Investigando o Uso da Inteligência Artificial em Projetos Python Hospedados no GitHub
Resumo
A Inteligência Artificial (IA) tem evoluído significativamente nos últimos anos. Apesar da crescente popularização da IA, será que ela também tem sido incorporada ao desenvolvimento de projetos de código-aberto nos últimos anos? Sob esta motivação, foi realizado um estudo com 15.770 repositórios Python. Os resultados mostraram que as bibliotecas em Python para a área de IA mais usadas foram TensorFlow, OpenCV e Scikit-Learn. Observou-se também que 12% dos projetos possuem pelo menos uma dependência para uma biblioteca relacionado à IA. Por fim, observou-se que os países com o maior número de projetos Python relacionados à IA são China, Estados Unidos e Alemanha.Referências
Aghili, R., Li, H., and Khomh, F. (2023). Studying the characteristics of aiops projects on github. Empirical Software Engineering, 28(6):143.
Borges, H., Hora, A., and Valente, M. T. (2016). Understanding the factors that impact the popularity of GitHub repositories. In 32nd IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 334–344.
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2023). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology.
Coelho, J. (2023). Crescendo, sobrevivendo ou morrendo? explorando a comunidade dos projetos brasileiros no github. In Anais do XX Congresso Latino-Americano de Software Livre e Tecnologias Abertas, pages 218–221. SBC.
Coelho, J., Valente, M. T., Milen, L., and Silva, L. L. (2020). Is this GitHub project maintained? measuring the level of maintenance activity of open-source projects. Information and Software Technology, 122:106274.
Coelho, J., Valente, M. T., Silva, L. L., and Shihab, E. (2018). Identifying unmaintained projects in GitHub. In 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–10.
Dakhel, A. M., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., and Jiang, Z. M. J. (2023). Github copilot ai pair programmer: Asset or liability? Journal of Systems and Software, 203:111734.
Fan, W., Zhao, Z., Li, J., Liu, Y., Mei, X., Wang, Y., Tang, J., and Li, Q. (2023). Recommender systems in the era of large language models (llms). arXiv preprint arXiv:2307.02046.
Gomes, R. M. and Baunach, M. (2019). Code generation from formal models for automatic rtos portability. In 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pages 271–272. IEEE.
Gonzalez, D., Zimmermann, T., and Nagappan, N. (2020). The state of the ml-universe: 10 years of artificial intelligence & machine learning software development on github. In Proceedings of the 17th International conference on mining software repositories, pages 431–442.
Peng, S., Kalliamvakou, E., Cihon, P., and Demirer, M. (2023). The impact of ai on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590.
Pina, D., Goldman, A., and Seaman, C. (2022). Sonarlizer xplorer: a tool to mine github projects and identify technical debt items using sonarqube. In Proceedings of the International Conference on Technical Debt, pages 71–75.
Shin, J. and Nam, J. (2021). A survey of automatic code generation from natural language. Journal of Information Processing Systems, 17(3):537–555.
Slowik, A. and Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32:12363–12379.
Tang, J. (2018). Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence Apps Using TensorFlow Mobile and Lite for IOS, Android, and Raspberry Pi. Packt Publishing Ltd.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8):1930–1940.
Wong, M.-F., Guo, S., Hang, C.-N., Ho, S.-W., and Tan, C.-W. (2023). Natural language generation and understanding of big code for ai-assisted programming: A review. Entropy, 25(6):888.
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.
Borges, H., Hora, A., and Valente, M. T. (2016). Understanding the factors that impact the popularity of GitHub repositories. In 32nd IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 334–344.
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2023). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology.
Coelho, J. (2023). Crescendo, sobrevivendo ou morrendo? explorando a comunidade dos projetos brasileiros no github. In Anais do XX Congresso Latino-Americano de Software Livre e Tecnologias Abertas, pages 218–221. SBC.
Coelho, J., Valente, M. T., Milen, L., and Silva, L. L. (2020). Is this GitHub project maintained? measuring the level of maintenance activity of open-source projects. Information and Software Technology, 122:106274.
Coelho, J., Valente, M. T., Silva, L. L., and Shihab, E. (2018). Identifying unmaintained projects in GitHub. In 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–10.
Dakhel, A. M., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., and Jiang, Z. M. J. (2023). Github copilot ai pair programmer: Asset or liability? Journal of Systems and Software, 203:111734.
Fan, W., Zhao, Z., Li, J., Liu, Y., Mei, X., Wang, Y., Tang, J., and Li, Q. (2023). Recommender systems in the era of large language models (llms). arXiv preprint arXiv:2307.02046.
Gomes, R. M. and Baunach, M. (2019). Code generation from formal models for automatic rtos portability. In 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pages 271–272. IEEE.
Gonzalez, D., Zimmermann, T., and Nagappan, N. (2020). The state of the ml-universe: 10 years of artificial intelligence & machine learning software development on github. In Proceedings of the 17th International conference on mining software repositories, pages 431–442.
Peng, S., Kalliamvakou, E., Cihon, P., and Demirer, M. (2023). The impact of ai on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590.
Pina, D., Goldman, A., and Seaman, C. (2022). Sonarlizer xplorer: a tool to mine github projects and identify technical debt items using sonarqube. In Proceedings of the International Conference on Technical Debt, pages 71–75.
Shin, J. and Nam, J. (2021). A survey of automatic code generation from natural language. Journal of Information Processing Systems, 17(3):537–555.
Slowik, A. and Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32:12363–12379.
Tang, J. (2018). Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence Apps Using TensorFlow Mobile and Lite for IOS, Android, and Raspberry Pi. Packt Publishing Ltd.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8):1930–1940.
Wong, M.-F., Guo, S., Hang, C.-N., Ho, S.-W., and Tan, C.-W. (2023). Natural language generation and understanding of big code for ai-assisted programming: A review. Entropy, 25(6):888.
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.
Publicado
30/09/2024
Como Citar
UBALDO, Luiz Andre do Nascimento; COELHO, Jailton.
Investigando o Uso da Inteligência Artificial em Projetos Python Hospedados no GitHub. In: WORKSHOP DE VISUALIZAÇÃO, EVOLUÇÃO E MANUTENÇÃO DE SOFTWARE (VEM), 12. , 2024, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 13-22.
DOI: https://doi.org/10.5753/vem.2024.3811.
