{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:30:53Z","timestamp":1775089853902,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":78,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Luxembourg National Research Funds (FNR)","award":["C17\/IS\/11686509\/CODEMATES"],"award-info":[{"award-number":["C17\/IS\/11686509\/CODEMATES"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,23]]},"DOI":"10.1145\/3524842.3528456","type":"proceedings-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:08:36Z","timestamp":1666051716000},"page":"524-536","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["GraphCode2Vec"],"prefix":"10.1145","author":[{"given":"Wei","family":"Ma","sequence":"first","affiliation":[{"name":"University of Luxembourg, Luxembourg"}]},{"given":"Mengjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"LMU Munich, Germany"}]},{"given":"Ezekiel","family":"Soremekun","sequence":"additional","affiliation":[{"name":"University of Luxembourg, Luxembourg"}]},{"given":"Qiang","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Luxembourg, Luxembourg"}]},{"given":"Jie M.","family":"Zhang","sequence":"additional","affiliation":[{"name":"University College London, United Kingdom"}]},{"given":"Mike","family":"Papadakis","sequence":"additional","affiliation":[{"name":"University of Luxembourg, Luxembourg"}]},{"given":"Maxime","family":"Cordy","sequence":"additional","affiliation":[{"name":"University of Luxembourg, Luxembourg"}]},{"given":"Xiaofei","family":"Xie","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}]},{"given":"Yves Le","family":"Traon","sequence":"additional","affiliation":[{"name":"University of Luxembourg, Luxembourg"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740","author":"Allamanis Miltiadis","year":"2017","unstructured":"Miltiadis Allamanis , Marc Brockschmidt , and Mahmoud Khademi . 2017. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 ( 2017 ). Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2017. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 (2017)."},{"key":"e_1_3_2_1_2_1","unstructured":"Uri Alon Shaked Brody Omer Levy and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. arXiv:1808.01400 [cs.LG]  Uri Alon Shaked Brody Omer Levy and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. arXiv:1808.01400 [cs.LG]"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290353"},{"key":"e_1_3_2_1_4_1","volume-title":"Adaptive control processes","author":"Bellman Richard E","unstructured":"Richard E Bellman . 2015. Adaptive control processes . Princeton university press . Richard E Bellman. 2015. Adaptive control processes. Princeton university press."},{"key":"e_1_3_2_1_5_1","volume-title":"Alice Shoshana Jakobovits, and Torsten Hoefler","author":"Ben-Nun Tal","year":"2018","unstructured":"Tal Ben-Nun , Alice Shoshana Jakobovits, and Torsten Hoefler . 2018 . Neural code comprehension: A learnable representation of code semantics. arXiv preprint arXiv:1806.07336 (2018). Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural code comprehension: A learnable representation of code semantics. arXiv preprint arXiv:1806.07336 (2018)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00109"},{"key":"e_1_3_2_1_7_1","unstructured":"Luca Buratti Saurabh Pujar Mihaela Bornea Scott McCarley Yunhui Zheng Gaetano Rossiello Alessandro Morari Jim Laredo Veronika Thost Yufan Zhuang etal 2020. Exploring software naturalness through neural language models. arXiv preprint arXiv:2006.12641 (2020).  Luca Buratti Saurabh Pujar Mihaela Bornea Scott McCarley Yunhui Zheng Gaetano Rossiello Alessandro Morari Jim Laredo Veronika Thost Yufan Zhuang et al. 2020. Exploring software naturalness through neural language models. arXiv preprint arXiv:2006.12641 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09778-7"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1198"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep Data Flow Analysis. arXiv preprint arXiv:2012.01470","author":"Cummins Chris","year":"2020","unstructured":"Chris Cummins , Hugh Leather , Zacharias Fisches , Tal Ben-Nun , Torsten Hoefler , and Michael O'Boyle . 2020. Deep Data Flow Analysis. arXiv preprint arXiv:2012.01470 ( 2020 ). Chris Cummins, Hugh Leather, Zacharias Fisches, Tal Ben-Nun, Torsten Hoefler, and Michael O'Boyle. 2020. Deep Data Flow Analysis. arXiv preprint arXiv:2012.01470 (2020)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/646153.679523"},{"key":"e_1_3_2_1_12_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-005-3861-2"},{"key":"e_1_3_2_1_15_1","volume-title":"Department of Computer Science","author":"Einarsson Arni","year":"2008","unstructured":"Arni Einarsson and Janus Dam Nielsen . 