{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:58:11Z","timestamp":1775822291515,"version":"3.50.1"},"reference-count":80,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Open Research Fund of The State Key Laboratory of Blockchain and Data Security"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62332004, 62276279"],"award-info":[{"award-number":["62332004, 62276279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024B1515020032"],"award-info":[{"award-number":["2024B1515020032"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"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            With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attention, showcasing great capabilities in code analysis tasks. In this article, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT\u2019s effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT\u2019s performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT\u2019s F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its\n            <jats:italic>uncertainty<\/jats:italic>\n            in answering questions; and the\n            <jats:italic>limited length<\/jats:italic>\n            of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities.\n          <\/jats:p>","DOI":"10.1145\/3702973","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T16:29:38Z","timestamp":1730824178000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8423-6757","authenticated-orcid":false,"given":"Chong","family":"Chen","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China and The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7685-944X","authenticated-orcid":false,"given":"Jianzhong","family":"Su","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-9992","authenticated-orcid":false,"given":"Jiachi","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China and The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7761-7269","authenticated-orcid":false,"given":"Yanlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2748-1249","authenticated-orcid":false,"given":"Tingting","family":"Bi","sequence":"additional","affiliation":[{"name":"University of Western Australia, Perth, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1340-3995","authenticated-orcid":false,"given":"Jianxing","family":"Yu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9645-388X","authenticated-orcid":false,"given":"Yanli","family":"Wang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, 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\/0000-0001-9165-8331","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\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Chat-Yuan. 2023. Retrieved from https:\/\/modelscope.cn\/studios\/ClueAI\/ChatYuan-large-v2\/summary"},{"key":"e_1_3_1_3_2","unstructured":"Ethereum. 2023. Retrieved from https:\/\/ethereum.org\/en\/"},{"key":"e_1_3_1_4_2","unstructured":"Xunfei-Xinghuo. 2023. Retrieved from https:\/\/xinghuo.xfyun.cn\/"},{"key":"e_1_3_1_5_2","unstructured":"Rohan Anil Andrew M. Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen et al. 2023. PaLM 2 Technical Report. arXiv:2305.10403. Retrieved from https:\/\/arxiv.org\/abs\/2305.10403"},{"key":"e_1_3_1_6_2","unstructured":"Anthropic. 2023. Claude. Retrieved from https:\/\/claude.ai\/chats"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.58496\/MJCSC\/2023\/002"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833721"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3385412.3385990"},{"key":"e_1_3_1_10_2","unstructured":"Lexi Brent Anton Jurisevic Michael Kong Eric Liu Francois Gauthier Vincent Gramoli Ralph Holz and Bernhard Scholz. 2018. Vandal: A scalable security analysis framework for smart contracts. arXiv:1809.03981. Retrieved from https:\/\/arxiv.org\/abs\/1809.03981"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Jiachi Chen Mingyuan Huang Zewei Lin Peilin Zheng and Zibin Zheng. 2023. To healthier ethereum: A comprehensive and iterative smart contract weakness enumeration. arXiv:2308.10227. Retrieved from https:\/\/arxiv.org\/abs\/2308.10227","DOI":"10.1016\/j.bcra.2024.100258"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678888"},{"key":"e_1_3_1_13_2","unstructured":"ConsenSys. 2021. Consensys\/Mythril: Security Analysis Tool for EVM Bytecode. Retrieved from https:\/\/github.com\/ConsenSys\/mythril"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-2535-6"},{"key":"e_1_3_1_15_2","unstructured":"Isaac David Liyi Zhou Kaihua Qin Dawn Song Lorenzo Cavallaro and Arthur Gervais. 2023. Do you still need a manual smart contract audit? arXiv:2306.12338. Retrieved from https:\/\/arxiv.org\/abs\/2306.12338"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380364"},{"key":"e_1_3_1_18_2","unstructured":"William Entriken Dieter Shirley Jacob Evans and Nastassia Sachs. 2018. ERC-721: Non-Fungible Token Standard. Retrieved from https:\/\/eips.ethereum.org\/EIPS\/eip-721"},{"key":"e_1_3_1_19_2","unstructured":"Ethereum. 2023. ERC-20 Token Standard. Retrieved from https:\/\/ethereum.org\/en\/developers\/docs\/standards\/tokens\/erc-20\/"},{"key":"e_1_3_1_20_2","unstructured":"Ethereum. 2023. Solidity Documentation. Retrieved from https:\/\/docs.soliditylang.org\/en\/v0.8.21\/"},{"key":"e_1_3_1_21_2","unstructured":"Ethereum. 