{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:18:49Z","timestamp":1770293929388,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":94,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":[[2023,11,30]]},"DOI":"10.1145\/3611643.3616254","type":"proceedings-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T23:14:38Z","timestamp":1701386078000},"page":"1522-1534","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["LExecutor: Learning-Guided Execution"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7978-719X","authenticated-orcid":false,"given":"Beatriz","family":"Souza","sequence":"first","affiliation":[{"name":"University of Stuttgart, Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-498X","authenticated-orcid":false,"given":"Michael","family":"Pradel","sequence":"additional","affiliation":[{"name":"University of Stuttgart, Stuttgart, Germany"}]}],"member":"320","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","unstructured":"Wasi Uddin Ahmad Saikat Chakraborty Baishakhi Ray and Kai-Wei Chang. 2020. A Transformer-based Approach for Source Code Summarization. In ACL. 4998\u20135007. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.449 10.18653\/v1\/2020.acl-main.449","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786849"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385412.3385997"},{"key":"e_1_3_2_2_4_1","unstructured":"Miltiadis Allamanis Marc Brockschmidt and Mahmoud Khademi. 2018. Learning to Represent Programs with Graphs. In ICLR. https:\/\/openreview.net\/forum?id=BJOFETxR-"},{"key":"e_1_3_2_2_5_1","volume-title":"Sutton","author":"Allamanis Miltiadis","year":"2016","unstructured":"Miltiadis Allamanis, Hao Peng, and Charles A. Sutton. 2016. A Convolutional Attention Network for Extreme Summarization of Source Code. In ICML. 2091\u20132100."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Uri Alon Shaked Brody Omer Levy and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. In ICLR. https:\/\/openreview.net\/forum?id=H1gKYo09tX","DOI":"10.1145\/3290353"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290353"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Jong-hoon (David) An Avik Chaudhuri Jeffrey S. Foster and Michael Hicks. 2011. Dynamic inference of static types for Ruby.. In POPL. 459\u2013472.","DOI":"10.1145\/1925844.1926437"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510106"},{"key":"e_1_3_2_2_10_1","volume-title":"Kaiser","author":"Aye Gareth Ari","year":"2020","unstructured":"Gareth Ari Aye and Gail E. Kaiser. 2020. Sequence Model Design for Code Completion in the Modern IDE. CoRR, abs\/2004.05249 (2020), arxiv:2004.05249. arxiv:2004.05249"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2206.01335"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2203.03771"},{"key":"e_1_3_2_2_13_1","unstructured":"David Bieber Charles Sutton Hugo Larochelle and Daniel Tarlow. 2020. Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. In NeurIPS. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/62326dc7c4f7b849d6f013ba46489d6c-Abstract.html"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2785841"},{"key":"e_1_3_2_2_15_1","volume-title":"Engler","author":"Cadar Cristian","year":"2008","unstructured":"Cristian Cadar, Daniel Dunbar, and Dawson R. Engler. 2008. KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs. In OSDI. USENIX."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678559"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Qibin Chen Jeremy Lacomis Edward J. Schwartz Graham Neubig Bogdan Vasilescu and Claire Le Goues. 2022. VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning. In ICSE.","DOI":"10.1145\/3510003.3510162"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Koen Claessen and John Hughes. 