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Eng."],"published-print":{"date-parts":[[2025,6,19]]},"abstract":"<jats:p>WebAssembly has become the preferred smart contract format for various blockchain platforms due to its high portability and near-native execution speed. To effectively understand WebAssembly contracts, it is crucial to recover high-level type signatures because of the limited type information that WebAssembly provides. However, existing studies on type inference for smart contracts primarily center around Ethereum Virtual Machine bytecode, which is not applicable to WebAssembly owing to their differing targets and runtime semantics. This paper introduces WasmHint, a novel solution that leverages deep learning inference to automatically recover high-level parameter and return types from WebAssembly contracts. More specifically, WasmHint constructs a wCFG representation to clarify dependencies within WebAssembly code and simulates its execution to capture type-related operational information. By learning comprehensive code semantics, it infers parameter and return types, with a semantic corrector designed to enhance information coordination. We conduct experiments on a newly constructed dataset containing 77,208 WebAssembly contract functions. The results demonstrate that WasmHint achieves inference accuracies of 80.0% for parameter types and 95.8% for return types, with average improvements of 86.6% and 34.0% over the baseline methods, respectively.<\/jats:p>","DOI":"10.1145\/3729388","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T15:15:34Z","timestamp":1750346134000},"page":"2665-2688","source":"Crossref","is-referenced-by-count":2,"title":["Recasting Type Hints from WebAssembly Contracts"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9886-0460","authenticated-orcid":false,"given":"Kunsong","family":"Zhao","sequence":"first","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4382-577X","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-100X","authenticated-orcid":false,"given":"Weimin","family":"Chen","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9082-3208","authenticated-orcid":false,"given":"Xiapu","family":"Luo","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9165-8331","authenticated-orcid":false,"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6388-2571","authenticated-orcid":false,"given":"Guozhu","family":"Meng","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7610-4736","authenticated-orcid":false,"given":"Yajin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2019. Eveem. https:\/\/eveem.org"},{"key":"e_1_2_1_2_1","unstructured":"2019. EVM bytecode decompiler. https:\/\/github.com\/MrLuit\/evm"},{"key":"e_1_2_1_3_1","unstructured":"2021. Online solidity decompiler. https:\/\/ethervm.io\/decompile"},{"key":"e_1_2_1_4_1","unstructured":"2022. Ethereum smart contract decompiler. https:\/\/www.pnfsoftware.com\/blog\/ethereum-smart-contract-decompiler"},{"key":"e_1_2_1_5_1","unstructured":"2024. Astar. https:\/\/astar.network\/"},{"key":"e_1_2_1_6_1","unstructured":"2024. CosmWasm. https:\/\/cosmwasm.com\/"},{"key":"e_1_2_1_7_1","unstructured":"2024. Customizing Builds with Release Profiles in Rust. https:\/\/doc.rust-lang.org\/book\/ch14-01-release-profiles.html"},{"key":"e_1_2_1_8_1","unstructured":"2024. EOSIO Overview. https:\/\/developers.eos.io\/manuals\/eos\/latest\/index\/"},{"key":"e_1_2_1_9_1","unstructured":"2024. Framework for building smart contracts in Wasm for the Cosmos SDK. https:\/\/github.com\/CosmWasm\/cosmwasm"},{"key":"e_1_2_1_10_1","unstructured":"2024. LLVM IR. https:\/\/llvm.org\/docs"},{"key":"e_1_2_1_11_1","unstructured":"2024. LLVM Language Reference Manual. https:\/\/llvm.org\/docs\/LangRef.html"},{"key":"e_1_2_1_12_1","unstructured":"2024. Mythril. https:\/\/github.com\/ConsenSys\/mythril"},{"key":"e_1_2_1_13_1","unstructured":"2024. Near Blockchain. https:\/\/near.org\/"},{"key":"e_1_2_1_14_1","unstructured":"2024. POL: One token for all Polygon chains. https:\/\/polygon.technology\/papers\/pol-whitepaper\/"},{"key":"e_1_2_1_15_1","unstructured":"2024. PyTorch Framework. https:\/\/pytorch.org\/"},{"key":"e_1_2_1_16_1","unstructured":"2024. Rust library for writing NEAR smart contracts. https:\/\/github.com\/near\/near-sdk-rs"},{"key":"e_1_2_1_17_1","unstructured":"2024. Rust Programming