2008. A survivor's guide to Java program analysis with soot. BRICS , Department of Computer Science , University of Aarhus , Denmark 17 ( 2008 ). Arni Einarsson and Janus Dam Nielsen. 2008. A survivor's guide to Java program analysis with soot. BRICS, Department of Computer Science, University of Aarhus, Denmark 17 (2008)."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 201--208","author":"Erhan Dumitru","year":"2010","unstructured":"Dumitru Erhan , Aaron Courville , Yoshua Bengio , and Pascal Vincent . 2010 . Why does unsupervised pre-training help deep learning? . In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 201--208 . Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal Vincent. 2010. Why does unsupervised pre-training help deep learning?. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 201--208."},{"key":"e_1_3_2_1_17_1","volume-title":"Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng , Daya Guo , Duyu Tang , Nan Duan , Xiaocheng Feng , Ming Gong , Linjun Shou , Bing Qin , Ting Liu , Daxin Jiang , 2020 . Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020). Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/24039.24041"},{"key":"e_1_3_2_1_19_1","volume-title":"JaDA-the Java deadlock analyser. Behavioural Types: from Theories to Tools","author":"Garcia Abel","year":"2017","unstructured":"Abel Garcia and Cosimo Laneve . 2017. JaDA-the Java deadlock analyser. Behavioural Types: from Theories to Tools ( 2017 ), 169--192. Abel Garcia and Cosimo Laneve. 2017. JaDA-the Java deadlock analyser. Behavioural Types: from Theories to Tools (2017), 169--192."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)","author":"Ghannay Sahar","year":"2016","unstructured":"Sahar Ghannay , Benoit Favre , Yannick Esteve , and Nathalie Camelin . 2016 . Word embedding evaluation and combination . In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) . 300--305. Sahar Ghannay, Benoit Favre, Yannick Esteve, and Nathalie Camelin. 2016. Word embedding evaluation and combination. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16). 300--305."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JCSSE.2017.8025917"},{"key":"e_1_3_2_1_22_1","volume-title":"Dahl","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S. Schoenholz , Patrick F. Riley , Oriol Vinyals , and George E . Dahl . 2017 . Neural Message Passing for Quantum Chemistry. CoRR abs\/1704.01212 (2017). arXiv:1704.01212 http:\/\/arxiv.org\/abs\/1704.01212 Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. CoRR abs\/1704.01212 (2017). arXiv:1704.01212 http:\/\/arxiv.org\/abs\/1704.01212"},{"key":"e_1_3_2_1_23_1","volume-title":"Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366","author":"Guo Daya","year":"2020","unstructured":"Daya Guo , Shuo Ren , Shuai Lu , Zhangyin Feng , Duyu Tang , Shujie Liu , Long Zhou , Nan Duan , Alexey Svyatkovskiy , Shengyu Fu , 2020 . Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366 (2020). Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020. Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366 (2020)."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton , Rex Ying , and Jure Leskovec . 2017 . Inductive representation learning on large graphs . In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035 . William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035."},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC","author":"Heinzerling Benjamin","year":"2018","unstructured":"Benjamin Heinzerling and Michael Strube . 2018 . BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages . In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan. https:\/\/aclanthology.org\/L18-1473 Benjamin Heinzerling and Michael Strube. 2018. BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan. https:\/\/aclanthology.org\/L18-1473"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380361"},{"key":"e_1_3_2_1_27_1","volume-title":"Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265","author":"Hu Weihua","year":"2019","unstructured":"Weihua Hu , Bowen Liu , Joseph Gomes , Marinka Zitnik , Percy Liang , Vijay Pande , and Jure Leskovec . 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 ( 2019 ). Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_29_1","volume-title":"Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436","author":"Husain Hamel","year":"2019","unstructured":"Hamel Husain , Ho-Hsiang Wu , Tiferet Gazit , Miltiadis Allamanis , and Marc Brockschmidt . 2019. Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 ( 2019 ). Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 (2019)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2007.30"},{"key":"e_1_3_2_1_31_1","volume-title":"Martin","author":"Jurafsky Dan","year":"2021","unstructured":"Dan Jurafsky and James H . Martin . 