{"title":"t路诱导故障组合的有效和保证检测","authors":"Paolo Arcaini, A. Gargantini, M. Radavelli","doi":"10.1109/ICSTW.2019.00054","DOIUrl":null,"url":null,"abstract":"Combinatorial testing is a widely applied black-box testing technique, which is used to detect failures caused by parameter interactions (we call them failure-inducing combinations). Traditional combinatorial testing techniques provide fault detection, but most of them have weak fault diagnosis. In this paper, we propose a new fault characterization method called MixTgTe to locate all the failure-inducing combinations in a system under test, up to an interaction size decided by the user. Our method is based on adaptive black-box testing, in which test cases are generated based on outcomes of previous tests. We show that our method performs better than existing strategies that explore all the faults first, and then obtain the failure-inducing combination(s) for each failure.","PeriodicalId":310230,"journal":{"name":"2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Efficient and Guaranteed Detection of t-Way Failure-Inducing Combinations\",\"authors\":\"Paolo Arcaini, A. Gargantini, M. Radavelli\",\"doi\":\"10.1109/ICSTW.2019.00054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Combinatorial testing is a widely applied black-box testing technique, which is used to detect failures caused by parameter interactions (we call them failure-inducing combinations). Traditional combinatorial testing techniques provide fault detection, but most of them have weak fault diagnosis. In this paper, we propose a new fault characterization method called MixTgTe to locate all the failure-inducing combinations in a system under test, up to an interaction size decided by the user. Our method is based on adaptive black-box testing, in which test cases are generated based on outcomes of previous tests. We show that our method performs better than existing strategies that explore all the faults first, and then obtain the failure-inducing combination(s) for each failure.\",\"PeriodicalId\":310230,\"journal\":{\"name\":\"2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSTW.2019.00054\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSTW.2019.00054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient and Guaranteed Detection of t-Way Failure-Inducing Combinations
Combinatorial testing is a widely applied black-box testing technique, which is used to detect failures caused by parameter interactions (we call them failure-inducing combinations). Traditional combinatorial testing techniques provide fault detection, but most of them have weak fault diagnosis. In this paper, we propose a new fault characterization method called MixTgTe to locate all the failure-inducing combinations in a system under test, up to an interaction size decided by the user. Our method is based on adaptive black-box testing, in which test cases are generated based on outcomes of previous tests. We show that our method performs better than existing strategies that explore all the faults first, and then obtain the failure-inducing combination(s) for each failure.