{"title":"故障定位:基于先验信息的覆盖阵列分析","authors":"R. Lekivetz, Joseph Morgan","doi":"10.1109/QRS-C.2018.00033","DOIUrl":null,"url":null,"abstract":"Covering arrays are being increasingly used as a tool to determine test cases for testing complex engineered systems. The primary appeal of using covering arrays for this purpose is that they are an efficient way to construct test cases that are effective at precipitating failures that are due to the combination of several inputs. However, when failures occur, determining the inputs that triggered the failures is usually a time consuming task. In this article we present a method that allows a test engineer to efficiently analyze the outcomes of a set of test cases by making use of prior knowledge about the system under test. This analysis provides a ranking of input combinations that potentially triggered the failures.","PeriodicalId":199384,"journal":{"name":"2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fault Localization: Analyzing Covering Arrays Given Prior Information\",\"authors\":\"R. Lekivetz, Joseph Morgan\",\"doi\":\"10.1109/QRS-C.2018.00033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Covering arrays are being increasingly used as a tool to determine test cases for testing complex engineered systems. The primary appeal of using covering arrays for this purpose is that they are an efficient way to construct test cases that are effective at precipitating failures that are due to the combination of several inputs. However, when failures occur, determining the inputs that triggered the failures is usually a time consuming task. In this article we present a method that allows a test engineer to efficiently analyze the outcomes of a set of test cases by making use of prior knowledge about the system under test. This analysis provides a ranking of input combinations that potentially triggered the failures.\",\"PeriodicalId\":199384,\"journal\":{\"name\":\"2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)\",\"volume\":\"30 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 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/QRS-C.2018.00033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QRS-C.2018.00033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Localization: Analyzing Covering Arrays Given Prior Information
Covering arrays are being increasingly used as a tool to determine test cases for testing complex engineered systems. The primary appeal of using covering arrays for this purpose is that they are an efficient way to construct test cases that are effective at precipitating failures that are due to the combination of several inputs. However, when failures occur, determining the inputs that triggered the failures is usually a time consuming task. In this article we present a method that allows a test engineer to efficiently analyze the outcomes of a set of test cases by making use of prior knowledge about the system under test. This analysis provides a ranking of input combinations that potentially triggered the failures.