2000. QuickCheck: a lightweight tool for random testing of Haskell programs. In ICFP. 268\u2013279.","DOI":"10.1145\/357766.351266"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"James A. Clause Wanchun Li and Alessandro Orso. 2007. Dytan: a generic dynamic taint analysis framework. In ISSTA. ACM 196\u2013206.","DOI":"10.1145\/1273463.1273490"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.602"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1449764.1449790"},{"key":"e_1_3_2_2_22_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR, abs\/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. CoRR, abs\/1810.04805 (2018), arxiv:1810.04805. arxiv:1810.04805"},{"key":"e_1_3_2_2_23_1","volume-title":"Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs. In ICLR. OpenReview.net. https:\/\/openreview.net\/forum?id=SJeqs6EFvB","author":"Dinella Elizabeth","year":"2020","unstructured":"Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. 2020. Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs. In ICLR. OpenReview.net. https:\/\/openreview.net\/forum?id=SJeqs6EFvB"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Aryaz Eghbali and Michael Pradel. 2022. DynaPyt: A Dynamic Analysis Framework for Python. In ESEC\/FSE. ACM.","DOI":"10.1145\/3540250.3549126"},{"key":"e_1_3_2_2_25_1","volume-title":"Workshop on Dynamic Analysis (WODA).","author":"Ernst Michael D.","year":"2003","unstructured":"Michael D. Ernst. 2003. Static and dynamic analysis: Synergy and duality. In Workshop on Dynamic Analysis (WODA)."},{"key":"e_1_3_2_2_26_1","unstructured":"Daya Guo et al.. 2021. GraphCodeBERT: Pre-training Code Representations with Data Flow. In ICLR. OpenReview.net. https:\/\/openreview.net\/forum?id=jLoC4ez43PZ"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"He Ye et al.. 2022. Neural Program Repair with Execution-based Backpropagation. In ICSE.","DOI":"10.1145\/3510003.3510222"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Kexin Pei et al.. 2021. StateFormer: Fine-Grained Type Recovery from Binaries Using Generative State Modeling. In ESEC\/FSE.","DOI":"10.1145\/3468264.3468607"},{"key":"e_1_3_2_2_29_1","unstructured":"Mark Chen et al.. 2021. Evaluating Large Language Models Trained on Code. CoRR abs\/2107.03374 (2021) arXiv:2107.03374. arxiv:2107.03374"},{"key":"e_1_3_2_2_30_1","unstructured":"Maxwell Nye et al.. 2021. Show Your Work: Scratchpads for Intermediate Computation with Language Models. CoRR abs\/2112.00114 (2021) arXiv:2112.00114. arxiv:2112.00114"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2012.63"},{"key":"e_1_3_2_2_32_1","unstructured":"Marko Vasic et al.. 2019. Neural Program Repair by Jointly Learning to Localize and Repair. In ICLR."},{"key":"e_1_3_2_2_33_1","volume-title":"Jigsaw: Large Language Models meet Program Synthesis. In ICSE.","author":"Naman","unstructured":"Naman Jain et al.. 2022. Jigsaw: Large Language Models meet Program Synthesis. In ICSE."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Rahul Gupta et al.. 2017. DeepFix: Fixing Common C Language Errors by Deep Learning. In AAAI. http:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14603","DOI":"10.1609\/aaai.v31i1.10742"},{"key":"e_1_3_2_2_35_1","unstructured":"Zimin Chen et al.. 2019. SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair. IEEE TSE."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","unstructured":"Zhangyin Feng et al.. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In EMNLP. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.139 10.18653\/v1\/2020.findings-emnlp.139","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","unstructured":"Kasra Ferdowsifard Shraddha Barke Hila Peleg Sorin Lerner and Nadia Polikarpova. 2021. LooPy: interactive program synthesis with control structures. OOPSLA https:\/\/doi.org\/10.1145\/3485530 10.1145\/3485530","DOI":"10.1145\/3485530"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2025113.2025179"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1453101.1453150"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Patrice Godefroid. 2014. Micro execution. In ICSE. 539\u2013549.","DOI":"10.1145\/2568225.2568273"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Patrice Godefroid Nils Klarlund and Koushik Sen. 2005. DART: directed automated random testing. In PLDI. ACM 213\u2013223.","DOI":"10.1145\/1064978.1065036"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180167"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","unstructured":"Vincent J. Hellendoorn Christian Bird Earl T. Barr and Miltiadis Allamanis. 