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Code retrieval techniques and tools have been playing a key role in facilitating software developers to retrieve existing code fragments from available open-source repositories given a user query.
A. V. Aho, R. Sethi, and J. D. Ullman, “Compilers, principles, techniques,” Addison wesley , vol. 7, no. 8, p. 9, 1986
1986
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
S. P. Reiss, “Semantics-based code search,” in Proceedings of the 31st International Conference on Software Engineering . IEEE Computer Society, 2009, pp. 243–253
2009
Earlier work this paper cites.
R. C. Martin, Clean code: a handbook of agile software craftsmanship . Pearson Education, 2009
2009
Earlier work this paper cites.
C. Maddison and D. Tarlow, “Structured generative models of natural source code,” in International Conference on Machine Learning , 2014, pp. 649–657
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
V. Mnih, N. Heess, A. Graves et al. , “Recurrent models of visual attention,” in Advances in neural information processing systems , 2014, pp. 2204–2212
2014
Earlier work this paper cites.
M. F. Stollenga, J. Masci, F. Gomez, and J. Schmidhuber, “Deep networks with internal selective attention through feedback connections,” in Advances in neural information processing systems , 2014, pp. 3545–3553
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Sui, D. Ye, and J. Xue, “Detecting memory leaks statically with full-sparse value-flow analysis,” IEEE Transactions on Software Engineering , vol. 40, no. 2, pp. 107–122, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Lu, X. Sun, S. Wang, D. Lo, and Y. Duan, “Query expansion via wordnet for effective code search,” in 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER) . IEEE, 2015, pp. 545–549
2015
Earlier work this paper cites.
F. Lv, H. Zhang, J.-g. Lou, S. Wang, D. Zhang, and J. Zhao, “Codehow: Effective code search based on api understanding and extended boolean model (e),” in 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2015, pp. 260–270
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Allamanis, D. Tarlow, A. Gordon, and Y. Wei, “Bimodal modelling of source code and natural language,” in International Conference on Machine Learning , 2015, pp. 2123–2132
2015
Earlier work this paper cites.
L. Ma, Z. Lu, L. Shang, and H. Li, “Multimodal convolutional neural networks for matching image and sentence,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2623–2631
2015
Earlier work this paper cites.
T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng, and Z. Zhang, “The application of two-level attention models in deep convolutional neural network for fine-grained image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 842–850
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Y. Sui and J. Xue, “Svf: interprocedural static value-flow analysis in llvm,” in Proceedings of the 25th international conference on compiler construction . ACM, 2016, pp. 265–266
2016
Later among the works it cites.
Y. Sui and J. Xue, “On-demand strong update analysis via value-flow refinement,” in Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering . ACM, 2016, pp. 460–473
2016
Later among the works it cites.
2016
Later among the works it cites.
Q.-Y. Jiang and W.-J. Li, “Deep cross-modal hashing,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3232–3240
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
M. White, M. Tufano, C. Vendome, and D. Poshyvanyk, “Deep learning code fragments for code clone detection,” in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering . ACM, 2016, pp. 87–98
2016
Cited alongside, same era.
L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin, “Convolutional neural networks over tree structures for programming language processing.” in AAAI , vol. 2, no. 3, 2016, p. 4
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Y. Cao, M. Long, J. Wang, Q. Yang, and P. S. Yu, “Deep visual-semantic hashing for cross-modal retrieval,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2016, pp. 1445–1454
2016
Cited alongside, same era.
Q. You, H. Jin, Z. Wang, C. Fang, and J. Luo, “Image captioning with semantic attention,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4651–4659
2016
Cited alongside, same era.
H. Nam, J.-W. Ha, and J. Kim, “Dual attention networks for multimodal reasoning and matching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 299–307
2017
Later among the works it cites.
2017
Later among the works it cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Later among the works it cites.
X. Gu, H. Zhang, and S. Kim, “Deep code search,” in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) . IEEE, 2018, pp. 933–944
2018
Later among the works it cites.
Y. Wan, Z. Zhao, M. Yang, G. Xu, H. Ying, J. Wu, and P. S. Yu, “Improving automatic source code summarization via deep reinforcement learning,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering . ACM, 2018, pp. 397–407
2018
Later among the works it cites.
J. Chen and H. Zhuge, “Abstractive text-image summarization using multi-modal attentional hierarchical rnn,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 4046–4056
2018
Later among the works it cites.
K.-M. Kim, S.-H. Choi, J.-H. Kim, and B.-T. Zhang, “Multimodal dual attention memory for video story question answering,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 673–688
2018
Later among the works it cites.
M. Carvalho, R. Cadène, D. Picard, L. Soulier, N. Thome, and M. Cord, “Cross-modal retrieval in the cooking context: Learning semantic text-image embeddings,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . ACM, 2018, pp. 35–44
2018
Later among the works it cites.
2018
Later among the works it cites.
“GitHub,” https://www.github.com, 2019, [Online; accessed 1-May-2019]
2019
Closest in time.
“StackOverflow,” https://www.stackoverflow.com, 2019, [Online; accessed 1-May-2019]
2019
Closest in time.
J.-G. Zhang, P. Zou, Z. Li, Y. Wan, X. Pan, Y. Gong, and P. S. Yu, “Multi-modal generative adversarial network for short product title generation in mobile e-commerce,” NAACL HLT 2019 , pp. 64–72, 2019
2019
Closest in time.
C. Hori, H. Alamri, J. Wang, G. Wichern, T. Hori, A. Cherian, T. K. Marks, V. Cartillier, R. G. Lopes, A. Das et al. , “End-to-end audio visual scene-aware dialog using multimodal attention-based video features,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 2352–2356
2019
Closest in time.
T. Baltrušaitis, C. Ahuja, and L.-P. Morency, “Multimodal machine learning: A survey and taxonomy,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 2, pp. 423–443, 2019
2019
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International conference on machine learning , 2015, pp. 2048–2057
2057
Closest in time.