Explorando o YouTube como Fonte de Aprendizado para Desenvolvedores: Uma Análise em Larga Escala sobre Vídeos de Tecnologia
Resumo
YouTube é uma plataforma amplamente utilizada pela comunidade de desenvolvimento de software. Existem vídeos abrangendo diversos cenários e linguagens de programação, que são utilizados pelos desenvolvedores para aprendizado e atualização. Por exemplo, há vídeos sobre novas técnicas e tendências em desenvolvimento de software, uso de frameworks, configurações de ambiente e melhores práticas, dentre outros. Consequentemente, os vídeos do YouTube se tornaram recursos importantes para apoiar o desenvolvimento de sistemas de software, proporcionando acesso fácil ao conhecimento atualizado e prático. Neste contexto, neste trabalho, analisam-se aproximadamente 11 mil vídeos relacionados ao desenvolvimento de software. A análise revela diversas características dos vídeos, por exemplo, os resultados sugerem um número significativo de vídeos para iniciantes envolvendo Python, bem como vídeos populares com curta duração. Por fim, discute-se características e futuras pesquisas que podem auxiliar na criação de vídeos de programação relevantes.Referências
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Brito, A., Xavier, L., Hora, A., and Valente, M. T. (2018). APIDiff: Detecting API breaking changes. In 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), Tool Track, pages 507–511.
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Dabas, C., Kaur, P., Gulati, N., and Tilak, M. (2019). Analysis of comments on youtube videos using hadoop. In 5th International Conference on Image Information Processing (ICIIP), pages 353–358.
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Hora, A. (2021). Googling for software development: What developers search for and what they find. In 18th International Conference on Mining Software Repositories (MSR), pages 317–328.
Le, L. H. and Hancer, M. (2021). Using social learning theory in examining youtube viewers’ desire to imitate travel vloggers. Journal of Hospitality and Tourism Technology, 12(3):512–532.
Lee, C. S., Osop, H., Goh, D. H.-L., and Kelni, G. (2017). Making sense of comments on youtube educational videos: A self-directed learning perspective. Online information review, 41(5):611–625.
Lima, C., de Moraes, P. H., and Hora, A. (2018). Um estudo em larga-escala sobre características de apis populares. 6th Brazilian Workshop on Software Visualization, Evolution and Maintenance (VEM), pages 1–8.
Lopes, M. and Hora, A. (2022). How and why we end up with complex methods: A multi-language study. Empirical Software Engineering, 27:1–42.
MacLeod, L., Storey, M.-A., and Bergen, A. (2015). Code, camera, action: How software developers document and share program knowledge using youtube. In 23rd International Conference on Program Comprehension, pages 104–114.
Meyerovich, L. A. and Rabkin, A. S. (2013). Empirical analysis of programming language adoption. In International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA), page 1–18.
Nawaz, S., Rizwan, M., and Rafiq, M. (2019). Recommendation of effectiveness of youtube video contents by qualitative sentiment analysis of its comments and replies. Pakistan Journal of Science, 71(4):91.
Poché, E., Jha, N., Williams, G., Staten, J., Vesper, M., and Mahmoud, A. (2017). Analyzing user comments on youtube coding tutorial videos. In 25th International Conference on Program Comprehension (ICPC), pages 196–206.
Ponzanelli, L., Bavota, G., Mocci, A., Di Penta, M., Oliveto, R., Hasan, M., Russo, B., Haiduc, S., and Lanza, M. (2016). Too long; didn’t watch! extracting relevant fragments from software development video tutorials. In 38th International Conference on Software Engineering (ICSE), page 261–272.
Silva, D. and Valente, M. T. (2017). RefDiff: Detecting refactorings in version histories. In 14th International Conference on Mining Software Repositories (MSR), pages 269–279.
Spadini, D., Aniche, M., and Bacchelli, A. (2018). PyDriller: Python framework for mining software repositories. In 26th Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE), pages 908–911.
Storey, M.-A., Singer, L., Cleary, B., Figueira Filho, F., and Zagalsky, A. (2014). The (r) evolution of social media in software engineering. In Future of software engineering, pages 100–116.
Sureka, A. (2011). Mining user comment activity for detecting forum spammers in youtube. arXiv preprint arXiv:1103.5044.
Tian, Y., Nagappan, M., Lo, D., and Hassan, A. E. (2015). What are the characteristics of high-rated apps? a case study on free android applications. In International Conference on Software Maintenance and Evolution (ICSME), pages 301–310.
Tsantalis, N., Ketkar, A., and Dig, D. (2020). RefactoringMiner 2.0. IEEE Transactions on Software Engineering (TSE), pages 1–22.
Vánder, N., Antal, G., Hegedüs, P., and Ferenc, R. (2024). On the usefulness of python structural pattern matching: An empirical study. In International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 501–511.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in software engineering. Springer Science & Business Media.
Xavier, L., Brito, A., Hora, A., and Valente, M. T. (2017). Historical and impact analysis of api breaking changes: A large-scale study. In 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 138–147.
Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4):139–161.
Becker, F. (1992). O que é construtivismo. Revista de educação AEC, Brasília, 21(83):7–15.
Bibiano, A. C., Garcia, E. F. D. O. A., Kalinowski, M., Fonseca, B., Oliveira, R., Oliveira, A., and Cedrim, D. (2019). A quantitative study on characteristics and effect of batch refactoring on code smells. In 13th International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–11.
Braga, J. C. (2014). Objetos de Aprendizagem: Introdução e Fundamentos. UFABC.
Brito, A., Hora, A., and Valente, M. T. (2023). Towards a catalog of composite refactorings. Journal of Software: Evolution and Process, e2530.
Brito, A., Xavier, L., Hora, A., and Valente, M. T. (2018). APIDiff: Detecting API breaking changes. In 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), Tool Track, pages 507–511.