2023. Units and Globally Available Variables. Retrieved from https:\/\/docs.soliditylang.org\/en\/develop\/units-and-global-variables.html"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00128"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/WETSEB.2019.00008"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3415298"},{"key":"e_1_3_1_25_2","first-page":"2757","volume-title":"Proceedings of the 29th USENIX Security Symposium (USENIX Security 20)","author":"Frank Joel","year":"2020","unstructured":"Joel Frank, Cornelius Aschermann, and Thorsten Holz. 2020. ETHBMC: A bounded model checker for smart contracts. In Proceedings of the 29th USENIX Security Symposium (USENIX Security 20). USENIX Association, 2757\u20132774. Retrieved from https:\/\/www.usenix.org\/conference\/usenixsecurity20\/presentation\/frank"},{"key":"e_1_3_1_26_2","unstructured":"Yu Gai Liyi Zhou Kaihua Qin Dawn Song and Arthur Gervais. 2023. Blockchain large language models. arXiv:2304.12749. Retrieved from https:\/\/arxiv.org\/abs\/2304.12749"},{"key":"e_1_3_1_27_2","first-page":"5539","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing SystemsCurran Associates, Inc","volume":"36","author":"Ge Yingqiang","year":"2023","unstructured":"Yingqiang Ge, Wenyue Hua, Kai Mei, jianchao ji, Juntao Tan, Shuyuan Xu, Zelong Li, and Yongfeng Zhang. 2023. OpenAGI: When LLM meets domain experts. In Proceedings of the 37th International Conference on Neural Information Processing Systems. A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), Vol. 36. Curran Associates, Inc., 5539\u20135568. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/1190733f217404edc8a7f4e15a57f301-Paper-Datasets_and_Benchmarks.pdf"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00087"},{"key":"e_1_3_1_29_2","first-page":"305","volume-title":"Proceedings of the International Symposium on Foundations and Practice of Security","author":"Gill Puneet","year":"2022","unstructured":"Puneet Gill, Indrani Ray, Alireza Lotfi Takami, and Mahesh Tripunitara. 2022. Finding unchecked low-level calls with zero false positives and negatives in ethereum smart contracts. In Proceedings of the International Symposium on Foundations and Practice of Security. Springer, 305\u2013321."},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3404366"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900656"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"Jeremy Howard and Sebastian Ruder. 2018. Universal language model fine-tuning for text classification. arXiv:1801.06146. Retrieved from https:\/\/arxiv.org\/abs\/1801.06146","DOI":"10.18653\/v1\/P18-1031"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238177"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25642"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.5555\/1622737.1622748"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23082"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496517"},{"key":"e_1_3_1_38_2","unstructured":"Junlong Li Fan Zhou Shichao Sun Yikai Zhang Hai Zhao and Pengfei Liu. 2024. Dissecting human and LLM preferences. arXiv:2402.11296. Retrieved from https:\/\/arxiv.org\/abs\/2402.11296"},{"key":"e_1_3_1_39_2","volume-title":"Finding Failure-Inducing Test Cases with ChatGPT","author":"Li T.","year":"2023","unstructured":"T. Li, W. Zong, Y. Wang, H. Tian, Y. Wang, S. Cheung, and J. Kramer. 2023. Finding Failure-Inducing Test Cases with ChatGPT. IEEE."},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2022.10.001"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"key":"e_1_3_1_42_2","unstructured":"Itemperance Consulting Pvt. Ltd. 2023. gpt-3-vs-gpt-3\u20135. Retrieved from https:\/\/www.iffort.com\/2023\/03\/31\/gpt-3-vs-gpt-3-5."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978309"},{"key":"e_1_3_1_44_2","unstructured":"Wei Ma Shangqing Liu Wenhan Wang Qiang Hu Ye Liu Cen Zhang Liming Nie and Yang Liu. 2023. The scope of ChatGPT in software engineering: A thorough investigation. arXiv:2305.12138."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00133"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274743"},{"key":"e_1_3_1_47_2","unstructured":"OpenAI. 2023. GPT-3.5. Retrieved from https:\/\/platform.openai.com\/docs\/models\/gpt-3-5"},{"key":"e_1_3_1_48_2","unstructured":"OpenAI. 2023. GPT-4. Retrieved from https:\/\/platform.openai.com\/docs\/models\/gpt-4"},{"key":"e_1_3_1_49_2","unstructured":"OpenAI. 2023. GPT-4 technical report. arXiv:2303.08774.Retrieved from https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"e_1_3_1_50_2","unstructured":"OpenAI. 2023. GPT-OpenAI API. Retrieved from https:\/\/platform.openai.com\/docs\/guides\/gpt"},{"key":"e_1_3_1_51_2","unstructured":"OpenAI. 2023. research-gpt4. Retrieved from https:\/\/openai.com\/research\/gpt-4"},{"key":"e_1_3_1_52_2","unstructured":"OpenAI. 2024. GPT-4o. Retrieved from https:\/\/openai.com\/index\/hello-gpt-4o\/"},{"key":"e_1_3_1_53_2","unstructured":"Openzeppelin solidity. 2018. SafeMath. Retrieved from https:\/\/github.com\/ConsenSysMesh\/openzeppelin-solidity\/blob\/master\/contracts\/math\/SafeMath.sol"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00024"},{"key":"e_1_3_1_55_2","unstructured":"Protofire. 