S. Krieter, R. Schröter, Thomas Thüm, W. Fenske, G. Saake
{"title":"高效特征模型切片算法的比较","authors":"S. Krieter, R. Schröter, Thomas Thüm, W. Fenske, G. Saake","doi":"10.1145/2934466.2934477","DOIUrl":null,"url":null,"abstract":"Feature models are a well-known concept to represent variability in software product lines by defining features and their dependencies. During feature-model evolution, for information hiding, and for feature-model analyses, it is often necessary to remove certain features from a model. As the crude deletion of features can have undesirable effects on their dependencies, dependency-preserving algorithms, known as feature-model slicing, have been proposed. However, current algorithms do not perform well when removing a high number of features from large feature models. Therefore, we propose an efficient algorithm for feature-model slicing based on logical resolution and the minimization of logical formulas. We empirically evaluate the scalability of our algorithm on a number of feature models and find that our algorithm generally outperforms existing algorithms.","PeriodicalId":128559,"journal":{"name":"Proceedings of the 20th International Systems and Software Product Line Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Comparing algorithms for efficient feature-model slicing\",\"authors\":\"S. Krieter, R. Schröter, Thomas Thüm, W. Fenske, G. Saake\",\"doi\":\"10.1145/2934466.2934477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Feature models are a well-known concept to represent variability in software product lines by defining features and their dependencies. During feature-model evolution, for information hiding, and for feature-model analyses, it is often necessary to remove certain features from a model. As the crude deletion of features can have undesirable effects on their dependencies, dependency-preserving algorithms, known as feature-model slicing, have been proposed. However, current algorithms do not perform well when removing a high number of features from large feature models. Therefore, we propose an efficient algorithm for feature-model slicing based on logical resolution and the minimization of logical formulas. We empirically evaluate the scalability of our algorithm on a number of feature models and find that our algorithm generally outperforms existing algorithms.\",\"PeriodicalId\":128559,\"journal\":{\"name\":\"Proceedings of the 20th International Systems and Software Product Line Conference\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 20th International Systems and Software Product Line Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2934466.2934477\",\"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 20th International Systems and Software Product Line Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2934466.2934477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparing algorithms for efficient feature-model slicing
Feature models are a well-known concept to represent variability in software product lines by defining features and their dependencies. During feature-model evolution, for information hiding, and for feature-model analyses, it is often necessary to remove certain features from a model. As the crude deletion of features can have undesirable effects on their dependencies, dependency-preserving algorithms, known as feature-model slicing, have been proposed. However, current algorithms do not perform well when removing a high number of features from large feature models. Therefore, we propose an efficient algorithm for feature-model slicing based on logical resolution and the minimization of logical formulas. We empirically evaluate the scalability of our algorithm on a number of feature models and find that our algorithm generally outperforms existing algorithms.