2023

Syntax and Domain Aware Model for Unsupervised Program Translation

Liu, Fang, Li, Jia, Zhang, Li

Understand

There is growing interest in software migration as the development of software and society.

Manually migrating projects between languages is error-prone and expensive.
In recent years, researchers have begun to explore automatic program translation using supervised deep learning techniques by learning from large-scale parallel code corpus.
However, parallel resources are scarce in the programming language domain, and it is costly to collect bilingual data manually.

Built on

Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Statistical Machine Translation
Philipp Koehn · 2010
Earlier work this paper cites.
AURA: a hybrid approach to identify framework evolution
Wei Wu, Yann-Gaël Guéhéneuc, Giuliano Antoniol, and Miryung Kim · 2010
Earlier work this paper cites.
Mining API mapping for language migration
Hao Zhong, Suresh Thummalapenta, Tao Xie, Lu Zhang, and Qing Wang · 2010
Earlier work this paper cites.
Lexical statistical machine translation for language migration
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N Nguyen · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Divide-and-conquer approach for multi-phase statistical migration for source code (t)
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N Nguyen · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma · 2016
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
Earlier work this paper cites.
Deepam: Migrate apis with multi-modal sequence to sequence learning
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.

Similar

Unsupervised neural machine translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho · 2018
Cited alongside, same era.
Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song · 2018
Cited alongside, same era.
Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Dynamic neural program embeddings for program repair
Ke Wang, Rishabh Singh, and Zhendong Su · 2018
Cited alongside, same era.
When deep learning met code search
José Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Codesearchnet challenge: Evaluating the state of semantic code search
Original
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Original
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
Cited alongside, same era.
Pythia: Ai-assisted code completion system
Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, and Neel Sundaresan · 2019
Cited alongside, same era.
A transformer-based approach for source code summarization
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2020
Cited alongside, same era.

Then

Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
Later among the works it cites.
Unsupervised translation of programming languages
Original
Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, and Guillaume Lample · 2020
Later among the works it cites.
BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
Later among the works it cites.
Multi-task learning based pre-trained language model for code completion
Fang Liu, Ge Li, Yunfei Zhao, and Zhi Jin · 2020
Later among the works it cites.
A contextual alignment enhanced cross graph attention network for cross-lingual entity alignment
Zhiwen Xie, Runjie Zhu, Kunsong Zhao, Jin Liu, Guangyou Zhou, and Xiangji Huang · 2020
Later among the works it cites.
Graph-based, self-supervised program repair from diagnostic feedback
Michihiro Yasunaga and Percy Liang · 2020
Later among the works it cites.
Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou · 2021
Later among the works it cites.
DOBF: A deobfuscation pre-training objective for programming languages
Marie-Anne Lachaux, Baptiste Rozière, Marc Szafraniec, and Guillaume Lample · 2021
Later among the works it cites.
Integrating tree path in transformer for code representation
Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, and Zhi Jin · 2021
Later among the works it cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi · 2021
Later among the works it cites.
Language-agnostic representation learning of source code from structure and context
Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, and Stephan Günnemann · 2021
Later among the works it cites.
Leveraging automated unit tests for unsupervised code translation
Baptiste Rozière, Jie Zhang, François Charton, Mark Harman, Gabriel Synnaeve, and Guillaume Lample · 2022
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…