Fetching the paper…
Reading the bibliography…
Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality.
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.
G. A. Miller, WordNet: An electronic lexical database . MIT press, 1998
1998
Earlier work this paper cites.
M. Maybury, Advances in automatic text summarization . MIT press, 1999
1999
Earlier work this paper cites.
S. Bajracharya, T. Ngo, E. Linstead, Y. Dou, P. Rigor, P. Baldi, and C. Lopes, “Sourcerer: a search engine for open source code supporting structure-based search,” in Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications , 2006, pp. 681–682
2006
Earlier work this paper cites.
M.-C. De Marneffe, B. MacCartney, C. D. Manning et al. , “Generating typed dependency parses from phrase structure parses.” in Lrec , vol. 6, 2006, pp. 449–454
2006
Earlier work this paper cites.
M.-C. De Marneffe and C. D. Manning, “Stanford typed dependencies manual,” 2008
2008
Earlier work this paper cites.
H. Schütze, C. D. Manning, and P. Raghavan, Introduction to information retrieval . Cambridge University Press Cambridge, 2008, vol. 39
2008
Earlier work this paper cites.
G. Frantzeskou, S. MacDonell, E. Stamatatos, and S. Gritzalis, “Examining the significance of high-level programming features in source code author classification,” Journal of Systems and Software , vol. 81, no. 3, pp. 447–460, 2008
2008
Earlier work this paper cites.
V. Keselj, “Speech and language processing daniel jurafsky and james h. martin,” 2009
2009
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel , J. Fürnkranz and T. Joachims, Eds. Omnipress, 2010, pp. 807–814. [Online]. Available: https://icml.cc/Conferences/2010/papers/432.pdf
2010
Earlier work this paper cites.
E. Hill, L. Pollock, and K. Vijay-Shanker, “Improving source code search with natural language phrasal representations of method signatures,” in 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011) . IEEE, 2011, pp. 524–527
2011
Earlier work this paper cites.
C. McMillan, M. Grechanik, D. Poshyvanyk, Q. Xie, and C. Fu, “Portfolio: finding relevant functions and their usage,” in Proceedings of the 33rd International Conference on Software Engineering , 2011, pp. 111–120
2011
Earlier work this paper cites.
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” Journal of machine learning research , vol. 12, no. ARTICLE, pp. 2493–2537, 2011
2011
Earlier work this paper cites.
S. K. Bajracharya and C. V. Lopes, “Analyzing and mining a code search engine usage log,” Empirical Software Engineering , vol. 17, no. 4, pp. 424–466, 2012
2012
Earlier work this paper cites.
K. Krugler, “Krugle code search architecture,” Finding Source Code on the Web for Remix and Reuse , pp. 103–120, 2013
2013
Earlier work this paper cites.
S. Haiduc, G. Bavota, A. Marcus, R. Oliveto, A. De Lucia, and T. Menzies, “Automatic query reformulations for text retrieval in software engineering,” in 2013 35th International Conference on Software Engineering (ICSE) . IEEE, 2013, pp. 842–851
2013
Earlier work this paper cites.
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov, “Devise: A deep visual-semantic embedding model,” in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States , C. J. C. Burges, L. Bottou, Z. Ghahramani, and K. Q. Weinberger, Eds., 2013, pp. 2121–2129. [Online]. Available: https://proceedings.neurips.cc/paper/2013/hash/7cce53cf90577442771720a370c3c723-Abstract.html
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Linares-Vásquez, C. McMillan, D. Poshyvanyk, and M. Grechanik, “On using machine learning to automatically classify software applications into domain categories,” Empirical Software Engineering , vol. 19, no. 3, pp. 582–618, 2014
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.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in neural information processing systems , 2015, pp. 2224–2232
2015
Earlier work this paper cites.
C. Gormley and Z. Tong, Elasticsearch: the definitive guide: a distributed real-time search and analytics engine . ” O’Reilly Media, Inc.”, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin, “Convolutional neural networks over tree structures for programming language processing,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA , D. Schuurmans and M. P. Wellman, Eds. AAAI Press, 2016, pp. 1287–1293. [Online]. Available: http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11775
2016
Cited alongside, same era.
2016
Cited alongside, same era.
