{"title":"基于评估图匹配模式的实用技术债务发现","authors":"Andriy Shapochka, B. Omelayenko","doi":"10.1109/MTD.2016.7","DOIUrl":null,"url":null,"abstract":"This article reflects on experiences collected by doing technical debt assessments for many years as a primary job. It argues for a model that represents software source code and other informational artifacts as a graph with metadata describing these artifacts. Technical debt items are discovered with graph matching patterns that represent technical debt discovery patterns. These patterns automate manual work, avoid effort duplication, and boost assessment performance. The overall approach is illustrated with a prototype implementation and a case study.","PeriodicalId":371173,"journal":{"name":"2016 IEEE 8th International Workshop on Managing Technical Debt (MTD)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Practical Technical Debt Discovery by Matching Patterns in Assessment Graph\",\"authors\":\"Andriy Shapochka, B. Omelayenko\",\"doi\":\"10.1109/MTD.2016.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article reflects on experiences collected by doing technical debt assessments for many years as a primary job. It argues for a model that represents software source code and other informational artifacts as a graph with metadata describing these artifacts. Technical debt items are discovered with graph matching patterns that represent technical debt discovery patterns. These patterns automate manual work, avoid effort duplication, and boost assessment performance. The overall approach is illustrated with a prototype implementation and a case study.\",\"PeriodicalId\":371173,\"journal\":{\"name\":\"2016 IEEE 8th International Workshop on Managing Technical Debt (MTD)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 8th International Workshop on Managing Technical Debt (MTD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MTD.2016.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 8th International Workshop on Managing Technical Debt (MTD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MTD.2016.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Practical Technical Debt Discovery by Matching Patterns in Assessment Graph
This article reflects on experiences collected by doing technical debt assessments for many years as a primary job. It argues for a model that represents software source code and other informational artifacts as a graph with metadata describing these artifacts. Technical debt items are discovered with graph matching patterns that represent technical debt discovery patterns. These patterns automate manual work, avoid effort duplication, and boost assessment performance. The overall approach is illustrated with a prototype implementation and a case study.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
