[1] HINDLE A, BARR E T, SU Z D, et al. On the naturalness of software[C]//Proceedings of the 34th International Conference on Software Engineering, Zurich, Jun 2-9, 2012. Washington: IEEE Computer Society, 2012: 837-847.
[2] KAMIYA T, KUSUMOTO S, INOUE K. CCFinder: a multilinguistic token-based code clone detection system for large scale source code[J]. IEEE Transactions on Software Engineering, 2002, 28(7): 654-670.
[3] SAJNANI H, SAINI V, SVAJLENKO J, et al. SourcererCC: scaling code clone detection to big-code[C]//Proceedings of the 38th International Conference on Software Engineering, Austin, May 14-22, 2016. New York: ACM, 2016: 1157-1168.
[4] ZHOU J, ZHANG H Y, LO D. Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports[C]//Proceedings of the 34th International Conference on Software Engineering, Zurich, Jun 2-9, 2012. Washington: IEEE Computer Society, 2012: 14-24.
[5] FRANTZESKOU G, MACDONELL S, STAMATATOS E, et al. Examining the significance of high-level programming features in source code author classification[J]. Journal of Systems Software, 2008, 81(3): 447-460.
[6] ZHOU Y, YANG X, CHEN T, et al. Boosting API recommendation with implicit feedback[J]. arXiv:2002.01264, 2020.
[7] ZHOU Y, YAN X, YANG W, et al. Augmenting Java method comments generation with context information based on neural networks[J]. Journal of Systems Software, 2019, 156: 328-340.
[8] HU X, LI G, XIA X, et al. Deep code comment generation[C]//Proceedings of the 26th Conference on Program Comprehension, Gothenburg, May 27-28, 2018. New York: ACM, 2018: 200-210.
[9] WHITE M, TUFANO M, VENDOME C, et al. Deep learning code fragments for code clone detection[C]//Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, Singapore, Sep 3-7, 2016. New York: ACM, 2016: 87-98.
[10] Alon U, Brody S, Levy O, et al. code2seq: generating sequences from structured representations of code[J]. arXiv:1808.01400, 2018.
[11] WAN Y, ZHAO Z, YANG M, et al. Improving automatic source code summarization via deep reinforcement learning[C]//Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, Montpellier, Sep 3-7, 2018. New York: ACM, 2018: 397-407.
[12] MOU L L, LI G, ZHANG L, et al. Convolutional neural networks over tree structures for programming language processing[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 1287-1293.
[13] BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166.
[14] HOCHREITER S. The vanishing gradient problem during learning recurrent neural nets and problem solutions[J]. International Journal of Uncertainty, Fuzziness Knowledge-Based Systems, 1998, 6(2): 107-116.
[15] LE P, ZUIDEMA W. Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs[J]. arXiv:1603.00423, 2016.
[16] ZHANG J, WANG X, ZHANG H Y, et al. A novel neural source code representation based on abstract syntax tree[C]//Proceedings of the 41st International Conference on Software Engineering, Montreal, May 25-31, 2019. Piscataway: IEEE, 2019: 783-794.
[17] SCHUSTER M, PALIWAL K K. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681.
[18] GERS F A, SCHMIDHUBER J, CUMMINS F. Learning to forget: continual prediction with LSTM[J]. Neural Computation, 2000, 12(10): 2451-2471.
[19] HENKEL J, LAHIRI S K, LIBLIT B, et al. Code vectors: understanding programs through embedded abstracted symbolic traces[C]//Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Lake Buena Vista, Nov 4-9, 2018. New York: ACM, 2018: 163-174.
[20] GU X D, ZHANG H Y, ZHANG D M, et al. Deep API learning[C]//Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Seattle, Nov 13-18, 2016. New York: ACM, 2016: 631-642.
[21] NGUYEN T D, NGUYEN A T, PHAN H D, et al. Exploring API embedding for API usages and applications[C]//Proceedings of the 39th International Conference on Software Engineering, Buenos Aires, May 20-28, 2017. Piscataway: IEEE, 2017: 438-449.
[22] Pradel M, Sen K J T D. Deep learning to find bugs: TUD-CS-2017-0295[R]. TU Darmstadt, 2017.
[23] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv:1301.3781, 2013.
[24] PéREZ D, CHIBA S. Cross-language clone detection by learning over abstract syntax trees[C]//Proceedings of the 16th International Conference on Mining Software Repositories, Montreal, May 26-27, 2019. Piscataway: IEEE, 2019: 518-528.
[25] SOCHER R, HUANG E H, PENNIN J, et al. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection[C]//Proceedings of the 25th Annual Conference on Neural Information Processing Systems, Granada, Dec 12-14, 2011. Red Hook: Curran Associates, 2011: 801-809.
[26] JOHNSON J, DOUZE M, JéGOU H. Billion-scale similarity search with GPUs[J]. arXiv:1702.08734v1, 2017.
[27] SCHLEIMER S, WILKERSON D S, Aiken A. Winnowing: local algorithms for document fingerprinting[C]//Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, Jun 9-12, 2003. Piscataway: IEEE, 2003: 76-85.
[28] JIANG L X, MISHERGHI G, SU Z D, et al. Deckard: scalable and accurate tree-based detection of code clones[C]//Proceedings of the 29th International Conference on Software Engineering, Minneapolis, May 20-26, 2007. Washington: IEEE Computer Society, 2007: 96-105.
[29] YAN X, ZHOU Y, HUANG Z Q. Code snippets recommendation based on sequence to sequence model[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(5): 731-739.
闫鑫, 周宇, 黄志球. 基于序列到序列模型的代码片段推荐[J]. 计算机科学与探索, 2020, 14(5): 731-739.
[30] TAI K S, SOCHER R, MANNING C D. Improved semantic representations from tree-structured long short-term memory networks[J]. arXiv:1503.00075, 2015.
[31] WEI H H, LI M. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Aug 19-25, 2017: 3034-3040.
[32] OU M, CUI P, PEI J, et al. Asymmetric transitivity preserving graph embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 1105-1114.
[33] ALLAMANIS M, BROCKSCHMIDT M, KHADEMI M. Learning to represent programs with graphs[J]. arXiv:1711. 00740, 2017.
[34] TUFANO M, WATSON C, BAVOTA G, et al. Deep learning similarities from different representations of source code[C]//Proceedings of the 2018 IEEE/ACM 15th International Conference on Mining Software Repositories, Gothenburg, May 27-Jun 3, 2018. Piscataway: IEEE, 2018: 542-553.
[35] MYERS E M. A precise inter-procedural data flow algorithm[C]//Proceedings of the 15th International Conference on Mining Software Repositories, Gothenburg, May 28-29, 2018. New York: ACM, 1981: 219-230. |