{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:25:36Z","timestamp":1792182336618,"version":"4.4.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"FSE","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2024,7,12]]},"abstract":"<jats:p>Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability detection presents a range of challenges, especially as smart contracts continue to increase in complexity. Existing tools perform poorly in terms of efficiency and successful detection rates for vulnerabilities in complex contracts.<\/jats:p>\n                  <jats:p>\n                    To effectively detect reentrancy vulnerabilities in contracts with complex logic, we propose a tool named SliSE. SliSE\u2019s detection process consists of two stages:\n                    <jats:italic toggle=\"yes\">Warning Search<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">Symbolic Execution Verification<\/jats:italic>\n                    . In Stage I, SliSE utilizes program slicing to analyze the\n                    <jats:italic toggle=\"yes\">Inter-contract Program Dependency Graph<\/jats:italic>\n                    (I-PDG) of the contract, and collects suspicious vulnerability information as warnings. In Stage II, symbolic execution is employed to verify the reachability of these warnings, thereby enhancing vulnerability detection accuracy. SliSE obtained the best performance compared with eight state-of-the-art detection tools. It achieved an F1 score of 78.65%, surpassing the highest score recorded by an existing tool of 9.26%. Additionally, it attained a recall rate exceeding 90% for detection of contracts on Ethereum. Overall, SliSE provides a robust and efficient method for detection of Reentrancy vulnerabilities for complex contracts.\n                  <\/jats:p>","DOI":"10.1145\/3643734","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:22:09Z","timestamp":1720779729000},"page":"161-181","source":"Crossref","is-referenced-by-count":44,"title":["Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1439-2989","authenticated-orcid":false,"given":"Zexu","family":"Wang","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"},{"name":"Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-9992","authenticated-orcid":false,"given":"Jiachi","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7761-7269","authenticated-orcid":false,"given":"Yanlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2040-5059","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"},{"name":"Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4783-876X","authenticated-orcid":false,"given":"Weizhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"},{"name":"Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"},{"name":"GuangDong Engineering Technology Research Center of Blockchain, Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Priyanka Bose Dipanjan Das Yanju Chen Yu Feng Christopher Kruegel and Giovanni Vigna. 2022. 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