2020. Solhint. Retrieved from https:\/\/github.com\/protofire\/solhint"},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of the USENIX Security Symposium","author":"Qin Kaihua","year":"2023","unstructured":"Kaihua Qin, Stefanos Chaliasos, Liyi Zhou, Benjamin Livshits, Dawn Song, and Arthur Gervais. 2023. The blockchain imitation game. In Proceedings of the USENIX Security Symposium."},{"key":"e_1_3_1_57_2","unstructured":"Kaihua Qin Zhe Ye Zhun Wang Weilin Li Liyi Zhou Chao Zhang Dawn Song and Arthur Gervais. 2023. Towards automated security analysis of smart contracts based on execution property graph. arXiv:2305.14046. Retrieved from https:\/\/arxiv.org\/abs\/2305.14046"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833734"},{"key":"e_1_3_1_59_2","unstructured":"Baptiste Rozi\u00e8re Jonas Gehring Fabian Gloeckle Sten Sootla Itai Gat Xiaoqing Ellen Tan Yossi Adi Jingyu Liu Tal Remez J\u00e9r\u00e9my Rapin et al. 2023. Code Llama: Open foundation models for code. arXiv:2308.12950. Retrieved from https:\/\/arxiv.org\/abs\/2308.12950"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2970495"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.55529\/ijitc.31.17.22"},{"key":"e_1_3_1_62_2","unstructured":"Sunbeom So Myungho Lee Jisu Park Heejo Lee and Hakjoo Oh. 2019. VeriSmart: A highly precise safety verifier for ethereum smart contracts. arXiv:1908.11227. Retrieved from https:\/\/arxiv.org\/abs\/1908.11227"},{"key":"e_1_3_1_63_2","unstructured":"Yuqiang Sun Daoyuan Wu Yue Xue Han Liu Haijun Wang Zhengzi Xu Xiaofei Xie and Yang Liu. 2023. When gpt meets program analysis: Towards intelligent detection of smart contract logic vulnerabilities in gptscan. arXiv:2308.03314. Retrieved from https:\/\/arxiv.org\/abs\/2308.03314"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639117"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.5210\/fm.v2i9.548"},{"key":"e_1_3_1_66_2","unstructured":"Google team. 2023. Google. Retrieved from https:\/\/about.google\/"},{"key":"e_1_3_1_67_2","unstructured":"OpenAI team. 2023. OpenAI. Retrieved from https:\/\/openai.com\/"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3194113.3194115"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP51992.2021.00018"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274737"},{"key":"e_1_3_1_71_2","first-page":"1591","volume-title":"Proceedings of the USENIX Security Symposium (USENIX Security 19)","author":"Torres Christof Ferreira","year":"2019","unstructured":"Christof Ferreira Torres, Mathis Steichen, and Radu State. 2019. The art of the scam: Demystifying honeypots in ethereum smart contracts. In Proceedings of the USENIX Security Symposium (USENIX Security 19), 1591\u20131607."},{"key":"e_1_3_1_72_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et al. 2023. LLaMA: Open and efficient foundation language models. arXiv:2302.13971. Retrieved from https:\/\/arxiv.org\/abs\/2302.13971"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243780"},{"key":"e_1_3_1_74_2","unstructured":"Nuno Veloso. 2021. Conkas. Retrieved from https:\/\/github.com\/nveloso\/conkas"},{"key":"e_1_3_1_75_2","unstructured":"Junjie Wang Yuchao Huang Chunyang Chen Zhe Liu Song Wang and Qing Wang. 2023. Software testing with large language model: Survey landscape and vision. arXiv:2307.07221. Retrieved from https:\/\/arxiv.org\/abs\/2307.07221"},{"key":"e_1_3_1_76_2","unstructured":"David Wong and Mason Hemmel. 2018. Decentralized Application Security Project Top 10 of 2018. Retrieved from https:\/\/dasp.co\/index.html"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417064"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29934"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.12.019"},{"key":"e_1_3_1_80_2","article-title":"Turn the rudder: A beacon of reentrancy detection for smart contracts on ethereum","author":"Zheng Z.","year":"2023","unstructured":"Z. Zheng, N. Zhang, J. Su, Z. Zhong, M. Ye, and J. Chen. 2023. Turn the rudder: A beacon of reentrancy detection for smart contracts on ethereum. In Proceedings of the 45th International Conference on Software Engineering.","journal-title":"Proceedings of the 45th International Conference on Software Engineering"},{"key":"e_1_3_1_81_2","doi-asserted-by":"crossref","unstructured":"Liyi Zhou Kaihua Qin Antoine Cully Benjamin Livshits and Arthur Gervais. 2021. On the just-in-time discovery of profit-generating transactions in DeFi protocols. arXiv:2103.02228. Retrieved from https:\/\/arxiv.org\/abs\/2103.02228","DOI":"10.1109\/SP40001.2021.00113"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3702973","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3702973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:04Z","timestamp":1750295884000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3702973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,28]]},"references-count":80,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3702973"],"URL":"https:\/\/doi.org\/10.1145\/3702973","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,28]]},"assertion":[{"value":"2023-09-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}