{"title":"通过源代码内的自动检测消除异常代码","authors":"M. Stange","doi":"10.1109/SECON.2004.1287900","DOIUrl":null,"url":null,"abstract":"The ACE approach combines prior research techniques with new ones to eliminate anomalous code from source code. The idea is as follows: (1) identify characteristics/patterns of anomalous code, identify proper syntax, and identify rules of safe programming practices (2) encode the above items as evaluation properties, and (3) verify whether the evaluation passed or failed. This process has been automated into a pushdown automation tool that uses relational databases, process algorithms, static analysis and dynamic analysis.","PeriodicalId":324953,"journal":{"name":"IEEE SoutheastCon, 2004. Proceedings.","volume":"61 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"ACE: Anomalous Code Elimination Through Automatic Detection Within Source Code\",\"authors\":\"M. Stange\",\"doi\":\"10.1109/SECON.2004.1287900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ACE approach combines prior research techniques with new ones to eliminate anomalous code from source code. The idea is as follows: (1) identify characteristics/patterns of anomalous code, identify proper syntax, and identify rules of safe programming practices (2) encode the above items as evaluation properties, and (3) verify whether the evaluation passed or failed. This process has been automated into a pushdown automation tool that uses relational databases, process algorithms, static analysis and dynamic analysis.\",\"PeriodicalId\":324953,\"journal\":{\"name\":\"IEEE SoutheastCon, 2004. Proceedings.\",\"volume\":\"61 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE SoutheastCon, 2004. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SECON.2004.1287900\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE SoutheastCon, 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.2004.1287900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ACE: Anomalous Code Elimination Through Automatic Detection Within Source Code
The ACE approach combines prior research techniques with new ones to eliminate anomalous code from source code. The idea is as follows: (1) identify characteristics/patterns of anomalous code, identify proper syntax, and identify rules of safe programming practices (2) encode the above items as evaluation properties, and (3) verify whether the evaluation passed or failed. This process has been automated into a pushdown automation tool that uses relational databases, process algorithms, static analysis and dynamic analysis.