{"title":"应用模糊逻辑减少软件开发过程中的量化误差和上下文偏差问题","authors":"F. Marcelloni, M. Aksit","doi":"10.1109/NAFIPS.1999.781696","DOIUrl":null,"url":null,"abstract":"Object-oriented methods define a considerable number of rules, which are generally expressed using two-valued logic. For example, an entity in a requirement specification is either accepted or rejected as a class. There are two major problems of how rules are defined and applied in current methods. Firstly, two-valued logic cannot effectively express the approximate and inexact nature of a typical software development process. Secondly, the influence of contextual factors on rules is generally not modeled explicitly. We term these problems as quantization error and contextual bias problems, respectively. To reduce these problems, we adopt fuzzy logic-based methodological rules. This approach is method independent and is useful for evaluating and enhancing current methods. In addition, the use of fuzzy logic increases the adaptability and reusability of design models.","PeriodicalId":335957,"journal":{"name":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Reducing quantization error and contextual bias problems in software development processes by applying fuzzy logic\",\"authors\":\"F. Marcelloni, M. Aksit\",\"doi\":\"10.1109/NAFIPS.1999.781696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Object-oriented methods define a considerable number of rules, which are generally expressed using two-valued logic. For example, an entity in a requirement specification is either accepted or rejected as a class. There are two major problems of how rules are defined and applied in current methods. Firstly, two-valued logic cannot effectively express the approximate and inexact nature of a typical software development process. Secondly, the influence of contextual factors on rules is generally not modeled explicitly. We term these problems as quantization error and contextual bias problems, respectively. To reduce these problems, we adopt fuzzy logic-based methodological rules. This approach is method independent and is useful for evaluating and enhancing current methods. In addition, the use of fuzzy logic increases the adaptability and reusability of design models.\",\"PeriodicalId\":335957,\"journal\":{\"name\":\"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.1999.781696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.1999.781696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reducing quantization error and contextual bias problems in software development processes by applying fuzzy logic
Object-oriented methods define a considerable number of rules, which are generally expressed using two-valued logic. For example, an entity in a requirement specification is either accepted or rejected as a class. There are two major problems of how rules are defined and applied in current methods. Firstly, two-valued logic cannot effectively express the approximate and inexact nature of a typical software development process. Secondly, the influence of contextual factors on rules is generally not modeled explicitly. We term these problems as quantization error and contextual bias problems, respectively. To reduce these problems, we adopt fuzzy logic-based methodological rules. This approach is method independent and is useful for evaluating and enhancing current methods. In addition, the use of fuzzy logic increases the adaptability and reusability of design models.