{"title":"Automatic Test Data Generation for Unit Testing to Achieve MC/DC Criterion","authors":"Tianyong Wu, Jun Yan, Jian Zhang","doi":"10.1109/SERE.2014.25","DOIUrl":null,"url":null,"abstract":"Modified Condition/Decision Coverage (MC/DC) became widely used in software testing, especially in safety-critical domain. However, existing testing tools often aim at achieving statement or branch coverage and do not support test generation for MC/DC. In this paper, we propose a novel test generation method to find appropriate test data for MC/DC. Specifically, we first extract paths from the target program and then find appropriate test data to trigger these paths. In the path extraction process, we propose a greedy strategy to determine the next selected branch. The evaluation results show that our method can actually generate test data quickly and the coverage increases a lot (up to 37.5%) compared with existing approaches.","PeriodicalId":248957,"journal":{"name":"2014 Eighth International Conference on Software Security and Reliability","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Eighth International Conference on Software Security and Reliability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERE.2014.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Modified Condition/Decision Coverage (MC/DC) became widely used in software testing, especially in safety-critical domain. However, existing testing tools often aim at achieving statement or branch coverage and do not support test generation for MC/DC. In this paper, we propose a novel test generation method to find appropriate test data for MC/DC. Specifically, we first extract paths from the target program and then find appropriate test data to trigger these paths. In the path extraction process, we propose a greedy strategy to determine the next selected branch. The evaluation results show that our method can actually generate test data quickly and the coverage increases a lot (up to 37.5%) compared with existing approaches.