{"title":"DV8:自动化架构分析工具套件","authors":"Yuanfang Cai, R. Kazman","doi":"10.1109/TechDebt.2019.00015","DOIUrl":null,"url":null,"abstract":"This paper present our tool suite called DV8. The objective of DV8 is to measure software modularity, detect architecture anti-patterns as technical debts, quantify the maintenance cost of each instance of an anti-pattern, and enable return on investment analyses of architectural debts. Different from other tools, DV8 integrates data from both source code and revision history. We now elaborate on each of DV8's capabilities","PeriodicalId":197657,"journal":{"name":"2019 IEEE/ACM International Conference on Technical Debt (TechDebt)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"DV8: Automated Architecture Analysis Tool Suites\",\"authors\":\"Yuanfang Cai, R. Kazman\",\"doi\":\"10.1109/TechDebt.2019.00015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper present our tool suite called DV8. The objective of DV8 is to measure software modularity, detect architecture anti-patterns as technical debts, quantify the maintenance cost of each instance of an anti-pattern, and enable return on investment analyses of architectural debts. Different from other tools, DV8 integrates data from both source code and revision history. We now elaborate on each of DV8's capabilities\",\"PeriodicalId\":197657,\"journal\":{\"name\":\"2019 IEEE/ACM International Conference on Technical Debt (TechDebt)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE/ACM International Conference on Technical Debt (TechDebt)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TechDebt.2019.00015\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM International Conference on Technical Debt (TechDebt)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TechDebt.2019.00015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper present our tool suite called DV8. The objective of DV8 is to measure software modularity, detect architecture anti-patterns as technical debts, quantify the maintenance cost of each instance of an anti-pattern, and enable return on investment analyses of architectural debts. Different from other tools, DV8 integrates data from both source code and revision history. We now elaborate on each of DV8's capabilities
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