2021 . Speech & language processing. Dan Jurafsky and James H. Martin. 2021. Speech & language processing."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2610384.2628055"},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Machine Learning. PMLR, 5110--5121","author":"Kanade Aditya","year":"2020","unstructured":"Aditya Kanade , Petros Maniatis , Gogul Balakrishnan , and Kensen Shi . 2020 . Learning and evaluating contextual embedding of source code . In International Conference on Machine Learning. PMLR, 5110--5121 . Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020. Learning and evaluating contextual embedding of source code. In International Conference on Machine Learning. PMLR, 5110--5121."},{"key":"e_1_3_2_1_34_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_35_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_36_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-2012"},{"key":"e_1_3_2_1_38_1","volume-title":"Fractalnet: Ultra-deep neural networks without residuals. arXiv preprint arXiv:1605.07648","author":"Larsson Gustav","year":"2016","unstructured":"Gustav Larsson , Michael Maire , and Gregory Shakhnarovich . 2016 . Fractalnet: Ultra-deep neural networks without residuals. arXiv preprint arXiv:1605.07648 (2016). Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. 2016. Fractalnet: Ultra-deep neural networks without residuals. arXiv preprint arXiv:1605.07648 (2016)."},{"key":"e_1_3_2_1_39_1","volume-title":"Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493","author":"Li Yujia","year":"2015","unstructured":"Yujia Li , Daniel Tarlow , Marc Brockschmidt , and Richard Zemel . 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 ( 2015 ). Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)."},{"key":"e_1_3_2_1_40_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , Mike Lewis , Luke Zettlemoyer , and Veselin Stoyanov . 2019 . Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019). Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2837614.2837617"},{"key":"e_1_3_2_1_42_1","volume-title":"1st International Conference on Learning Representations, ICLR","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov , Kai Chen , Greg Corrado , and Jeffrey Dean . 2013. Efficient Estimation of Word Representations in Vector Space . In 1st International Conference on Learning Representations, ICLR 2013 , Scottsdale, Arizona, USA , May 2--4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). http:\/\/arxiv.org\/abs\/1301.3781 Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2--4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1301.3781"},{"key":"e_1_3_2_1_43_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00973"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSoC.2019.00035"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63836-8_32"},{"key":"e_1_3_2_1_47_1","volume-title":"Yves Le Traon, and Mark Harman","author":"Papadakis Mike","year":"2019","unstructured":"Mike Papadakis , Marinos Kintis , Jie Zhang , Yue Jia , Yves Le Traon, and Mark Harman . 2019 . Mutation testing advances: an analysis and survey. In Advances in Computers. Vol. 112 . Elsevier , 275--378. Mike Papadakis, Marinos Kintis, Jie Zhang, Yue Jia, Yves Le Traon, and Mark Harman. 2019. Mutation testing advances: an analysis and survey. In Advances in Computers. Vol. 112. Elsevier, 275--378."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180183"},{"key":"e_1_3_2_1_49_1","volume-title":"How could Neural Networks understand Programs? arXiv preprint arXiv:2105.04297","author":"Peng Dinglan","year":"2021","unstructured":"Dinglan Peng , Shuxin Zheng , Yatao Li , Guolin Ke , Di He , and Tie-Yan Liu . 2021. How could Neural Networks understand Programs? arXiv preprint arXiv:2105.04297 ( 2021 ). Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, and Tie-Yan Liu. 2021. How could Neural Networks understand Programs? arXiv preprint arXiv:2105.04297 (2021)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CINTI.2018.8928201"},{"key":"e_1_3_2_1_51_1","unstructured":"Ruchir Puri David S Kung Geert Janssen Wei Zhang Giacomo Domeniconi Vladmir Zolotov Julian Dolby Jie Chen Mihir Choudhury Lindsey Decker etal 2021. Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks. arXiv preprint arXiv:2105.12655 (2021).  Ruchir Puri David S Kung Geert Janssen Wei Zhang Giacomo Domeniconi Vladmir Zolotov Julian Dolby Jie Chen Mihir Choudhury Lindsey Decker et al. 2021. Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks. arXiv preprint arXiv:2105.12655 (2021)."},{"key":"e_1_3_2_1_52_1","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel , Noam Shazeer , Adam Roberts , Katherine Lee , Sharan Narang , Michael Matena , Yanqi Zhou , Wei Li , and Peter J. Liu . 