{"title":"Evolution Style Mining in Software Architecture","authors":"Kadidiatou Djibo, M. Oussalah, J. Konaté","doi":"10.5220/0009349203130322","DOIUrl":null,"url":null,"abstract":": Sequential pattern extraction techniques are applied to the evolution styles of an evolving software architecture in order to plan and predict future evolution paths for the architecture. We present in this paper, a formalism to express the evolution styles in a more practical way. Then, we analyze these collected styles from the formalism introduced by the techniques of sequential patterns extraction to discover the sequential patterns of software architecture evolution. Finaly, from the analysis results, we develop a learning base and prediction rules to predict future evolution paths.","PeriodicalId":420861,"journal":{"name":"International Conference on Evaluation of Novel Approaches to Software Engineering","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Evaluation of Novel Approaches to Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0009349203130322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
: Sequential pattern extraction techniques are applied to the evolution styles of an evolving software architecture in order to plan and predict future evolution paths for the architecture. We present in this paper, a formalism to express the evolution styles in a more practical way. Then, we analyze these collected styles from the formalism introduced by the techniques of sequential patterns extraction to discover the sequential patterns of software architecture evolution. Finaly, from the analysis results, we develop a learning base and prediction rules to predict future evolution paths.