{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:03:28Z","timestamp":1770228208417,"version":"3.49.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T00:00:00Z","timestamp":1745625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100018735","name":"Ant Group","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018735","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key R&D Program of Zhejiang","award":["2022C01018"],"award-info":[{"award-number":["2022C01018"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62102359"],"award-info":[{"award-number":["62102359"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ministry of Education, Singapore under its Academic Research Fund Tier 2","award":["T2EP20222-0037"],"award-info":[{"award-number":["T2EP20222-0037"]}]},{"name":"Cryptography Research Experimental Environment Platform Construction","award":["202204"],"award-info":[{"award-number":["202204"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>\n            The concept of\n            <jats:bold>Deep Learning (DL)<\/jats:bold>\n            compiler was proposed to deploy DL models more efficiently on diverse hardware through optimization techniques. As one of the most popular DL compilers, TVM incorporates three levels (high-level, schedule, and low-level) of optimizations, which can inadvertently introduce code logic bugs and build failure bugs. Among these optimizations, scheduling optimization is the core component of DL compilers, which ensures the acceleration of models on all devices. However, the existing works only focus on the testing of high-level and low-level optimizations in TVM, fail to take the most important and challenging intermediate scheduling optimization layer into consideration.\n          <\/jats:p>\n          <jats:p>\n            To fill the gap, we propose a\n            <jats:bold>\n              Scheduling Optimization Oriented Fuzzer (\n              <jats:sc>Scuzer<\/jats:sc>\n              )\n            <\/jats:bold>\n            for TVM, which is specially designed to effectively detect bugs introduced by the scheduling optimization. In particular,\n            <jats:sc>Scuzer<\/jats:sc>\n            first proposes a set of schedule-triggering mutators to actively trigger many scheduling optimizations. Meanwhile, observing that scheduling optimization is closely coupled with program dataflow and operator type,\n            <jats:sc>Scuzer<\/jats:sc>\n            additionally proposes a set of structure-enriching mutators to enrich the structure of dataflows and operators. Based on these carefully designed mutators,\n            <jats:sc>Scuzer<\/jats:sc>\n            then devises a multi-objective algorithm that can adaptively select different combinations of objectives at each period to guide the selection of seeds and mutators during fuzzing. We conduct extensive experiments comparing with three state-of-the-art fuzzers that can be applied in testing scheduling optimization to evaluate the effectiveness of\n            <jats:sc>Scuzer<\/jats:sc>\n            . The experimental results demonstrate that\n            <jats:sc>Scuzer<\/jats:sc>\n            outperforms the 2nd-best state-of-the-art fuzzer by 7.4% in edge coverage and achieves 7\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            improvement in rule-operator coverage.\n            <jats:sc>Scuzer<\/jats:sc>\n            has successfully detected 17 previously unknown bugs (9 are inconsistent results and 5 are inconsistent compilations) in TVM, out of which 10 have been confirmed and 5 been fixed.\n          <\/jats:p>","DOI":"10.1145\/3705308","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T14:48:06Z","timestamp":1734965286000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["S\n            <scp>cuzer<\/scp>\n            : A Scheduling Optimization Fuzzer for TVM"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4482-3430","authenticated-orcid":false,"given":"Xiangxiang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Zhejiang University, Hangzhou, China and NGICS Platform, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5048-2516","authenticated-orcid":false,"given":"Xingwei","family":"Lin","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-7635","authenticated-orcid":false,"given":"Jingyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3545-1392","authenticated-orcid":false,"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3100-0534","authenticated-orcid":false,"given":"Jiashui","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China and Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1936-2840","authenticated-orcid":false,"given":"Wenhai","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China and NGICS Platform, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation OSDI \u201916)","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, et al. 2016. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation OSDI \u201916). USENIX Association, 265\u2013283."},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"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, Luca Antiga, et al. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the Advances in Neural Information Processing Systems. H. Wallach, H. Larochelle, A. Beygelzimer, F. d\u2019Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32, Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf"},{"key":"e_1_3_2_4_2","first-page":"579","volume-title":"Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI \u201918)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, et al. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI \u201918). USENIX Association, 579\u2013594."