Language. https:\/\/www.rust-lang.org\/"},{"key":"e_1_2_1_18_1","unstructured":"2024. The Rust reference: types. https:\/\/doc.rustlang.org\/reference\/types.html"},{"key":"e_1_2_1_19_1","unstructured":"2024. Solana Blockchain. https:\/\/solana.com\/"},{"key":"e_1_2_1_20_1","unstructured":"2024. The Solidity Contract-Oriented Programming Language. https:\/\/github.com\/ethereum\/solidity\/"},{"key":"e_1_2_1_21_1","unstructured":"2024. Transformers: State-of-the-art Machine Learning for Pytorch TensorFlow and JAX.. https:\/\/github.com\/huggingface\/transformers"},{"key":"e_1_2_1_22_1","unstructured":"2024. WebAssembly. https:\/\/webassembly.org\/"},{"key":"e_1_2_1_23_1","unstructured":"2024. The WebAssembly binary toolkit. https:\/\/github.com\/WebAssembly\/wabt"},{"key":"e_1_2_1_24_1","unstructured":"2024. WebAssembly functions. https:\/\/webassembly.github.io\/spec\/core\/syntax\/modules.html#functions"},{"key":"e_1_2_1_25_1","unstructured":"2024. WebAssembly opcodes. https:\/\/pengowray.github.io\/wasm-ops\/"},{"key":"e_1_2_1_26_1","unstructured":"2024. WebAssembly-Rust Programming Language. https:\/\/www.rust-lang.org\/what\/wasm"},{"key":"e_1_2_1_27_1","unstructured":"2024. WebAssembly types. https:\/\/webassembly.github.io\/spec\/core\/syntax\/types.html"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385412.3385997"},{"key":"e_1_2_1_30_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."},{"key":"e_1_2_1_31_1","volume-title":"Scheduled sampling for sequence prediction with recurrent neural networks. Advances in Neural Information Processing Systems (NeurIPS), 28","author":"Bengio Samy","year":"2015","unstructured":"Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015. 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WRTester: Differential Testing of WebAssembly Runtimes via Semantic-aware Binary Generation. arXiv preprint arXiv:2312.10456."},{"key":"e_1_2_1_35_1","first-page":"15084","article-title":"Decision transformer: Reinforcement learning via sequence modeling","volume":"34","author":"Chen Lili","year":"2021","unstructured":"Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. 2021. Decision transformer: Reinforcement learning via sequence modeling. 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In Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering, (SEKE). 537\u2013542."},{"key":"e_1_2_1_50_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."},{"key":"e_1_2_1_51_1","first-page":"1","article-title":"Cosmos whitepaper","volume":"27","author":"Kwon Jae","year":"2019","unstructured":"Jae Kwon and Ethan Buchman. 2019. Cosmos whitepaper. A Netw. Distrib. Ledgers, 27 (2019), 1\u201332.","journal-title":"A Netw. Distrib. Ledgers"},{"key":"e_1_2_1_52_1","volume-title":"Proceedings of 2004 International Symposium on Code Generation and Optimization (CGO).. 75\u201386","author":"Lattner Chris","year":"2004","unstructured":"Chris Lattner and Vikram Adve. 2004. LLVM: A compilation framework for lifelong program analysis & transformation. In Proceedings of 2004 International Symposium on Code Generation and Optimization (CGO).. 75\u201386."},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the 31st International Conference on Machine Learning (ICML). 1188\u20131196","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. In Proceedings of the 31st International Conference on Machine Learning (ICML). 1188\u20131196."},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 9th International Conference on Internet (ICONI).","author":"Lee Young Jun","year":"2017","unstructured":"Young Jun Lee, Sang-Hoon Choi, Chulwoo Kim, Seung-Ho Lim, and Ki-Woong Park. 2017. Learning binary code with deep learning to detect software weakness. In Proceedings of the 9th International Conference on Internet (ICONI)."},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the 29th USENIX Security Symposium (USENIX SEC). 217\u2013234","author":"Lehmann Daniel","year":"2020","unstructured":"Daniel Lehmann, Johannes Kinder, and Michael Pradel. 2020. Everything old is new again: Binary security of WebAssembly. In Proceedings of the 29th USENIX Security Symposium (USENIX SEC). 217\u2013234."},{"key":"e_1_2_1_56_1","volume-title":"Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 1045\u20131058","author":"Lehmann Daniel","year":"2019","unstructured":"Daniel Lehmann and Michael Pradel. 