Brito, R. and Valente, M. T. (2021). RAID - Refactoring aware and intelligent diffs. In 29th International Conference on Program Comprehension (ICPC), pages 265–275.
Carvalho, H. C. F. B., Dorça, F. A., Pitangui, C. G., Assis, L. P. d., Andrade, A. V., and Trindade, E. A. C. (2022). Classificação automática de vídeos educacionais por meio de comentários apoiada por técnicas de aprendizado de máquina: uma análise experimental utilizando o youtube. Revista Brasileira de Informática na Educação, 30:419–448.
Castro, R., Classe, T., and Siqueira, S. (2022). Técnicas e tecnologias diversas no ensino remoto emergencial de engenharia de software. In II Simpósio Brasileiro de Educação em Computação (Educomp), pages 163–170.
Dabas, C., Kaur, P., Gulati, N., and Tilak, M. (2019). Analysis of comments on youtube videos using hadoop. In 5th International Conference on Image Information Processing (ICIIP), pages 353–358.
Ge, X., Sarkar, S., and Murphy-Hill, E. (2014). Towards refactoring-aware code review. In 7th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), pages 99–102. ACM.
Ge, X., Sarkar, S., Witschey, J., and Murphy-Hill, E. (2017). Refactoring-aware code review. In Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pages 71–79.
Goedert, L. and Arndt, K. B. F. (2020). Mediação pedagógica e educação mediada por tecnologias digitais em tempos de pandemia. Criar Educação, 9(2):104–121.
Hayashi, S., Thangthumachit, S., and Saeki, M. (2013). Rediffs: Refactoring-aware difference viewer for Java. In 20th Working Conference on Reverse Engineering (WCRE), pages 487–488.
Hora, A. (2021). Googling for software development: What developers search for and what they find. In 18th International Conference on Mining Software Repositories (MSR), pages 317–328.
Le, L. H. and Hancer, M. (2021). Using social learning theory in examining youtube viewers’ desire to imitate travel vloggers. Journal of Hospitality and Tourism Technology, 12(3):512–532.
Lee, C. S., Osop, H., Goh, D. H.-L., and Kelni, G. (2017). Making sense of comments on youtube educational videos: A self-directed learning perspective. Online information review, 41(5):611–625.
Lima, C., de Moraes, P. H., and Hora, A. (2018). Um estudo em larga-escala sobre características de apis populares. 6th Brazilian Workshop on Software Visualization, Evolution and Maintenance (VEM), pages 1–8.
Lopes, M. and Hora, A. (2022). How and why we end up with complex methods: A multi-language study. Empirical Software Engineering, 27:1–42.
MacLeod, L., Storey, M.-A., and Bergen, A. (2015). Code, camera, action: How software developers document and share program knowledge using youtube. In 23rd International Conference on Program Comprehension, pages 104–114.
Meyerovich, L. A. and Rabkin, A. S. (2013). Empirical analysis of programming language adoption. In International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA), page 1–18.
Nawaz, S., Rizwan, M., and Rafiq, M. (2019). Recommendation of effectiveness of youtube video contents by qualitative sentiment analysis of its comments and replies. Pakistan Journal of Science, 71(4):91.
Poché, E., Jha, N., Williams, G., Staten, J., Vesper, M., and Mahmoud, A. (2017). Analyzing user comments on youtube coding tutorial videos. In 25th International Conference on Program Comprehension (ICPC), pages 196–206.
Ponzanelli, L., Bavota, G., Mocci, A., Di Penta, M., Oliveto, R., Hasan, M., Russo, B., Haiduc, S., and Lanza, M. (2016). Too long; didn’t watch! extracting relevant fragments from software development video tutorials. In 38th International Conference on Software Engineering (ICSE), page 261–272.
Silva, D. and Valente, M. T. (2017). RefDiff: Detecting refactorings in version histories. In 14th International Conference on Mining Software Repositories (MSR), pages 269–279.
Spadini, D., Aniche, M., and Bacchelli, A. (2018). PyDriller: Python framework for mining software repositories. In 26th Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE), pages 908–911.
Storey, M.-A., Singer, L., Cleary, B., Figueira Filho, F., and Zagalsky, A. (2014). The (r) evolution of social media in software engineering. In Future of software engineering, pages 100–116.
Sureka, A. (2011). Mining user comment activity for detecting forum spammers in youtube. arXiv preprint arXiv:1103.5044.
Tian, Y., Nagappan, M., Lo, D., and Hassan, A. E. (2015). What are the characteristics of high-rated apps? a case study on free android applications. In International Conference on Software Maintenance and Evolution (ICSME), pages 301–310.
Tsantalis, N., Ketkar, A., and Dig, D. (2020). RefactoringMiner 2.0. IEEE Transactions on Software Engineering (TSE), pages 1–22.
Vánder, N., Antal, G., Hegedüs, P., and Ferenc, R. (2024). On the usefulness of python structural pattern matching: An empirical study. In International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 501–511.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in software engineering. Springer Science & Business Media.
Xavier, L., Brito, A., Hora, A., and Valente, M. T. (2017). Historical and impact analysis of api breaking changes: A large-scale study. In 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 138–147.
Publicado
07/04/2025
Como Citar
PAULA, Gabriel Victor de; GUERRA, João Victor; SOUZA, Luís Antônio; SOARES, Luiz Gustavo; MACHADO, Pedro Henrique; BRITO, Aline; XAVIER, Laerte.
Explorando o YouTube como Fonte de Aprendizado para Desenvolvedores: Uma Análise em Larga Escala sobre Vídeos de Tecnologia. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 5. , 2025, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 72-84.
DOI: https://doi.org/10.5753/educomp.2025.4930.