},{"key":"e_1_3_2_5_2","unstructured":"Nadav Rotem Jordan Fix Saleem Abdulrasool Garret Catron Summer Deng Roman Dzhabarov Nick Gibson James Hegeman Meghan Lele Roman Levenstein et al. 2018. Glow: Graph lowering compiler techniques for neural networks. arXiv:1805.00907. Retrieved from https:\/\/arxiv.org\/abs\/1805.00907"},{"key":"e_1_3_2_6_2","unstructured":"Scott Cyphers Arjun K. Bansal Anahita Bhiwandiwalla Jayaram Bobba Matthew Brookhart Avijit Chakraborty Will Constable Christian Convey Leona Cook Omar Kanawi et al. 2018. Intel nGraph: An intermediate representation compiler and executor for deep learning. arXiv:1801.08058. Retrieved from https:\/\/arxiv.org\/abs\/1801.08058"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3030548"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3527317"},{"key":"e_1_3_2_9_2","unstructured":"Tianqi Chen Mu Li Yutian Li Min Lin Naiyan Wang Minjie Wang Tianjun Xiao Bing Xu Chiyuan Zhang and Zheng Zhang. 2015. MxNet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv:1512.01274. Retrieved from https:\/\/arxiv.org\/abs\/1512.01274"},{"key":"e_1_3_2_10_2","first-page":"863","volume-title":"Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201920)","author":"Zheng Lianmin","year":"2020","unstructured":"Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, et al. 2020. Ansor: Generating high-performance tensor programs for deep learning. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201920), 863\u2013879."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2499370.2462176"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468591"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3575707"},{"key":"e_1_3_2_14_2","unstructured":"David Pankratz. 2020. TVMFuzz: Fuzzing Tensor-Level Intermediate Representation in TVM. Retrieved March 18 2024 from https:\/\/github.com\/dpankratz\/TVMFuzz"},{"key":"e_1_3_2_15_2","unstructured":"Ehsan M. Kermani. 2024. Tensor Expression API. Retrieved March 18 2024 from https:\/\/tvm.apache.org\/docs\/reference\/api\/python\/te.html"},{"key":"e_1_3_2_16_2","first-page":"1949","volume-title":"Proceedings of the 28th USENIX Security Symposium (USENIX Security \u201919)","author":"Lyu Chenyang","year":"2019","unstructured":"Chenyang Lyu, Shouling Ji, Chao Zhang, Yuwei Li, Wei-Han Lee, Yu Song, and Raheem Beyah. 2019. MOPT: Optimized mutation scheduling for fuzzers. In Proceedings of the 28th USENIX Security Symposium (USENIX Security \u201919). USENIX Association, Santa Clara, CA, 1949\u20131966. Retrieved from https:\/\/www.usenix.org\/conference\/usenixsecurity19\/presentation\/lyu"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2022.24314"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/SecDev.2016.043"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409761"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3489048.3522655"},{"key":"e_1_3_2_21_2","first-page":"848","volume-title":"Proceedings of Machine Learning and Systems","volume":"4","author":"Zheng Bojian","year":"2022","unstructured":"Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, and Gennady Pekhimenko. 2022. DietCode: Automatic optimization for dynamic tensor programs. Proceedings of Machine Learning and Systems 4 (2022), 848\u2013863."},{"key":"e_1_3_2_22_2","first-page":"204","volume-title":"Proceedings of Machine Learning and Systems","volume":"4","author":"Xing Jiarong","year":"2022","unstructured":"Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, and Yibo Zhu. 2022. Bolt: Bridging the gap between auto-tuners and hardware-native performance. Proceedings of Machine Learning and Systems 4 (2022), 204\u2013216."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503222.3507744"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503222.3507764"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00017"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3512345"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597926.3598053"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.214548"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/71.86109"},{"issue":"426","key":"e_1_3_2_30_2","first-page":"4","article-title":"The art of computer programming","volume":"3","author":"Donald E. Knuth","year":"1999","unstructured":"E. Knuth Donald. 1999. The art of computer programming. Sorting and Searching 3, 426\u2013458 (1999), 4.","journal-title":"Sorting and Searching"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/135226.135233"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/71.544354"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.2307\/1427934"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00105"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO51591.2021.9370308"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534220"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510041"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549085"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00107"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/1993498.1993532"},{"key":"e_1_3_2_41_2","first-page":"445","volume-title":"Proceedings of the 21st USENIX Security Symposium (USENIX Security \u201912)","author":"Holler Christian","year":"2012","unstructured":"Christian Holler, Kim Herzig, and Andreas Zeller. 2012. Fuzzing with code fragments. In Proceedings of the 21st USENIX Security Symposium (USENIX Security \u201912). USENIX Association, Bellevue, WA, 445\u2013458. Retrieved from https:\/\/www.usenix.org\/conference\/usenixsecurity12\/technical-sessions\/presentation\/holler"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.2197\/ipsjtsldm.7.91"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2908080.2908095"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/2666356.2594334"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/2813885.2737986"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/2983990.2984038"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705308","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:02Z","timestamp":1750295882000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,26]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3705308"],"URL":"https:\/\/doi.org\/10.1145\/3705308","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,26]]},"assertion":[{"value":"2024-03-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}