X. Gu, H. Zhang, D. Zhang, and S. Kim, “Deep api learning,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering , 2016, pp. 631–642
2016
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Cvitkovic, B. Singh, and A. Anandkumar, “Open vocabulary learning on source code with a graph-structured cache,” in International Conference on Machine Learning . PMLR, 2019, pp. 1475–1485
2019
Later among the works it cites.
J. Cambronero, H. Li, S. Kim, K. Sen, and S. Chandra, “When deep learning met code search,” in Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2019, pp. 964–974
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin, “Convolutional neural networks over tree structures for programming language processing,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA , D. Schuurmans and M. P. Wellman, Eds. AAAI Press, 2016, pp. 1287–1293. [Online]. Available: http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11775
2016
Cited alongside, same era.
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
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in neural information processing systems , 2017, pp. 1024–1034
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Honnibal and I. Montani, “Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing,” Unpublished software application. https://spacy. io , 2017
2017
Cited alongside, same era.
H. Wei and M. Li, “Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code.” in IJCAI , 2017, pp. 3034–3040
2017
Cited alongside, same era.
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
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “code2vec: Learning distributed representations of code,” Proceedings of the ACM on Programming Languages , vol. 3, no. POPL, pp. 1–29, 2019
2019
Later among the works it cites.
J. Zhang, X. Wang, H. Zhang, H. Sun, K. Wang, and X. Liu, “A novel neural source code representation based on abstract syntax tree,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) , 2019, pp. 783–794
2019
Later among the works it cites.
R. Haldar, L. Wu, J. Xiong, and J. Hockenmaier, “A multi-perspective architecture for semantic code search,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 8563–8568. [Online]. Available: https://www.aclweb.org/anthology/2020.acl-main.758
2020
Later among the works it cites.
J. Shuai, L. Xu, C. Liu, M. Yan, X. Xia, and Y. Lei, “Improving code search with co-attentive representation learning,” in Proceedings of the 28th International Conference on Program Comprehension , 2020, pp. 196–207
2020
Later among the works it cites.
S. Yan, H. Yu, Y. Chen, B. Shen, and L. Jiang, “Are the code snippets what we are searching for? a benchmark and an empirical study on code search with natural-language queries,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2020, pp. 344–354
2020
Later among the works it cites.
M. Allamanis, E. T. Barr, S. Ducousso, and Z. Gao, “Typilus: neural type hints,” in Proceedings of the 41st acm sigplan conference on programming language design and implementation , 2020, pp. 91–105
2020
Later among the works it cites.
V. J. Hellendoorn, C. Sutton, R. Singh, P. Maniatis, and D. Bieber, “Global relational models of source code,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020. [Online]. Available: https://openreview.net/forum?id=B1lnbRNtwr
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Q. Zhu, Z. Sun, X. Liang, Y. Xiong, and L. Zhang, “Ocor: an overlapping-aware code retriever,” in 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2020, pp. 883–894
2020
Later among the works it cites.
S. Liu, C. Gao, S. Chen, N. L. Yiu, and Y. Liu, “Atom: Commit message generation based on abstract syntax tree and hybrid ranking,” IEEE Transactions on Software Engineering , 2020
2020
Later among the works it cites.
C. Liu, X. Xia, D. Lo, C. Gao, X. Yang, and J. Grundy, “Opportunities and challenges in code search tools,” ACM Computing Surveys (CSUR) , vol. 54, no. 9, pp. 1–40, 2021
2021
Closest in time.
C. Liu, X. Xia, D. Lo, Z. Liu, A. E. Hassan, and S. Li, “Codematcher: Searching code based on sequential semantics of important query words,” ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 31, no. 1, pp. 1–37, 2021
2021
Closest in time.
S. Fang, Y.-S. Tan, T. Zhang, and Y. Liu, “Self-attention networks for code search,” Information and Software Technology , vol. 134, p. 106542, 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
S. Liu, Y. Chen, X. Xie, J. K. Siow, and Y. Liu, “Retrieval-augmented generation for code summarization via hybrid GNN,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021. [Online]. Available: https://openreview.net/forum?id=zv-typ1gPxA
2021
Closest in time.
U. Alon and E. Yahav, “On the bottleneck of graph neural networks and its practical implications,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=i80OPhOCVH2
2021
Closest in time.
L. Xu, H. Yang, C. Liu, J. Shuai, M. Yan, Y. Lei, and Z. Xu, “Two-stage attention-based model for code search with textual and structural features,” in 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2021, pp. 342–353
2021
Closest in time.
2021
Closest in time.