2020 . Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer . Journal of Machine Learning Research 21 , 140 (2020), 1 -- 67 . http:\/\/jmlr.org\/papers\/v21\/20-074.html Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research 21, 140 (2020), 1--67. http:\/\/jmlr.org\/papers\/v21\/20-074.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00349"},{"key":"e_1_3_2_1_54_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. 2008 . The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80. Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80."},{"key":"e_1_3_2_1_55_1","unstructured":"Cedric Seger. 2018. An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing.  Cedric Seger. 2018. An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1162"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_1_59_1","volume-title":"International Conference on Advanced Computing, Networking and Security. Springer, 171--178","author":"Urolagin Siddhaling","year":"2011","unstructured":"Siddhaling Urolagin , KV Prema , and NV Subba Reddy . 2011 . Generalization capability of artificial neural network incorporated with pruning method . In International Conference on Advanced Computing, Networking and Security. Springer, 171--178 . Siddhaling Urolagin, KV Prema, and NV Subba Reddy. 2011. Generalization capability of artificial neural network incorporated with pruning method. In International Conference on Advanced Computing, Networking and Security. Springer, 171--178."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/1925805.1925818"},{"key":"e_1_3_2_1_61_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3418463"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3416590"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER48275.2020.9054857"},{"key":"e_1_3_2_1_65_1","volume-title":"Learning to Represent Programs with Heterogeneous Graphs. arXiv preprint arXiv:2012.04188","author":"Wang Wenhan","year":"2020","unstructured":"Wenhan Wang , Kechi Zhang , Ge Li , and Zhi Jin . 2020. Learning to Represent Programs with Heterogeneous Graphs. arXiv preprint arXiv:2012.04188 ( 2020 ). Wenhan Wang, Kechi Zhang, Ge Li, and Zhi Jin. 2020. Learning to Represent Programs with Heterogeneous Graphs. arXiv preprint arXiv:2012.04188 (2020)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/2970276.2970326"},{"key":"e_1_3_2_1_67_1","unstructured":"Yonghui Wu Mike Schuster Zhifeng Chen Quoc V Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey etal 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016).  Yonghui Wu Mike Schuster Zhifeng Chen Quoc V Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180182"},{"key":"e_1_3_2_1_70_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 ( 2018 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_71_1","volume-title":"Garnett (Eds.)","volume":"32","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang , Zihang Dai , Yiming Yang , Jaime Carbonell , Russ R Salakhutdinov , and Quoc V Le . 2019 . XLNet: Generalized Autoregressive Pretraining for Language Understanding. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R . Garnett (Eds.) , Vol. 32 . Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/ 2019\/file\/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2021.3071750"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00086"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236068"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.109"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1341"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_1_78_1","unstructured":"Andrew Zisserman. 2018. Self-Supervised Learning. https:\/\/project.inria.fr\/paiss\/files\/2018\/07\/zisserman-self-supervised.pdf.  Andrew Zisserman. 2018. Self-Supervised Learning. https:\/\/project.inria.fr\/paiss\/files\/2018\/07\/zisserman-self-supervised.pdf."}],"event":{"name":"MSR '22: 19th International Conference on Mining Software Repositories","location":"Pittsburgh Pennsylvania","acronym":"MSR '22","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"]},"container-title":["Proceedings of the 19th International Conference on Mining Software Repositories"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524842.3528456","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3524842.3528456","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:35Z","timestamp":1750183775000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524842.3528456"}},"subtitle":["generic code embedding via lexical and program dependence analyses"],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":78,"alternative-id":["10.1145\/3524842.3528456","10.1145\/3524842"],"URL":"https:\/\/doi.org\/10.1145\/3524842.3528456","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}