{"title":"Titan: a toolset that connects software architecture with quality analysis","authors":"Lu Xiao, Yuanfang Cai, R. Kazman","doi":"10.1145/2635868.2661677","DOIUrl":null,"url":null,"abstract":"In this tool demo, we will illustrate our tool---Titan---that supports a new architecture model: design rule spaces (DRSpaces). We will show how Titan can capture both architecture and evolutionary structure and help to bridge the gap between architecture and defect prediction. We will demo how to use our toolset to capture hundreds of buggy files into just a few architecturally related groups, and to reveal architecture issues that contribute to the error-proneness and change-proneness of these groups. Our tool has been used to analyze dozens of large-scale industrial projects, and has demonstrated its ability to provide valuable direction on which parts of the architecture are problematic, and on why, when, and how to refactor. The video demo of Titan can be found at https://art.cs.drexel.edu/~lx52/titan.mp4","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"55","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2635868.2661677","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 55
Abstract
In this tool demo, we will illustrate our tool---Titan---that supports a new architecture model: design rule spaces (DRSpaces). We will show how Titan can capture both architecture and evolutionary structure and help to bridge the gap between architecture and defect prediction. We will demo how to use our toolset to capture hundreds of buggy files into just a few architecturally related groups, and to reveal architecture issues that contribute to the error-proneness and change-proneness of these groups. Our tool has been used to analyze dozens of large-scale industrial projects, and has demonstrated its ability to provide valuable direction on which parts of the architecture are problematic, and on why, when, and how to refactor. The video demo of Titan can be found at https://art.cs.drexel.edu/~lx52/titan.mp4