Code generation for a family of executable modelling notations

被引：0
|
作者：
Prout, Adam [1 ]
Atlee, Joanne M. [1 ]
Day, Nancy A. [1 ]
Shaker, Pourya [1 ]
机构：
[1] David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
来源：
Software and Systems Modeling | 2012年 / 11卷 / 02期
关键词：
Compilation and indexing terms; Copyright 2024 Elsevier Inc;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Codes (symbols) - Program compilers - Software design - Petri nets
引用
收藏
页码：251 / 272
相关论文
共 21 条
  • [1] Compiling business process models into executable code
    Faculty of Informatics, University of Lugano, Switzerland
    Handbook of Res. on Bus. Proc. Modeling, 1600, (318-337):
  • [2] Exploring and Evaluating Personalized Models for Code Generation
    Zlotchevski, Andrei
    Drain, Dawn
    Svyatkovskiy, Alexey
    Clement, Colin
    Sundaresan, Neel
    Tufano, Michele
    arXiv, 2022,
  • [3] SymForce: Symbolic Computation and Code Generation for Robotics
    Martiros, Hayk
    Miller, Aaron
    Bucki, Nathan
    Solliday, Bradley
    Kennedy, Ryan
    Zhu, Jack
    Dang, Tung
    Pattison, Dominic
    Zheng, Harrison
    Tomic, Teo
    Henry, Peter
    Cross, Gareth
    Vander Mey, Josiah
    Sun, Alvin
    Wang, Samuel
    Holtz, Kristen
    Robotics: Science and Systems, 2022,
  • [4] Evaluating Code Comment Generation with Summarized API Docs
    Matmti, Bilel
    Fard, Fatemeh
    Proceedings - 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering, NLBSE 2023, 2023, : 60 - 63
  • [5] MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages
    Wang, Zhiruo
    Cuenca, Grace
    Zhou, Shuyan
    Xu, Frank F.
    Neubig, Graham
    EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023, 2023, : 265 - 273
  • [6] CodePAD: Sequence-based Code Generation with Pushdown Automaton
    Dong, Yihong
    Jiang, Xue
    Liu, Yuchen
    Li, Ge
    Jin, Zhi
    arXiv, 2022,
  • [7] Execution-Based Evaluation for Open-Domain Code Generation
    Wang, Zhiruo
    Zhou, Shuyan
    Fried, Daniel
    Neubig, Graham
    Findings of the Association for Computational Linguistics: EMNLP 2023, 2023, : 1271 - 1290
  • [8] Code Generation from Natural Language with Less Prior and More Monolingual Data
    Norouzi, Sajad
    Tang, Keyi
    Cao, Yanshuai
    ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021, 2 : 776 - 785
  • [9] Code line generation based on deep context-awareness of onsite programming
    Tao, Chuanqi
    Bao, Panpan
    Huang, Zhiqiu
    SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (09)
  • [10] CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models
    Yu, Hao
    Zhang, Jiaxin
    Liang, Guangtai
    Shen, Bo
    Zhang, Qi
    Li, Ying
    Xie, Tao
    Ran, Dezhi
    Ma, Yuchi
    Wang, Qianxiang
    arXiv, 2023,
123