Page 159 - 《软件学报》2021年第5期
P. 159

顾斌  等:程序智能合成技术研究进展                                                              1383


                [18]    Yin P, Neubig G.  A syntactic neural  model for general-purpose  code generation. In: Proc. of the 55th  Annual  Meeting of  the
                     Association for Computational Linguistics, Vol.1. 2017. 440−450. [doi: 10.18653/v1/P17-1041].
                [19]    Beltagy I, Quirk C. Improved semantic parsers for if-then statements. In: Proc. of the 54th Annual Meeting of the Association for
                     Computational Linguistics, Vol.1. 2016. 726−736.
                [20]    Cai R, Xu B, Yang X, et al. An encoder-decoder framework translating natural language to database queries. arXiv preprint arXiv:
                     1711.06061, 2017.
                [21]    Alur R, Martin M, Raghothaman M, et al. Synthesizing finite-state protocols from scenarios and requirements. In: Proc. of the 10th
                     Int’l Haifa Verification Conf. (HVC). 2014. 75−91.
                [22]    Hughes J, Sparks C, Stoughton A, et al. Building resource adaptive software systems (BRASS): Objectives and system evaluation.
                     ACM SIGSOFT Software Engineering Notes, 2016,41(1):1−2.
                [23]    Intent-defined adaptive software (IDAS). Broad Agency Announcement, 2019. https://www.darpa.mil/program/
                [24]    Shaw D, Wart W, Green C. Inferring LISP programs from examples. In: Proc. of the 4th Int’1 Joint Conf. on Artificial Intelligence
                     (IJCAI), Vol.1. 1975. 260−267.
                [25]    Summers PD. A methodology for LISP program construction from examples. Journal of the ACM, 1977,24(1):161−175.
                [26]    Manna Z, Waldinger R. Synthesis: Dreams→programs. IEEE Trans. on Software Engineering, 1979,5(4):294−328.
                [27]    Manna Z,  Waldinger R. A  deductive approach  to  program synthesis. ACM Trans.  on  Programming  Languages and  Systems
                     (TOPLAS), 1980,2(1):90−121.
                [28]    Kitzelmann E. Analytical inductive functional programming. In: Proc. of the 18th Int’1 Symp. on Logic-based Program Synthesis
                     and Transformation (LOPSTR). 2009. 87−102.
                [29]    Bornholt J, Torlak E. Scaling program synthesis by exploiting existing code. In: Proc. of the Machine Learning for Programming
                     Languages Workshop (ML4PL). 2015.
                [30]    Reed S, De Freitas N. Neural programmer-interpreters. In: Proc. of the Int’l Conf. on Learning Representations (ICLR). 2016.
                [31]    Li CT, Tarlow D, et al. Neural program lattices. In: Proc. of the Int’l Conf. on Learning Representations (ICLR). 2017.
                [32]    Cai J, Shin R, Song D. Making neural programming architectures generalize via recursion. In: Proc. of the Int’l Conf. on Learning
                     Representations (ICLR). 2017.
                [33]    Devlin J, Uesato J, Bhupatiraju S, et al. RobustFill: Neural program learning under noisy I/O. In: Proc. of the 34th Int’l Conf. on
                     Machine Learning. 2017. 990−998.
                [34]    Guo Z, James M, Justo D, et al. Program Synthesis by Type-guided Abstraction Refinement. In: Proc. of the ACM on Programming
                     Languages 4 (POPL). 2019. 1−28.
                [35]    Balog M, Gaunt AL, Brockschmidt M, et al. Deepcoder: Learning to write programs. arXiv preprint arXiv:1611.01989, 2016.
                [36]    Becker K, Gottschlich J. AI programmer: Autonomously creating software programs using genetic algorithms. arXiv preprint arXiv:
                     1709.05703, 2017.
                [37]    Parisotto E, Mohamed A, Singh R, et al. Neuro-symbolic program synthesis. arXiv preprint arXiv:1611.01855, 2016.
                [38]    Solar-Lezama A. Program synthesis by sketching [Ph.D. Thesis]. Berkeley: University of California, 2008.
                [39]    Alur R, Černý P, Madhusudan P, et al. Synthesis of interface specifications for Java classes. In: Proc. of the 32nd ACM SIGPLAN-
                     SIGACT Symp. on Principles of Programming Languages (POPL). ACM, 2005. 98−109.
                [40]    Murali V, Qi L, Chaudhuri S, et al. Neural sketch learning for conditional program generation. arXiv preprint arXiv:1703.05698,
                     2017.
                [41]    Alur R, Bodik R, Juniwal G, et al. Syntax-guided synthesis. In: Proc. of the Formal Methods in Computer-aided Design. IEEE,
                     2013.
                [42]    Alur R, Singh R, Fisman D, et al. Search-based program synthesis. Communications of the ACM, 2018,61(12):84−93.
                [43]    Yaghmazadeh N, Wang YP,  Dillig I,  et  al.  Type-  and content-driven synthesis of SQL queries from natural language.  arXiv
                     preprint arXiv:1702.01168, 2017.
                [44]    Luan S, Yang D, Barnaby C,  et al.  Aroma:  Code recommendation via structural  code search.  arXiv preprint  arXiv:1812.01158,
                     2018.
                [45]    Chi C, Xin P, Jun S, et al. Generative API usage code recommendation with parameter concretization. Science China Information
                     Sciences, 2019,62(9):51−72.
                [46]    Hu X, Men R, Li G, et al. Deep-AutoCoder: Learning to complete code precisely with induced code tokens. In: Proc. of the 43rd
                     Annual Computer Software and Applications Conf. (COMPSAC). IEEE, 2019. 159−168.
   154   155   156   157   158   159   160   161   162   163   164