{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T08:43:29Z","timestamp":1780994609546,"version":"3.54.1"},"reference-count":71,"publisher":"Association for Computing Machinery (ACM)","issue":"OOPSLA1","license":[{"start":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T00:00:00Z","timestamp":1651190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Program. Lang."],"published-print":{"date-parts":[[2022,4,29]]},"abstract":"<jats:p>In the past decade, Deep Learning (DL) systems have been widely deployed in various application domains to facilitate our daily life, e.g., natural language processing, healthcare, activity recognition, and autonomous driving. Meanwhile, it is extremely challenging to ensure the correctness of DL systems (e.g., due to their intrinsic nondeterminism), and bugs in DL systems can cause serious consequences and may even threaten human lives. In the literature, researchers have explored various techniques to test, analyze, and verify DL models, since their quality directly affects the corresponding system behaviors. Recently, researchers have also proposed novel techniques for testing the underlying operator-level DL libraries (such as TensorFlow and PyTorch), which provide general binary implementations for each high-level DL operator and are the foundation for running DL models on different hardware platforms. However, there is still limited work targeting the reliability of the emerging tensor compilers (also known as DL compilers), which aim to automatically compile high-level tensor computation graphs directly into high-performance binaries for better efficiency, portability, and scalability than traditional operator-level libraries. Therefore, in this paper, we target the important problem of tensor compiler testing, and have proposed Tzer, a practical fuzzing technique for the widely used TVM tensor compiler. Tzer focuses on mutating the low-level Intermediate Representation (IR) for TVM due to the limited mutation space for the high-level IR. More specifically, Tzer leverages both general-purpose and tensor-compiler-specific mutators guided by coverage feedback for diverse and evolutionary IR mutation; furthermore, since tensor compilers provide various passes (i.e., transformations) for IR optimization, Tzer also performs pass mutation in tandem with IR mutation for more effective fuzzing. Our experimental results show that Tzer substantially outperforms existing fuzzing techniques on tensor compiler testing, with 75% higher coverage and 50% more valuable tests than the 2nd-best technique. Also, different components of Tzer have been validated via ablation study. To date, Tzer has detected 49 previously unknown bugs for TVM, with 37 bugs confirmed and 25 bugs fixed (PR merged).<\/jats:p>","DOI":"10.1145\/3527317","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T15:42:03Z","timestamp":1651246923000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Coverage-guided tensor compiler fuzzing with joint IR-pass mutation"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7122-8625","authenticated-orcid":false,"given":"Jiawei","family":"Liu","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4391-3753","authenticated-orcid":false,"given":"Yuxiang","family":"Wei","sequence":"additional","affiliation":[{"name":"Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8866-2097","authenticated-orcid":false,"given":"Sen","family":"Yang","sequence":"additional","affiliation":[{"name":"Fudan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4628-4219","authenticated-orcid":false,"given":"Yinlin","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5175-2702","authenticated-orcid":false,"given":"Lingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , Manjunath Kudlur , Josh Levenberg , Rajat Monga , Sherry Moore , Derek G. Murray , Benoit Steiner , Paul Tucker , Vijay Vasudevan , Pete Warden , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2016 . TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) . USENIX Association, Savannah, GA. 265\u2013283. isbn:978-1-93 1971-33-1 https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA. 265\u2013283. isbn:978-1-931971-33-1 https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2010.118"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/0613026"},{"key":"e_1_2_1_4_1","unstructured":"Google Security Blog. 2016. Guided in-process fuzzing of Chrome components. https:\/\/security.googleblog.com\/2016\/08\/guided-in-process-fuzzing-of-chrome.html  Google Security Blog. 2016. Guided in-process fuzzing of Chrome components. https:\/\/security.googleblog.com\/2016\/08\/guided-in-process-fuzzing-of-chrome.html"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409748"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2785841"},{"key":"e_1_2_1_7_1","volume-title":"OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields","author":"Cao Zhe","year":"2019","unstructured":"Zhe Cao , Gines Hidalgo , Tomas Simon , Shih-En Wei , and Yaser Sheikh . 