{"title":"自动生成单元测试数据以达到MC/DC标准","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":"{\"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}","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}
Automatic Test Data Generation for Unit Testing to Achieve MC/DC Criterion
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.