Commenced in January 2007
Paper Count: 32468
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
Abstract:Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 657
 S. Gulwani, “Dimensions in program synthesis,” in Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming. ACM, 2010, pp. 13–24.
 ——, “Automating string processing in spreadsheets using input-output examples,” ACM Sigplan Notices, vol. 46, no. 1, pp. 317–330, 2011.
 Z. Manna and R. Waldinger, “A deductive approach to program synthesis,” ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 2, no. 1, pp. 90–121, 1980.
 Y. Feng, R. Martins, J. Van Geffen, I. Dillig, and S. Chaudhuri, “Component-based synthesis of table consolidation and transformation tasks from examples,” in ACM SIGPLAN Notices, vol. 52, no. 6. ACM, 2017, pp. 422–436.
 T. A. Lau and D. S. Weld, “Programming by demonstration: An inductive learning formulation,” in International Conference on Intelligent User Interfaces: Proceedings of the 4 th international conference on Intelligent user interfaces, vol. 5, no. 08. Citeseer, 1998, pp. 145–152.
 C. Wang, A. Cheung, and R. Bodik, “Synthesizing highly expressive sql queries from input-output examples,” in ACM SIGPLAN Notices, vol. 52, no. 6. ACM, 2017, pp. 452–466.
 A. Solar-Lezama and R. Bodik, Program synthesis by sketching. Citeseer, 2008.
 L. Cheng, “Sqlsol: An accurate sql query synthesizer,” in International Conference on Formal Engineering Methods. Springer, 2019, pp. 104–120.
 M. Balog, A. L. Gaunt, M. Brockschmidt, S. Nowozin, and D. Tarlow, “Deepcoder: Learning to write programs,” arXiv preprint arXiv:1611.01989, 2016.
 N. Yaghmazadeh, Y. Wang, I. Dillig, and T. Dillig, “Sqlizer: query synthesis from natural language,” Proceedings of the ACM on Programming Languages, vol. 1, no. OOPSLA, p. 63, 2017.
 N. Locascio, K. Narasimhan, E. DeLeon, N. Kushman, and R. Barzilay, “Neural generation of regular expressions from natural language with minimal domain knowledge,” arXiv preprint arXiv:1608.03000, 2016.
 S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems, 2015, pp. 91–99.
 S. Zherzdev and A. Gruzdev, “Lprnet: License plate recognition via deep neural networks,” arXiv preprint arXiv:1806.10447, 2018.
 G. B. Shelly and M. E. Vermaat, Discovering Computers, Complete: Your Interactive Guide to the Digital World. Cengage Learning, 2011.
 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
 R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580–587.
 M. Jaderberg, K. Simonyan, A. Zisserman et al., “Spatial transformer networks,” in Advances in neural information processing systems, 2015, pp. 2017–2025.
 D. M. Etter, Introduction to C. Prentice Hall, 1998.