{"title":"过度依赖人工智能代码助手对科学软件的威胁。","authors":"Gabrielle O’Brien","doi":"10.1038/s43588-025-00845-2","DOIUrl":null,"url":null,"abstract":"The adoption of generative artificial intelligence (AI) code assistants in scientific software development is promising, but user studies across an array of programming contexts suggest that programmers are at risk of over-reliance on these tools, leading them to accept undetected errors in generated code. Scientific software may be particularly vulnerable to such errors because most research code is untested and scientists are undertrained in software development skills. This Comment outlines the factors that place scientific code at risk and suggests directions for research groups, educators, publishers and funders to counter these liabilities.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 9","pages":"701-703"},"PeriodicalIF":18.3000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Threats to scientific software from over-reliance on AI code assistants\",\"authors\":\"Gabrielle O’Brien\",\"doi\":\"10.1038/s43588-025-00845-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The adoption of generative artificial intelligence (AI) code assistants in scientific software development is promising, but user studies across an array of programming contexts suggest that programmers are at risk of over-reliance on these tools, leading them to accept undetected errors in generated code. Scientific software may be particularly vulnerable to such errors because most research code is untested and scientists are undertrained in software development skills. This Comment outlines the factors that place scientific code at risk and suggests directions for research groups, educators, publishers and funders to counter these liabilities.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"5 9\",\"pages\":\"701-703\"},\"PeriodicalIF\":18.3000,\"publicationDate\":\"2025-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-025-00845-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-025-00845-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Threats to scientific software from over-reliance on AI code assistants
The adoption of generative artificial intelligence (AI) code assistants in scientific software development is promising, but user studies across an array of programming contexts suggest that programmers are at risk of over-reliance on these tools, leading them to accept undetected errors in generated code. Scientific software may be particularly vulnerable to such errors because most research code is untested and scientists are undertrained in software development skills. This Comment outlines the factors that place scientific code at risk and suggests directions for research groups, educators, publishers and funders to counter these liabilities.
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