{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T03:43:35Z","timestamp":1777347815716,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T00:00:00Z","timestamp":1608508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,12,21]]},"DOI":"10.1145\/3324884.3416633","type":"proceedings-article","created":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T23:39:02Z","timestamp":1611790742000},"page":"1016-1028","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Attend and represent"],"prefix":"10.1145","author":[{"given":"Cedric","family":"Richter","sequence":"first","affiliation":[{"name":"Paderborn University, Paderborn, Germany"}]},{"given":"Heike","family":"Wehrheim","sequence":"additional","affiliation":[{"name":"Paderborn University, Paderborn, Germany"}]}],"member":"320","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45237-7_25"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_1_3_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)."},{"key":"e_1_3_2_1_4_1","volume-title":"International conference on machine learning. 2091--2100","author":"Allamanis Miltiadis","year":"2016","unstructured":"Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016. A convolutional attention network for extreme summarization of source code. In International conference on machine learning. 2091--2100."},{"key":"e_1_3_2_1_5_1","volume-title":"code2seq: Generating sequences from structured representations of code. arXiv preprint arXiv:1808.01400","author":"Alon Uri","year":"2018","unstructured":"Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2018. code2seq: Generating sequences from structured representations of code. arXiv preprint arXiv:1808.01400 (2018)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3296979.3192412"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290353"},{"key":"e_1_3_2_1_8_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_3_2_1_9_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_3_2_1_10_1","volume-title":"Context2Name: A deep learning-based approach to infer natural variable names from usage contexts. arXiv preprint arXiv:1809.05193","author":"Bavishi Rohan","year":"2018","unstructured":"Rohan Bavishi, Michael Pradel, and Koushik Sen. 2018. Context2Name: A deep learning-based approach to infer natural variable names from usage contexts. arXiv preprint arXiv:1809.05193 (2018)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSM.1998.738528"},{"key":"e_1_3_2_1_12_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. In Advances in Neural Information Processing Systems. 3585--3597."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-54580-5_20"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03421-4_11"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21690-4_42"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22110-1_16"},{"key":"e_1_3_2_1_17_1","volume-title":"Formal Methods in Computer Aided Design","author":"Beyer Dirk","unstructured":"Dirk Beyer, M Erkan Keremoglu, and Philipp Wendler. 2010. Predicate abstraction with adjustable-block encoding. In Formal Methods in Computer Aided Design. IEEE, 189--197."},{"key":"e_1_3_2_1_18_1","volume-title":"Scalable Verification Framework for C Program. In 2018 25th Asia-Pacific Software Engineering Conference (APSEC). 129--138","author":"Chen G.","unstructured":"G. Chen, D. Wang, T. Li, C. Zhang, M. Gu, and J. Sun. 2018. Scalable Verification Framework for C Program. In 2018 25th Asia-Pacific Software Engineering Conference (APSEC). 129--138."},{"key":"e_1_3_2_1_19_1","volume-title":"Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509","author":"Child Rewon","year":"2019","unstructured":"Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509 (2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"What Does BERT Look At? An Analysis of BERT's Attention. arXiv preprint arXiv:1906.04341","author":"Clark Kevin","year":"2019","unstructured":"Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D Manning. 2019. What Does BERT Look At? An Analysis of BERT's Attention. arXiv preprint arXiv:1906.04341 (2019)."},{"key":"e_1_3_2_1_21_1","volume-title":"Bounded model checking using satisfiability solving. Formal methods in system design 19, 1","author":"Clarke Edmund","year":"2001","unstructured":"Edmund Clarke, Armin Biere, Richard Raimi, and Yunshan Zhu. 2001. Bounded model checking using satisfiability solving. Formal methods in system design 19, 1 (2001), 7--34."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3121257.3121262"},{"key":"e_1_3_2_1_23_1","volume-title":"A deep language model for software code. arXiv preprint arXiv:1608.02715","author":"Dam Hoa Khanh","year":"2016","unstructured":"Hoa Khanh Dam, Truyen Tran, and Trang Pham. 2016. A deep language model for software code. arXiv preprint arXiv:1608.02715 (2016)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21690-4_39"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10703-016-0264-5"},{"key":"e_1_3_2_1_26_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)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"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).","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-017-9573-6"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10742"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00113"},{"key":"e_1_3_2_1_31_1","volume-title":"Global Relational Models of Source Code. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=B1lnbRNtwr","author":"Hellendoorn Vincent J.","year":"2020","unstructured":"Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber. 2020. Global Relational Models of Source Code. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=B1lnbRNtwr"},{"key":"e_1_3_2_1_32_1","volume-title":"Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/314"},{"key":"e_1_3_2_1_34_1","volume-title":"CodeSum: Translate Program Language to Natural Language. ArXiv abs\/1708.01837","author":"Hu Xing","year":"2017","unstructured":"Xing Hu, Yuhan Wei, Ge Li, and Zhi Jin. 2017. CodeSum: Translate Program Language to Natural Language. ArXiv abs\/1708.01837 (2017)."},{"key":"e_1_3_2_1_35_1","volume-title":"Music transformer. arXiv preprint arXiv:1809.04281","author":"Anna Huang Cheng-Zhi","year":"2018","unstructured":"Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M Dai, Matthew D Hoffman, Monica Dinculescu, and Douglas Eck. 2018. Music transformer. arXiv preprint arXiv:1809.04281 (2018)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2008.08.002"},{"key":"e_1_3_2_1_37_1","volume-title":"European Conference on Computer Vision. Springer, 67--84","author":"Joulin Armand","unstructured":"Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache. 2016. Learning visual features from large weakly supervised data. In European Conference on Computer Vision. Springer, 67--84."