2019. OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields . IEEE transactions on pattern analysis and machine intelligence, 43, 1 ( 2019 ), 172\u2013186. Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2019. OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. IEEE transactions on pattern analysis and machine intelligence, 43, 1 (2019), 172\u2013186."},{"key":"e_1_2_1_8_1","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Yan , Haichen Shen , Meghan Cowan , Leyuan Wang , Yuwei Hu , and Luis Ceze . 2018 . TVM: An automated end-to-end optimizing compiler for deep learning . In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) . 578\u2013594. Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, and Luis Ceze. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 578\u2013594."},{"key":"e_1_2_1_9_1","unstructured":"Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759.  Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759."},{"key":"e_1_2_1_10_1","volume-title":"Generating constrained random data with uniform distribution. Journal of functional programming, 25","author":"Claessen Koen","year":"2015","unstructured":"Koen Claessen , Jonas Dureg\u00e5rd , and Micha\u0142 H Pa\u0142 ka. 2015. Generating constrained random data with uniform distribution. Journal of functional programming, 25 ( 2015 ). Koen Claessen, Jonas Dureg\u00e5rd, and Micha\u0142 H Pa\u0142 ka. 2015. Generating constrained random data with uniform distribution. Journal of functional programming, 25 (2015)."},{"key":"e_1_2_1_11_1","unstructured":"Apache TVM Community. 2020. tvm.relay.testing \u2014 tvm 0.8.dev0 documentation. https:\/\/tvm.apache.org\/docs\/api\/python\/relay\/testing.html  Apache TVM Community. 2020. tvm.relay.testing \u2014 tvm 0.8.dev0 documentation. https:\/\/tvm.apache.org\/docs\/api\/python\/relay\/testing.html"},{"key":"e_1_2_1_12_1","volume-title":"The Coq Workshop. 125","author":"D\u00e9n\u00e8s Maxime","year":"2014","unstructured":"Maxime D\u00e9n\u00e8s , Catalin Hritcu , Leonidas Lampropoulos , Zoe Paraskevopoulou , and Benjamin C Pierce . 2014 . QuickChick: Property-based testing for Coq . In The Coq Workshop. 125 , 126. Maxime D\u00e9n\u00e8s, Catalin Hritcu, Leonidas Lampropoulos, Zoe Paraskevopoulou, and Benjamin C Pierce. 2014. QuickChick: Property-based testing for Coq. In The Coq Workshop. 125, 126."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1423"},{"key":"e_1_2_1_14_1","volume-title":"A guide to deep learning in healthcare. Nature medicine, 25, 1","author":"Esteva Andre","year":"2019","unstructured":"Andre Esteva , Alexandre Robicquet , Bharath Ramsundar , Volodymyr Kuleshov , Mark DePristo , Katherine Chou , Claire Cui , Greg Corrado , Sebastian Thrun , and Jeff Dean . 2019. A guide to deep learning in healthcare. Nature medicine, 25, 1 ( 2019 ), 24\u201329. Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean. 2019. A guide to deep learning in healthcare. Nature medicine, 25, 1 (2019), 24\u201329."},{"key":"e_1_2_1_15_1","volume-title":"14th USENIX Workshop on Offensive Technologies (WOOT 20)","author":"Fioraldi Andrea","year":"2020","unstructured":"Andrea Fioraldi , Dominik Maier , Heiko Ei\u00df feldt, and Marc Heuse . 2020 . AFL++: Combining Incremental Steps of Fuzzing Research . In 14th USENIX Workshop on Offensive Technologies (WOOT 20) . USENIX Association. Andrea Fioraldi, Dominik Maier, Heiko Ei\u00df feldt, and Marc Heuse. 2020. AFL++: Combining Incremental Steps of Fuzzing Research. In 14th USENIX Workshop on Offensive Technologies (WOOT 20). USENIX Association."},{"key":"e_1_2_1_16_1","unstructured":"Python Software Foundation. 2021. https:\/\/docs.python.org\/3\/library\/ctypes.html  Python Software Foundation. 2021. https:\/\/docs.python.org\/3\/library\/ctypes.html"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380397"},{"key":"e_1_2_1_18_1","unstructured":"Google. 