A. Felfernig, Stefan Reiterer, Martin Stettinger, J. Tiihonen
{"title":"配置知识演化的智能技术","authors":"A. Felfernig, Stefan Reiterer, Martin Stettinger, J. Tiihonen","doi":"10.1145/2701319.2701320","DOIUrl":null,"url":null,"abstract":"Automated testing and debugging of knowledge bases (such as configuration knowledge bases and feature models) is an important contribution to manage knowledge evolution efficiently. However, existing approaches rely on the assumption of consistent test suites which are always kept up-to-date within the scope of different knowledge base maintenance cycles. In this paper we introduce diagnosis techniques that actively guide stakeholders (knowledge engineers and domain experts) in the process of testing and debugging knowledge bases. These techniques take into account faulty test cases and constraints and recommend diagnoses which are the source of a given inconsistency.","PeriodicalId":232045,"journal":{"name":"Proceedings of the 9th International Workshop on Variability Modelling of Software-Intensive Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Intelligent Techniques for Configuration Knowledge Evolution\",\"authors\":\"A. Felfernig, Stefan Reiterer, Martin Stettinger, J. Tiihonen\",\"doi\":\"10.1145/2701319.2701320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated testing and debugging of knowledge bases (such as configuration knowledge bases and feature models) is an important contribution to manage knowledge evolution efficiently. However, existing approaches rely on the assumption of consistent test suites which are always kept up-to-date within the scope of different knowledge base maintenance cycles. In this paper we introduce diagnosis techniques that actively guide stakeholders (knowledge engineers and domain experts) in the process of testing and debugging knowledge bases. These techniques take into account faulty test cases and constraints and recommend diagnoses which are the source of a given inconsistency.\",\"PeriodicalId\":232045,\"journal\":{\"name\":\"Proceedings of the 9th International Workshop on Variability Modelling of Software-Intensive Systems\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-01-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th International Workshop on Variability Modelling of Software-Intensive Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2701319.2701320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th International Workshop on Variability Modelling of Software-Intensive Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2701319.2701320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent Techniques for Configuration Knowledge Evolution
Automated testing and debugging of knowledge bases (such as configuration knowledge bases and feature models) is an important contribution to manage knowledge evolution efficiently. However, existing approaches rely on the assumption of consistent test suites which are always kept up-to-date within the scope of different knowledge base maintenance cycles. In this paper we introduce diagnosis techniques that actively guide stakeholders (knowledge engineers and domain experts) in the process of testing and debugging knowledge bases. These techniques take into account faulty test cases and constraints and recommend diagnoses which are the source of a given inconsistency.