2018. Deep learning type inference. In ESEC\/FSE. 152\u2013162. https:\/\/doi.org\/10.1145\/3236024.3236051 10.1145\/3236024.3236051","DOI":"10.1145\/3236024.3236051"},{"key":"e_1_3_2_2_44_1","unstructured":"Vincent J. Hellendoorn Charles Sutton Rishabh Singh Petros Maniatis and David Bieber. 2020. Global Relational Models of Source Code. In ICLR. https:\/\/openreview.net\/forum?id=B1lnbRNtwr"},{"key":"e_1_3_2_2_45_1","volume-title":"Executability of Python Snippets in Stack Overflow. CoRR, abs\/1907.04908","author":"Monir Hossain Md.","year":"2019","unstructured":"Md. Monir Hossain, Nima Mahmoudi, Changyuan Lin, Hamzeh Khazaei, and Abram Hindle. 2019. Executability of Python Snippets in Stack Overflow. CoRR, abs\/1907.04908 (2019), arXiv:1907.04908. arxiv:1907.04908"},{"key":"e_1_3_2_2_46_1","unstructured":"Rafael-Michael Karampatsis and Charles Sutton. 2020. SCELMo: Source Code Embeddings from Language Models. https:\/\/openreview.net\/pdf?id=ryxnJlSKvr"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052674"},{"key":"e_1_3_2_2_48_1","unstructured":"Seohyun Kim Jinman Zhao Yuchi Tian and Satish Chandra. 2021. Code Prediction by Feeding Trees to Transformers. In ICSE."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304068"},{"key":"e_1_3_2_2_50_1","volume-title":"Nguyen","author":"Li Yi","year":"2020","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020. DLFix: Context-based Code Transformation Learning for Automated Program Repair. In ICSE."},{"key":"e_1_3_2_2_51_1","volume-title":"Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong.","author":"Li Zhen","year":"2018","unstructured":"Zhen Li, Shouhuai Xu Deqing Zou and, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018. VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. In NDSS."},{"key":"e_1_3_2_2_52_1","volume-title":"Jing Kai Siow, and Yang Liu","author":"Liu Shangqing","year":"2021","unstructured":"Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2021. Retrieval-Augmented Generation for Code Summarization via Hybrid GNN. In ICLR. OpenReview.net. https:\/\/openreview.net\/forum?id=zv-typ1gPxA"},{"key":"e_1_3_2_2_53_1","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In ICLR. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","unstructured":"Stephan Lukasczyk Florian Kroi\u00df and Gordon Fraser. 2020. Automated Unit Test Generation for Python. In SSBSE. 9\u201324. https:\/\/doi.org\/10.1007\/978-3-030-59762-7_2 10.1007\/978-3-030-59762-7_2","DOI":"10.1007\/978-3-030-59762-7_2"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397369"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","unstructured":"Rabee Sohail Malik Jibesh Patra and Michael Pradel. 2019. NL2Type: Inferring JavaScript function types from natural language information. In ICSE. https:\/\/doi.org\/10.1109\/ICSE.2019.00045 10.1109\/ICSE.2019.00045","DOI":"10.1109\/ICSE.2019.00045"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","unstructured":"Bj\u00f6rn Mathis Rahul Gopinath Micha\u00ebl Mera Alexander Kampmann Matthias H\u00f6schele and Andreas Zeller. 2019. Parser-directed fuzzing. In PLDI. https:\/\/doi.org\/10.1145\/3314221.3314651 10.1145\/3314221.3314651","DOI":"10.1145\/3314221.3314651"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","unstructured":"Bj\u00f6rn Mathis Rahul Gopinath and Andreas Zeller. 2020. Learning input tokens for effective fuzzing. In ISSTA. https:\/\/doi.org\/10.1145\/3395363.3397348 10.1145\/3395363.3397348","DOI":"10.1145\/3395363.3397348"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3158117"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"crossref","unstructured":"Amir M Mir Evaldas Lato\u0161kinas Sebastian Proksch and Georgios Gousios. 