henke, and Monica Trench. 2017. Artificial intelligence: The next digital frontier? (2017).
Paulo Jorge Borges da Costa. 2022. Garantia de qualidade de software (SQA). Ph. D. Dissertation.
Michael Felderer and Rudolf Ramler. 2021. Quality assurance for AI-based systems: overview and challenges. arXiv preprint arXiv:2102.05351 (2021).
Valentina Golendukhina, Valentina Lenarduzzi, and Michael Felderer. 2022. What is software quality for AI engineers? towards a thinning of the fog. In Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI. 1–9.
Mark Kasunic. 2005. Designing an effective survey.
Wang L. Zhang Z. Zhang B. Song, X. 2020. Research on the Application of Machine Learning and Artificial Intelligence in Modern Enterprise Software. In Proceedings of the 6th International Conference on Education, Language, Art and Inter-cultural Communication (ELAIC 2020). Atlantis Press, 18–23.
Mojsilović A. Varshney, K. R. 2018. Towards trustworthy AI systems: Designing AI systems that people can understand and trust. In IEEE Computer Society. IEEE, 26–35.
Publicado
26/09/2023
Como Citar
SILVA, Lidiane C.; FERNANDES, Lucinara K. S.; SOARES, Eder F.; MAIA, Paulo Henrique M.; SANTOS, Ismayle S.; OLIVEIRA, Francisco C. M. B..
Qualidade de Software para Engenheiros de IA: Um Estudo Inicial da Realidade Brasileira. In: WORKSHOP BRASILEIRO DE ENGENHARIA DE SOFTWARE INTELIGENTE (ISE), 3. , 2023, Campo Grande/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 19-24.
DOI: https://doi.org/10.5753/ise.2023.235838.