2015. Keras. https:\/\/keras.io  Google. 2015. Keras. https:\/\/keras.io"},{"key":"e_1_2_1_19_1","volume-title":"XLA: Optimizing Compiler for Machine Learning. https:\/\/www.tensorflow.org\/xla","year":"2016","unstructured":"Google. 2016 . XLA: Optimizing Compiler for Machine Learning. https:\/\/www.tensorflow.org\/xla Google. 2016. XLA: Optimizing Compiler for Machine Learning. https:\/\/www.tensorflow.org\/xla"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2568225.2568278"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"e_1_2_1_22_1","unstructured":"Yixiao Guo Jiawei Liu Guo Li Luo Mai and Hao Dong. 2021. Fast and Flexible Human Pose Estimation with HyperPose. arXiv preprint arXiv:2108.11826.  Yixiao Guo Jiawei Liu Guo Li Luo Mai and Hao Dong. 2021. Fast and Flexible Human Pose Estimation with HyperPose. arXiv preprint arXiv:2108.11826."},{"key":"e_1_2_1_23_1","volume-title":"Fuzzing with Code Fragments. In 21st USENIX Security Symposium (USENIX Security 12)","author":"Holler Christian","year":"2012","unstructured":"Christian Holler , Kim Herzig , and Andreas Zeller . 2012 . Fuzzing with Code Fragments. In 21st USENIX Security Symposium (USENIX Security 12) . USENIX Association, Bellevue, WA. 445\u2013458. isbn:978-93 1971-95-9 https:\/\/www.usenix.org\/conference\/usenixsecurity12\/technical-sessions\/presentation\/holler Christian Holler, Kim Herzig, and Andreas Zeller. 2012. Fuzzing with Code Fragments. In 21st USENIX Security Symposium (USENIX Security 12). USENIX Association, Bellevue, WA. 445\u2013458. isbn:978-931971-95-9 https:\/\/www.usenix.org\/conference\/usenixsecurity12\/technical-sessions\/presentation\/holler"},{"key":"e_1_2_1_24_1","unstructured":"Intel. 2017. PlaidML is a framework for making deep learning work everywhere.. https:\/\/github.com\/plaidml\/plaidml  Intel. 2017. PlaidML is a framework for making deep learning work everywhere.. https:\/\/github.com\/plaidml\/plaidml"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359630"},{"key":"e_1_2_1_26_1","unstructured":"Tian Jin Gheorghe-Teodor Bercea Tung D Le Tong Chen Gong Su Haruki Imai Yasushi Negishi Anh Leu Kevin O\u2019Brien and Kiyokuni Kawachiya. 2020. Compiling ONNX Neural Network Models Using MLIR. arXiv preprint arXiv:2008.08272.  Tian Jin Gheorghe-Teodor Bercea Tung D Le Tong Chen Gong Su Haruki Imai Yasushi Negishi Anh Leu Kevin O\u2019Brien and Kiyokuni Kawachiya. 2020. Compiling ONNX Neural Network Models Using MLIR. arXiv preprint arXiv:2008.08272."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.24018"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243804"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01225"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3360607"},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the ACM on Programming Languages, 2, POPL","author":"Lampropoulos Leonidas","year":"2017","unstructured":"Leonidas Lampropoulos , Zoe Paraskevopoulou , and Benjamin C Pierce . 2017 . Generating good generators for inductive relations . Proceedings of the ACM on Programming Languages, 2, POPL (2017), 1\u201330. Leonidas Lampropoulos, Zoe Paraskevopoulou, and Benjamin C Pierce. 2017. Generating good generators for inductive relations. Proceedings of the ACM on Programming Languages, 2, POPL (2017), 1\u201330."},{"key":"e_1_2_1_32_1","volume-title":"MLIR: A compiler infrastructure for the end of Moore\u2019s law. arXiv preprint arXiv:2002.11054.","author":"Lattner Chris","year":"2020","unstructured":"Chris Lattner , Mehdi Amini , Uday Bondhugula , Albert Cohen , Andy Davis , Jacques Pienaar , River Riddle , Tatiana Shpeisman , Nicolas Vasilache , and Oleksandr Zinenko . 2020 . MLIR: A compiler infrastructure for the end of Moore\u2019s law. arXiv preprint arXiv:2002.11054. Chris Lattner, Mehdi Amini, Uday Bondhugula, Albert Cohen, Andy Davis, Jacques Pienaar, River Riddle, Tatiana Shpeisman, Nicolas Vasilache, and Oleksandr Zinenko. 2020. MLIR: A compiler infrastructure for the end of Moore\u2019s law. arXiv preprint arXiv:2002.11054."},{"key":"e_1_2_1_33_1","unstructured":"Chris Arthur Lattner. 2002. LLVM: An infrastructure for multi-stage optimization. Ph.D. Dissertation. University of Illinois at Urbana-Champaign.  Chris Arthur Lattner. 2002. LLVM: An infrastructure for multi-stage optimization. Ph.D. Dissertation. University of Illinois at Urbana-Champaign."