PLN e MSR em Repositórios Open Source de Criptomoedas: Um Estudo de Caso no Ethereum
Resumo
Este estudo propõe uma abordagem integrada de Processamento de Linguagem Natural (PLN) e Mineração de Repositórios de Software para caracterizar padrões linguísticos e operacionais em issues do repositório oficial da Ethereum no GitHub, investigando sua associação com variações de curto prazo no prec¸o da Ether (ETH). A abordagem combina BERTopic, embeddings de sentenc¸as com MiniLM, clusterização com KMeans e métricas de sentimento com VADER e TextBlob. Os resultados preliminares indicam que textos curtos e especializados limitam a separabilidade temática, enquanto a clusterização por embeddings revela perfis operacionais associados ao tamanho das mensagens, ao perfil de autoria e ao tempo de resolução das issues. As análises de sentimento e de eventos não indicaram associação substantiva com retornos de curto prazo da ETH.
Palavras-chave:
Processamento de Linguagem Natural, Mineração de Repositórios de Software, Blockchain, Ethereum, GitHub
Referências
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Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., and Zhou, M. (2020). Codebert: A pre-trained model for programming and natural languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1536–1547. ACL.
Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
Hassan, A. E. (2008). The road ahead for mining software repositories. In Frontiers of Software Maintenance, 2008. FoSM 2008, pages 48–57. IEEE.
Hu, S., Huang, T., Chow, K.-H., Wei, W., Wu, Y., and Liu, L. (2024). Zipzap: Efficient training of language models for large-scale fraud detection on blockchain. In Proceedings of the ACM Web Conference 2024 (WWW ’24), pages 2807–2816. ACM.
Hu, S., Zhang, Z., Luo, B., Lu, S., He, B., and Liu, L. (2023). BERT4ETH: A pre-trained transformer for ethereum fraud detection. In Proceedings of the ACM Web Conference 2023 (WWW ’23), pages 2189–2197. ACM.
Hutto, C. and Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th International Conference on Weblogs and Social Media (ICWSM), pages 216–225.
Li, Z., Li, X., Li, W., and Wang, X. (2025). SCALM: Detecting bad practices in smart contracts through llms. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 470–477.
Loria, S. (2018). TextBlob: Simplified text processing.
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Montgomery, L., Lüders, C., and Maalej, W. (2025). Mining issue trackers: Concepts and techniques. In Handbook on Natural Language Processing for Requirements Engineering, pages 309–336. Springer.
Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. In EMNLP-IJCNLP.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.
Vaccargiu, M., Aufiero, S., Bartolucci, S., Neykova, R., Tonelli, R., and Destefanis, G. (2024). Sustainability in blockchain development: A BERT-based analysis of ethereum developer discussions. In Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE ’24), pages 381–386, Salerno, Italy. ACM.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2):224–227.
de Groot, M., Aliannejadi, M., and Haas, M. R. (2022). Experiments on generalizability of BERTopic on multi-domain short text. arXiv preprint arXiv:2212.08459.
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., and Zhou, M. (2020). Codebert: A pre-trained model for programming and natural languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1536–1547. ACL.
Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
Hassan, A. E. (2008). The road ahead for mining software repositories. In Frontiers of Software Maintenance, 2008. FoSM 2008, pages 48–57. IEEE.
Hu, S., Huang, T., Chow, K.-H., Wei, W., Wu, Y., and Liu, L. (2024). Zipzap: Efficient training of language models for large-scale fraud detection on blockchain. In Proceedings of the ACM Web Conference 2024 (WWW ’24), pages 2807–2816. ACM.
Hu, S., Zhang, Z., Luo, B., Lu, S., He, B., and Liu, L. (2023). BERT4ETH: A pre-trained transformer for ethereum fraud detection. In Proceedings of the ACM Web Conference 2023 (WWW ’23), pages 2189–2197. ACM.
Hutto, C. and Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th International Conference on Weblogs and Social Media (ICWSM), pages 216–225.
Li, Z., Li, X., Li, W., and Wang, X. (2025). SCALM: Detecting bad practices in smart contracts through llms. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 470–477.
Loria, S. (2018). TextBlob: Simplified text processing.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pages 281–297. University of California Press.
Montgomery, L., Lüders, C., and Maalej, W. (2025). Mining issue trackers: Concepts and techniques. In Handbook on Natural Language Processing for Requirements Engineering, pages 309–336. Springer.
Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. In EMNLP-IJCNLP.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.
Vaccargiu, M., Aufiero, S., Bartolucci, S., Neykova, R., Tonelli, R., and Destefanis, G. (2024). Sustainability in blockchain development: A BERT-based analysis of ethereum developer discussions. In Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE ’24), pages 381–386, Salerno, Italy. ACM.
Publicado
08/09/2026
Como Citar
SANTOS, Cleane Sousa; ARAÚJO, Arlino Henrique Magalhães de.
PLN e MSR em Repositórios Open Source de Criptomoedas: Um Estudo de Caso no Ethereum. In: WORKSHOP DE TESES E DISSERTAÇÕES (WTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 328-334.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249678.