2022. Type4Py: Practical deep similarity learning-based type inference for Python. In ICSE.","DOI":"10.1145\/3510003.3510124"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"crossref","unstructured":"Lili Mou Ge Li Lu Zhang Tao Wang and Zhi Jin. 2016. Convolutional Neural Networks over Tree Structures for Programming Language Processing. In AAAI.","DOI":"10.1609\/aaai.v30i1.10139"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"crossref","unstructured":"Carlos Pacheco Shuvendu K. Lahiri Michael D. Ernst and Thomas Ball. 2007. Feedback-Directed Random Test Generation. In ICSE.","DOI":"10.1109\/ICSE.2007.37"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510144"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"crossref","unstructured":"Yun Peng Cuiyun Gao Zongjie Li Bowei Gao David Lo Qirun Zhang and Michael Lyu. 2022. Static inference meets deep learning: a hybrid type inference approach for python. In ICSE.","DOI":"10.1145\/3510003.3510038"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","unstructured":"Hung Phan Hoan Anh Nguyen Ngoc M. Tran Linh H. Truong Anh Tuan Nguyen and Tien N. Nguyen. [n. d.]. Statistical learning of API fully qualified names in code snippets of online forums. In ICSE. https:\/\/doi.org\/10.1145\/3180155.3180230 10.1145\/3180155.3180230","DOI":"10.1145\/3180155.3180230"},{"key":"e_1_3_2_2_66_1","volume-title":"Synchromesh: Reliable Code Generation from Pre-trained Language Models. In ICLR. https:\/\/openreview.net\/forum?id=KmtVD97J43e","author":"Poesia Gabriel","year":"2022","unstructured":"Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022. Synchromesh: Reliable Code Generation from Pre-trained Language Models. In ICLR. https:\/\/openreview.net\/forum?id=KmtVD97J43e"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460348"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","unstructured":"Michael Pradel Georgios Gousios Jason Liu and Satish Chandra. 2020. TypeWriter: Neural Type Prediction with Search-based Validation. In ESEC\/FSE. https:\/\/doi.org\/10.1145\/3368089.3409715 10.1145\/3368089.3409715","DOI":"10.1145\/3368089.3409715"},{"key":"e_1_3_2_2_69_1","volume-title":"Gross","author":"Pradel Michael","year":"2009","unstructured":"Michael Pradel and Thomas R. Gross. 2009. Automatic Generation of Object Usage Specifications from Large Method Traces. In ASE."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3276517"},{"key":"e_1_3_2_2_71_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. J. Mach. Learn. Res., 21 (2020), 140:1\u2013140:67. http:\/\/jmlr.org\/papers\/v21\/20-074.html","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_72_1","volume-title":"Engler","author":"Ramos David A.","year":"2015","unstructured":"David A. Ramos and Dawson R. Engler. 2015. Under-Constrained Symbolic Execution: Correctness Checking for Real Code. In USENIX. https:\/\/www.usenix.org\/conference\/usenixsecurity15\/technical-sessions\/presentation\/ramos"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"crossref","unstructured":"Veselin Raychev Martin T. Vechev and Andreas Krause. 2015. Predicting Program Properties from \"Big Code\".. In POPL.","DOI":"10.1145\/2676726.2677009"},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/2480362.2480655"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"crossref","unstructured":"Saksham Sachdev Hongyu Li Sifei Luan Seohyun Kim Koushik Sen and Satish Chandra. 2018. Retrieval on source code: a neural code search. In MAPL.","DOI":"10.1145\/3211346.3211353"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2010.26"},{"key":"e_1_3_2_2_77_1","volume-title":"Rezwana Karim Nawrin, and Frank Tip","author":"Selakovic Marija","year":"2018","unstructured":"Marija Selakovic, Michael Pradel, Rezwana Karim Nawrin, and Frank Tip. 2018. Test Generation for Higher-Order Functions in Dynamic Languages. In OOPSLA."},{"key":"e_1_3_2_2_78_1","volume-title":"Jalangi: A Selective Record-Replay and Dynamic Analysis Framework for JavaScript. In ESEC\/FSE.","author":"Sen Koushik","year":"2013","unstructured":"Koushik Sen, Swaroop Kalasapur, Tasneem Brutch, and Simon Gibbs. 2013. Jalangi: A Selective Record-Replay and Dynamic Analysis Framework for JavaScript. In ESEC\/FSE."},{"key":"e_1_3_2_2_79_1","unstructured":"Koushik Sen Darko Marinov and Gul Agha. 2005. CUTE: a concolic unit testing engine for C. In ESEC\/FSE."},{"key":"e_1_3_2_2_80_1","unstructured":"Yannis Smaragdakis and Christoph Csallner. 2007. Combining Static and Dynamic Reasoning for Bug Detection. In TAP."},{"key":"e_1_3_2_2_81_1","doi-asserted-by":"publisher","unstructured":"Weisong Sun Chunrong Fang Yuchen Chen Guanhong Tao Tingxu Han and Quanjun Zhang. 