{"title":"The Influence Ranking for Software Classes","authors":"Hui Li, Guofeng Gao, X. Ge, Shikai Guo, Liying Hao","doi":"10.1109/FSKD.2018.8687115","DOIUrl":null,"url":null,"abstract":"As the growth of software system scale and complexity, the risk of system collapse is rising because of the increasing software defects. Many defect detection and prediction methods are presented to solve this problem, but the effect is still not satisfied so far. Therefore, software network is brought in to analyze the system structure from the overall view of the system, and some methods have achieved satisfying results. In this paper, a weighted software network model is first presented to describe the software systems. Then a method of influence ranking for software classes (IRSC)is proposed to sort the propagation capacity of the underlying defect for all the classes. In the following, software defect implantation experiments are conducted and verified that IRSC method is effective on measuring the influence of the classes for software systems.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2018.8687115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
As the growth of software system scale and complexity, the risk of system collapse is rising because of the increasing software defects. Many defect detection and prediction methods are presented to solve this problem, but the effect is still not satisfied so far. Therefore, software network is brought in to analyze the system structure from the overall view of the system, and some methods have achieved satisfying results. In this paper, a weighted software network model is first presented to describe the software systems. Then a method of influence ranking for software classes (IRSC)is proposed to sort the propagation capacity of the underlying defect for all the classes. In the following, software defect implantation experiments are conducted and verified that IRSC method is effective on measuring the influence of the classes for software systems.