2019. Wasabi: A framework for dynamically analyzing webassembly. In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 1045\u20131058."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3519939.3523449"},{"key":"e_1_2_1_58_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461.","author":"Lewis Mike","year":"2019","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. 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In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)."},{"key":"e_1_2_1_61_1","volume-title":"Proceedings of the 46th IEEE\/ACM International Conference on Software Engineering (ICSE). 1\u201312","author":"Liang Ruichao","year":"2024","unstructured":"Ruichao Liang, Jing Chen, Kun He, Yueming Wu, Gelei Deng, Ruiying Du, and Cong Wu. 2024. Ponziguard: Detecting ponzi schemes on ethereum with contract runtime behavior graph (crbg). In Proceedings of the 46th IEEE\/ACM International Conference on Software Engineering (ICSE). 1\u201312."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678597"},{"key":"e_1_2_1_63_1","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1162\/tacl_a_00343","article-title":"Multilingual Denoising Pre-training for Neural Machine Translation","volume":"8","author":"Liu Yinhan","year":"2020","unstructured":"Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020. Multilingual Denoising Pre-training for Neural Machine Translation. 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In Proceedings of the 41st IEEE\/ACM International Conference on Software Engineering (ICSE). 304\u2013315."},{"key":"e_1_2_1_68_1","volume-title":"Proceedings of the 44th International Conference on Software Engineering (ICSE). 2241\u20132252","author":"Mir Amir M","year":"2022","unstructured":"Amir M Mir, Evaldas Lato\u0161kinas, Sebastian Proksch, and Georgios Gousios. 2022. Type4py: Practical deep similarity learning-based type inference for python. In Proceedings of the 44th International Conference on Software Engineering (ICSE). 2241\u20132252."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468607"},{"key":"e_1_2_1_70_1","volume-title":"Proceedings of the 44th International Conference on Software Engineering (ICSE). 2019\u20132030","author":"Peng Yun","year":"2022","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 Proceedings of the 44th International Conference on Software Engineering (ICSE). 2019\u20132030."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3616283"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_1_73_1","volume-title":"Language models are unsupervised multitask learners. OpenAI blog, 1, 8","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. 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Codeart: Better code models by attention regularization when symbols are lacking. Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering (FSE), 562\u2013585."},{"key":"e_1_2_1_78_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. Advances in Neural Information Processing Systems (NeurIPS), 30 (2017)."},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.129"},{"key":"e_1_2_1_80_1","first-page":"1","article-title":"Ethereum: A secure decentralised generalised transaction ledger","volume":"151","author":"Wood Gavin","year":"2014","unstructured":"Gavin Wood. 2014. Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper, 151 (2014), 1\u201332.","journal-title":"Ethereum Project Yellow Paper"},{"key":"e_1_2_1_81_1","volume-title":"Proceedings of the 2016 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). 1480\u20131489","author":"Yang Zichao","year":"2016","unstructured":"Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). 1480\u20131489."},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL).","author":"Zhang Wen","year":"2019","unstructured":"Wen Zhang, Yang Feng, Fandong Meng, Di You, and Qun Liu. 2019. Bridging the Gap between Training and Inference for Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)."},{"key":"e_1_2_1_83_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3624743","article-title":"Characterizing and detecting webassembly runtime bugs","volume":"33","author":"Zhang Yixuan","year":"2023","unstructured":"Yixuan Zhang, Shangtong Cao, Haoyu Wang, Zhenpeng Chen, Xiapu Luo, Dongliang Mu, Yun Ma, Gang Huang, and Xuanzhe Liu. 2023. Characterizing and detecting webassembly runtime bugs. 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