{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T14:24:11Z","timestamp":1772979851590,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"abstract":"<jats:p>Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to update once deployed, which requires redeploying the contracts and migrating the data. It means that the observation of smart contract evolution in the real world makes more sense. Hence, in this paper, we conducted the first large-scale empirical study to characterize the evolution of smart contracts in Ethereum. For evolution identification, we presented a contract similarity-based search algorithm, digEvolution, and evaluated its effectiveness with five different search strategies. Then we applied this algorithm to 80,152 on-chain contracts we collected from Ethereum, to dig out the evolution among these contracts. We then explored three research questions. We first studied whether the evolution of smart contracts is common (RQ1), then we studied how do the Gas consumption (RQ2) and the vulnerability (RQ3) of smart contracts vary during the evolution. Our research results show that the evolution of smart contracts is not very common. There are some contract components that have vulnerability but still be called by users. The Gas consumption of most smart contracts doesn\u2019t vary during the evolution, contract is Gas-efficient before and after the evolution. The vulnerability of most smart contracts doesn\u2019t vary during the evolution, both are secure before and after the evolution.<\/jats:p>","DOI":"10.1145\/3719004","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T15:53:26Z","timestamp":1740671606000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Characterizing Smart Contract Evolution"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8234-3186","authenticated-orcid":false,"given":"Xiangping","family":"Chen","sequence":"first","affiliation":[{"name":"Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion, School of Journalism and Communication, Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6793-4324","authenticated-orcid":false,"given":"Ziang","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5702-1910","authenticated-orcid":false,"given":"Peiyong","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1691-0959","authenticated-orcid":false,"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1431-4073","authenticated-orcid":false,"given":"Changlin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, China"}]}],"member":"320","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"GASOL: Gas Analysis and Optimization for Ethereum Smart Contracts. 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