Um estudo preliminar sobre o uso de uma arquitetura deep learning para seleção de respostas no problema de recuperação de código-fonte
Resumo
Dado uma questão e um conjunto de trechos de código-fonte, recuperação de código-fonte ou code retrieval busca encontrar o código que soluciona a dada questão. Este artigo apresenta um estudo preliminar sobre uma nova abordagem para o problema de recuperação de código-fonte, utilizando uma arquitetura deep learning de seleção de respostas. Apresentamos os resultados preliminares do modelo de redes neurais recorrentes bidirecionais (bi-LSTM) com redes neurais convolucionais (CNN) adaptado para o problema de recuperação de código-fonte. Após 20 execuções na amostra de teste, nosso modelo conseguiu atingir uma média MRR de 0,60 ± 0,02 e a medida top-1 o máximo de 51%.
Referências
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Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D. (2015). A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.
Feng, M., Xiang, B., Glass, M. R., Wang, L., and Zhou, B. (2015). Applying deep learning to answer selection: A study and an open task. In 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pages 813–820.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Iyer, S., Konstas, I., Cheung, A., and Zettlemoyer, L. (2016). Summarizing source code using a neural attention model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2073–2083, Berlin, Germany. Association for Computational Linguistics.
Lai, T. M., Bui, T., and Li, S. (2018). A review on deep learning techniques applied to answer selection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2132–2144, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc.
Tan, M., dos Santos, C., Xiang, B., and Zhou, B. (2015). Lstm-based deep learning models for non-factoid answer selection. CoRR, abs/1511.04108.
Wang, M., Smith, N. A., and Mitamura, T. (2007). What is the Jeopardy model? a quasi-synchronous grammar for QA. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 22–32, Prague, Czech Republic. Association for Computational Linguistics.
Yao, Z., Weld, D. S., Chen, W.-P., and Sun, H. (2018). Staqc: A systematically mined question-code dataset from stack overflow. In Proceedings of the 2018 World Wide Web Conference, WWW ’18, pages 1693–1703, Republic and Canton of Geneva, Switzerland. International World Wide Web Conferences Steering Committee.
Zhang, S., Yao, L., Sun, A., and Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv., 52(1):5:1–5:38.
Allamanis, M., Tarlow, D., Gordon, A. D., and Wei, Y. (2015). Bimodal modelling of source code and natural language. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, pages 2123–2132. JMLR.org.
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D. (2015). A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.
Feng, M., Xiang, B., Glass, M. R., Wang, L., and Zhou, B. (2015). Applying deep learning to answer selection: A study and an open task. In 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pages 813–820.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Iyer, S., Konstas, I., Cheung, A., and Zettlemoyer, L. (2016). Summarizing source code using a neural attention model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2073–2083, Berlin, Germany. Association for Computational Linguistics.
Lai, T. M., Bui, T., and Li, S. (2018). A review on deep learning techniques applied to answer selection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2132–2144, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc.
Tan, M., dos Santos, C., Xiang, B., and Zhou, B. (2015). Lstm-based deep learning models for non-factoid answer selection. CoRR, abs/1511.04108.
Wang, M., Smith, N. A., and Mitamura, T. (2007). What is the Jeopardy model? a quasi-synchronous grammar for QA. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 22–32, Prague, Czech Republic. Association for Computational Linguistics.
Yao, Z., Weld, D. S., Chen, W.-P., and Sun, H. (2018). Staqc: A systematically mined question-code dataset from stack overflow. In Proceedings of the 2018 World Wide Web Conference, WWW ’18, pages 1693–1703, Republic and Canton of Geneva, Switzerland. International World Wide Web Conferences Steering Committee.
Zhang, S., Yao, L., Sun, A., and Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv., 52(1):5:1–5:38.
Publicado
25/09/2019
Como Citar
DE REZENDE MARTINS, Marcelo; GEROSA, Marco Aurélio.
Um estudo preliminar sobre o uso de uma arquitetura deep learning para seleção de respostas no problema de recuperação de código-fonte. In: WORKSHOP DE VISUALIZAÇÃO, EVOLUÇÃO E MANUTENÇÃO DE SOFTWARE (VEM), 1. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 82-89.
DOI: https://doi.org/10.5753/vem.2019.7589.