2022. Code Search based on Context-aware Code Translation. In ICSE. https:\/\/doi.org\/10.1145\/3510003.3510140 10.1145\/3510003.3510140","DOI":"10.1145\/3510003.3510140"},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"crossref","unstructured":"Daniel Tarlow Subhodeep Moitra Andrew Rice Zimin Chen Pierre-Antoine Manzagol Charles Sutton and Edward Aftandilian. 2019. Learning to Fix Build Errors with Graph2Diff Neural Networks.","DOI":"10.1145\/3387940.3392181"},{"key":"e_1_3_2_2_83_1","doi-asserted-by":"publisher","unstructured":"Yaza Wainakh Moiz Rauf and Michael Pradel. 2021. IdBench: Evaluating Semantic Representations of Identifier Names in Source Code. In ICSE. https:\/\/doi.org\/10.1109\/ICSE43902.2021.00059 10.1109\/ICSE43902.2021.00059","DOI":"10.1109\/ICSE43902.2021.00059"},{"key":"e_1_3_2_2_84_1","doi-asserted-by":"publisher","unstructured":"Jiawei Wang Li Li and Andreas Zeller. 2021. Restoring Execution Environments of Jupyter Notebooks. In ICSE. https:\/\/doi.org\/10.1109\/ICSE43902.2021.00144 10.1109\/ICSE43902.2021.00144","DOI":"10.1109\/ICSE43902.2021.00144"},{"key":"e_1_3_2_2_85_1","doi-asserted-by":"publisher","unstructured":"Ke Wang and Zhendong Su. 2020. Blended precise semantic program embeddings. In PLDI. https:\/\/doi.org\/10.1145\/3385412.3385999 10.1145\/3385412.3385999","DOI":"10.1145\/3385412.3385999"},{"key":"e_1_3_2_2_86_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_87_1","unstructured":"Jiayi Wei Maruth Goyal Greg Durrett and Isil Dillig. [n. d.]. LambdaNet: Probabilistic Type Inference using Graph Neural Networks. In ICLR. https:\/\/openreview.net\/forum?id=Hkx6hANtwH"},{"key":"e_1_3_2_2_88_1","volume-title":"Hellendoorn","author":"Xu Frank F.","year":"2022","unstructured":"Frank F. Xu, Uri Alon, Graham Neubig, and Vincent J. Hellendoorn. 2022. A Systematic Evaluation of Large Language Models of Code. CoRR, abs\/2202.13169 (2022), arXiv:2202.13169. arxiv:2202.13169"},{"key":"e_1_3_2_2_89_1","unstructured":"Jinlin Yang David Evans Deepali Bhardwaj Thirumalesh Bhat and Manuvir Das. [n. d.]. Perracotta: Mining temporal API rules from imperfect traces. In ICSE. 282\u2013291."},{"key":"e_1_3_2_2_90_1","volume-title":"ICML","author":"Yasunaga Michihiro","unstructured":"Michihiro Yasunaga and Percy Liang. 2021. Break-It-Fix-It: Unsupervised Learning for Program Repair. In ICML. http:\/\/proceedings.mlr.press\/v139\/yasunaga21a.html"},{"key":"e_1_3_2_2_91_1","unstructured":"Michal Zalewski. 2013. American Fuzzy Lop (AFL). https:\/\/lcamtuf.coredump.cx\/afl\/"},{"key":"e_1_3_2_2_92_1","volume-title":"Learning to Execute. CoRR, abs\/1410.4615","author":"Zaremba Wojciech","year":"2014","unstructured":"Wojciech Zaremba and Ilya Sutskever. 2014. Learning to Execute. CoRR, abs\/1410.4615 (2014), arxiv:1410.4615"},{"key":"e_1_3_2_2_93_1","doi-asserted-by":"crossref","unstructured":"Jian Zhang Xu Wang Hongyu Zhang Hailong Sun and Xudong Liu. 2020. Retrieval-based Neural Source Code Summarization. In ICSE.","DOI":"10.1145\/3377811.3380383"},{"key":"e_1_3_2_2_94_1","doi-asserted-by":"crossref","unstructured":"Jian Zhang Xu Wang Hongyu Zhang Hailong Sun Kaixuan Wang and Xudong Liu. 2019. A Novel Neural Source Code Representation based on Abstract Syntax Tree. In ICSE.","DOI":"10.1109\/ICSE.2019.00086"}],"event":{"name":"ESEC\/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","location":"San Francisco CA USA","acronym":"ESEC\/FSE '23","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering"]},"container-title":["Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3611643.3616254","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3611643.3616254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:03Z","timestamp":1750178163000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3611643.3616254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"references-count":94,"alternative-id":["10.1145\/3611643.3616254","10.1145\/3611643"],"URL":"https:\/\/doi.org\/10.1145\/3611643.3616254","relation":{},"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"2023-11-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}