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2666356.2594334"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238176"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1186\/s42400-018-0002-y"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3030548"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.6371291"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2946563"},{"key":"e_1_2_1_40_1","volume-title":"Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19, 6","author":"Miotto Riccardo","year":"2018","unstructured":"Riccardo Miotto , Fei Wang , Shuang Wang , Xiaoqian Jiang , and Joel T Dudley . 2018. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19, 6 ( 2018 ), 1236\u20131246. Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley. 2018. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19, 6 (2018), 1236\u20131246."},{"key":"e_1_2_1_41_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) (Proceedings of Machine Learning Research","volume":"4911","author":"Odena Augustus","year":"2019","unstructured":"Augustus Odena , Catherine Olsson , David Andersen , and Ian Goodfellow . 2019 . TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing . In Proceedings of the 36th International Conference on Machine Learning, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) (Proceedings of Machine Learning Research , Vol. 97). PMLR, 4901\u2013 4911 . https:\/\/proceedings.mlr.press\/v97\/odena19a.html Augustus Odena, Catherine Olsson, David Andersen, and Ian Goodfellow. 2019. TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. In Proceedings of the 36th International Conference on Machine Learning, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) (Proceedings of Machine Learning Research, Vol. 97). PMLR, 4901\u20134911. https:\/\/proceedings.mlr.press\/v97\/odena19a.html"},{"key":"e_1_2_1_42_1","unstructured":"David Pankratz. 2020. TVMFuzz: Fuzzing Tensor-level Intermediate Representation in TVM. https:\/\/github.com\/dpankratz\/TVMFuzz  David Pankratz. 2020. TVMFuzz: Fuzzing Tensor-level Intermediate Representation in TVM. https:\/\/github.com\/dpankratz\/TVMFuzz"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2003.1214317"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00067"},{"key":"e_1_2_1_45_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , and Luca Antiga . 2019 . Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32 (2019), 8026\u20138037. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, and Luca Antiga. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32 (2019), 8026\u20138037."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3361566"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00107"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499370.2462176"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3194085.3194087"},{"key":"e_1_2_1_50_1","volume-title":"Glow: Graph lowering compiler techniques for neural networks. arXiv preprint arXiv:1805.00907.","author":"Rotem Nadav","year":"2018","unstructured":"Nadav Rotem , Jordan Fix , Saleem Abdulrasool , Garret Catron , Summer Deng , Roman Dzhabarov , Nick Gibson , James Hegeman , Meghan Lele , and Roman Levenstein . 2018 . Glow: Graph lowering compiler techniques for neural networks. arXiv preprint arXiv:1805.00907. Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Garret Catron, Summer Deng, Roman Dzhabarov, Nick Gibson, James Hegeman, Meghan Lele, and Roman Levenstein. 2018. Glow: Graph lowering compiler techniques for neural networks. arXiv preprint arXiv:1805.00907."},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Kosta Serebryany. 2016. Continuous fuzzing with libfuzzer and addresssanitizer. In 2016 IEEE Cybersecurity Development (SecDev). 157\u2013157.  Kosta Serebryany. 2016. Continuous fuzzing with libfuzzer and addresssanitizer. In 2016 IEEE Cybersecurity Development (SecDev). 157\u2013157.","DOI":"10.1109\/SecDev.2016.043"},{"key":"e_1_2_1_52_1","unstructured":"Kostya Serebryany. 2017. OSS-Fuzz-Google\u2019s continuous fuzzing service for open source software.  Kostya Serebryany. 2017. OSS-Fuzz-Google\u2019s continuous fuzzing service for open source software."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2014.110"},{"key":"e_1_2_1_54_1","unstructured":"Bjarne Stroustrup. 2017. Why doesn\u2019t C++ provide a \"finally\" construct? https:\/\/www.stroustrup.com\/bs_faq2.html#finally  Bjarne Stroustrup. 