Page 44 - 《软件学报》2021年第11期
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3370 Journal of Software 软件学报 Vol.32, No.11, November 2021
[19] Konecny J, Liu J, Richtárik P, Takác M. Mini-batch semi-stochastic gradient descent in the proximal setting. IEEE Journal of
Selected Topics in Signal Processing, 2016,10(2):242−255.
[20] Bennell JA, Cabo M, Martínez-Sykora A. A beam search approach to solve the convex irregular bin packing problem with
guillotine guts. European Journal of Operational Research, 2018,270(1):89−102.
[21] Bielik P, Raychev V, Vechev M. PHOG: Probabilistic model for code. In: Proc. of the Int’l Conf. on Machine Learning. IMLS,
2016. 2933−2942.
[22] White M, Vendome C, Linares-Vasquez M. Toward deep learning software repositories. In: Proc. of the Mining Software
Repositories. New York: IEEE, 2015. 334−345.
[23] Keivanloo I, Rilling J, Zou Y. Spotting working code examples. In: Proc. of the 36th Int’l Conf. on Software Engineering. New
York: ACM, 2014. 664−675.
[24] Raychev V, Bielik P, Vechev M, Krause A. Learning programs from noisy data. In: Proc. of the ACM SIGPLAN-SIGACT Symp.
on Principles of Programming Languages. New York: ACM, 2016. 761−774.
[25] Reiss SP. Semantics-based code search demonstration proposal. In: Proc. of the IEEE Int’l Conf. on Software Maintenance. New
York: IEEE, 2009. 385−386.
[26] Bajracharya S, Ossher J, Masiero PC, Lopes CV. A test-driven approach to code search and its application to the reuse of auxiliary
functionality. Information & Software Technology, 2011,53(4):294−306.
[27] Hill E, Pollock L, Vijayshanker K. Improving source code search with natural language phrasal representations of method
signatures. In: Proc. of the IEEE/ACM Int’l Conf. on Automated Software Engineering. New York: IEEE, 2011. 524−527.
[28] Nguyen TV, Nguyen AT, Phan HD, et al. Combining Word2Vec with revised vector space model for better code retrieval. In: Proc.
of the Int’l Conf. on Software Engineering Companion. New York: IEEE, 2017. 183−185.
[29] Hill E, Pollock L, Vijay-Shanker K. Automatically capturing source code context of NL-queries for software maintenance and reuse.
In: Proc. of the Int’l Conf. on Software Engineering. New York: IEEE, 2009. 232−242.
[30] Roldan-Vega M, Mallet G, Hill E. CONQUER: A tool for NL-based query refinement and contextualizing code search results. In:
Proc. of the IEEE Int’l Conf. on Software Maintenance. New York: IEEE Computer Society, 2013. 512−515.
[31] Lu M, Sun X, Wang S. Query expansion via WordNet for effective code search. In: Proc. of the Int’l Conf. on Software Analysis,
Evolution and Reengineering. New York: IEEE, 2015. 545−549.
[32] Haiduc S, Rosa GD, Bavota G. Query quality prediction and reformulation for source code search: The refoqus tool. In: Proc. of the
2013 Int’l Conf. on Software Engineering. New York: IEEE Computer Society, 2013. 1307−1310.
[33] Rahman MM, Roy CK, Lo D. RACK: Automatic API recommendation using crowdsourced knowledge. In: Proc. of the Int’l Conf.
on Software Analysis, Evolution, and Reengineering. New York: IEEE, 2016. 349−359.
[34] Reed S, Freitas N. Neural programmer-interpreters. In: Proc. of the Int’l Conf. on Learning Representations. arXiv preprint
arXiv:1511.06279, 2015.
[35] Li C, Tarlow D, Gaunt AL, Brockschmidt M, Kushman N. Neural program lattices. In: Proc. of the Int’l Conf. on Learning
Representations. Springer-Verlag, 2017.
[36] Asaduzzaman M, Roy CK, Schneider KA, Hou D. A simple, efficient, context-sensitive approach for code completion. Journal of
Software: Evolution and Process, 2016,28(7):512−541.
[37] Allamanis M, Barr ET, Bird C, Sutton CA. Suggesting accurate method and class names. In: Proc. of the Joint Meeting on
Foundations of Software Engineering. New York: ACM, 2015. 38−49.
[38] Mou L, Men R, Li G, Zhang L, Jin Z. On end-to-end program generation from user intention by deep neural networks. arXiv
preprint arXiv:1510.07211, 2015.
[39] Tu Z, Su Z, Devanbu P. On the localness of software. In: Proc. of the 22nd ACM SIGSOFT Int’l Symp. on Foundations of Software
Engineering. ACM, 2014. 269−280.
[40] Wang S, Lo D, Jiang L. Active code search: Incorporating user feedback to improve code search relevance. In: Proc. of the ACM/
IEEE Int’l Conf. on Automated Software Engineering. New York: ACM, 2014. 677−682.
[41] Ishihara T, Hotta K, Higo Y, Kusumoto S. Reusing reused code. In: Proc. of the 20th Working Conf. on Reverse Engineering. New
York: IEEE Computer Society, 2013. 457−461.