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2020.2987024"},{"key":"e_1_3_2_1_39_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)."},{"key":"e_1_3_2_1_40_1","volume-title":"Data Mining and Constraint Programming","author":"Kotthoff Lars","unstructured":"Lars Kotthoff. 2016. Algorithm selection for combinatorial search problems: A survey. In Data Mining and Constraint Programming. Springer, 149--190."},{"key":"e_1_3_2_1_41_1","volume-title":"ICSR (LNCS)","author":"Li Wenchao","unstructured":"Wenchao Li, Hassen Saidi, Huascar Sanchez, Martin Sch\u00e4f, and Pascal Schweitzer. 2016. Detecting Similar Programs via The Weisfeiler-Leman Graph Kernel. In ICSR (LNCS), Vol. 9679. Springer, 315--330."},{"key":"e_1_3_2_1_42_1","volume-title":"Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025","author":"Luong Minh-Thang","year":"2015","unstructured":"Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)."},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV). 181--196","author":"Mahajan Dhruv","unstructured":"Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten. 2018. Exploring the limits of weakly supervised pretraining. In Proceedings of the European Conference on Computer Vision (ECCV). 181--196."},{"key":"e_1_3_2_1_44_1","volume-title":"Molecule Attention Transformer. arXiv preprint arXiv:2002.08264","author":"Maziarka \u0141ukasz","year":"2020","unstructured":"\u0141ukasz Maziarka, Tomasz Danel, S\u0142awomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanis\u0142aw Jastrz\u0119bski. 2020. Molecule Attention Transformer. arXiv preprint arXiv:2002.08264 (2020)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00143877"},{"key":"e_1_3_2_1_46_1","volume-title":"TBCNN: A tree-based convolutional neural network for programming language processing. arXiv preprint arXiv:1409.5718","author":"Mou Lili","year":"2014","unstructured":"Lili Mou, Ge Li, Zhi Jin, Lu Zhang, and Tao Wang. 2014. TBCNN: A tree-based convolutional neural network for programming language processing. arXiv preprint arXiv:1409.5718 (2014)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106238"},{"key":"e_1_3_2_1_48_1","volume-title":"Transformers without tears: Improving the normalization of self-attention. arXiv preprint arXiv:1910.05895","author":"Nguyen Toan Q","year":"2019","unstructured":"Toan Q Nguyen and Julian Salazar. 2019. Transformers without tears: Improving the normalization of self-attention. arXiv preprint arXiv:1910.05895 (2019)."},{"key":"e_1_3_2_1_49_1","volume-title":"Image transformer. arXiv preprint arXiv:1802.05751","author":"Parmar Niki","year":"2018","unstructured":"Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, \u0141ukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran. 2018. Image transformer. arXiv preprint arXiv:1802.05751 (2018)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236029"},{"key":"e_1_3_2_1_51_1","volume-title":"Advances in Large Margin Classifiers","author":"Platt John","unstructured":"John Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, 6--74."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3276517"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3213846.3213873"},{"key":"e_1_3_2_1_54_1","volume-title":"Do imagenet classifiers generalize to imagenet? arXiv preprint arXiv:1902.10811","author":"Recht Benjamin","year":"2019","unstructured":"Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019. Do imagenet classifiers generalize to imagenet? arXiv preprint arXiv:1902.10811 (2019)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0065-2458(08)60520-3"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-020-00270-x"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17502-3_19"},{"key":"e_1_3_2_1_58_1","volume-title":"A survey of decision tree classifier methodology","author":"Rasoul Safavian S","year":"1991","unstructured":"S Rasoul Safavian and David Landgrebe. 1991. A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21, 3 (1991), 660--674."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.5555\/559923"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786845"},{"key":"e_1_3_2_1_62_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929--1958."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2597073.2597080"},{"key":"e_1_3_2_1_64_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008."},{"key":"e_1_3_2_1_65_1","volume-title":"Learning deep transformer models for machine translation. arXiv preprint arXiv:1906.01787","author":"Wang Qiang","year":"2019","unstructured":"Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F Wong, and Lidia S Chao. 2019. Learning deep transformer models for machine translation. arXiv preprint arXiv:1906.01787 (2019)."},{"key":"e_1_3_2_1_66_1","volume-title":"Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree. In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)","author":"Wang Wenhan","unstructured":"Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin. 2020. Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree. In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 261--271."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2490"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00086"}],"event":{"name":"ASE '20: 35th IEEE\/ACM International Conference on Automated Software Engineering","location":"Virtual Event Australia","acronym":"ASE '20","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"]},"container-title":["Proceedings of the 35th IEEE\/ACM International Conference on Automated Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3324884.3416633","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3324884.3416633","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:22Z","timestamp":1750193242000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3324884.3416633"}},"subtitle":["a novel view on algorithm selection for software verification"],"short-title":[],"issued":{"date-parts":[[2020,12,21]]},"references-count":68,"alternative-id":["10.1145\/3324884.3416633","10.1145\/3324884"],"URL":"https:\/\/doi.org\/10.1145\/3324884.3416633","relation":{},"subject":[],"published":{"date-parts":[[2020,12,21]]},"assertion":[{"value":"2021-01-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}