2017. Why doesn\u2019t C++ provide a \"finally\" construct? https:\/\/www.stroustrup.com\/bs_faq2.html#finally"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3315508.3329973"},{"key":"e_1_2_1_57_1","volume-title":"\u0141 ukasz Kaiser, and Illia Polosukhin","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141 ukasz Kaiser, and Illia Polosukhin . 2017 . Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). 30, Curran Associates, Inc .. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). 30, Curran Associates, Inc.. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409761"},{"key":"e_1_2_1_59_1","unstructured":"Anjiang Wei Yinlin Deng Chenyuan Yang and Lingming Zhang. 2022. Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source. arXiv preprint arXiv:2201.06589.  Anjiang Wei Yinlin Deng Chenyuan Yang and Lingming Zhang. 2022. Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source. arXiv preprint arXiv:2201.06589."},{"key":"e_1_2_1_60_1","unstructured":"Glibc Wiki. 2016. Fuzzing libc. https:\/\/sourceware.org\/glibc\/wiki\/FuzzingLibc  Glibc Wiki. 2016. Fuzzing libc. https:\/\/sourceware.org\/glibc\/wiki\/FuzzingLibc"},{"key":"e_1_2_1_61_1","volume-title":"Visualizing dataflow graphs of deep learning models in tensorflow","author":"Wongsuphasawat Kanit","year":"2017","unstructured":"Kanit Wongsuphasawat , Daniel Smilkov , James Wexler , Jimbo Wilson , Dandelion Mane , Doug Fritz , Dilip Krishnan , Fernanda B Vi\u00e9gas , and Martin Wattenberg . 2017. Visualizing dataflow graphs of deep learning models in tensorflow . IEEE transactions on visualization and computer graphics, 24, 1 ( 2017 ), 1\u201312. Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mane, Doug Fritz, Dilip Krishnan, Fernanda B Vi\u00e9gas, and Martin Wattenberg. 2017. Visualizing dataflow graphs of deep learning models in tensorflow. IEEE transactions on visualization and computer graphics, 24, 1 (2017), 1\u201312."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510174"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/1993498.1993532"},{"key":"e_1_2_1_64_1","volume-title":"Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13, 3","author":"Young Tom","year":"2018","unstructured":"Tom Young , Devamanyu Hazarika , Soujanya Poria , and Erik Cambria . 2018. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13, 3 ( 2018 ), 55\u201375. Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13, 3 (2018), 55\u201375."},{"key":"e_1_2_1_65_1","unstructured":"Michal Zalewski. 2018. American Fuzzing Lop (AFL). https:\/\/lcamtuf.coredump.cx\/afl\/  Michal Zalewski. 2018. American Fuzzing Lop (AFL). https:\/\/lcamtuf.coredump.cx\/afl\/"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238187"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3062341.3062379"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453483.3454106"},{"key":"e_1_2_1_69_1","volume-title":"History-Driven Test Program Synthesis for JVM Testing. In 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE).","author":"Zhao Yingquan","year":"2022","unstructured":"Yingquan Zhao , Zan Wang , Junjie Chen , Mengdi Liu , Mingyuan Wu , Yuqun Zhang , and Lingming Zhang . 2022 . History-Driven Test Program Synthesis for JVM Testing. In 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE). Yingquan Zhao, Zan Wang, Junjie Chen, Mengdi Liu, Mingyuan Wu, Yuqun Zhang, and Lingming Zhang. 2022. History-Driven Test Program Synthesis for JVM Testing. In 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE)."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417260"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380422"}],"container-title":["Proceedings of the ACM on Programming Languages"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3527317","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3527317","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:52Z","timestamp":1750191532000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3527317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,29]]},"references-count":71,"journal-issue":{"issue":"OOPSLA1","published-print":{"date-parts":[[2022,4,29]]}},"alternative-id":["10.1145\/3527317"],"URL":"https:\/\/doi.org\/10.1145\/3527317","relation":{},"ISSN":["2475-1421"],"issn-type":[{"value":"2475-1421","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,29]]},"assertion":[{"value":"2022-04-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}