安全关键软件基于需求的自动化测试生成

Meng Li, B. Meng, Han Yu, Kit Siu, Michael Durling, Daniel Russell, Craig McMillan, Matthew Smith, M. Stephens, Scott Thomson
{"title":"安全关键软件基于需求的自动化测试生成","authors":"Meng Li, B. Meng, Han Yu, Kit Siu, Michael Durling, Daniel Russell, Craig McMillan, Matthew Smith, M. Stephens, Scott Thomson","doi":"10.1109/DASC43569.2019.9081726","DOIUrl":null,"url":null,"abstract":"With the growing size and complexity of safety critical software in industrial domains such as aviation, automotive and medical devices, developing tests for such software to achieve corresponding standards such as DO-178C and ISO-26262 has become a challenge. Existing test generation tools are either not generating a complete set of tests to satisfy the standards or requires considerable human interventions. General Electric developed a toolchain called ASSERT™ (Analysis of Semantic Specifications and Efficient generation of Requirements based Tests) to address the challenges and limitations of existing tools by formally capturing requirements and automatically generating a complete set of requirements-based tests to satisfy certain industry standards. This paper describes our approach to automatically generate test objectives, test cases, and test procedures from requirements to satisfy DO-178C. We demonstrate ASSERT™'s requirements-based automated test generation (ATG) tool on an avionics system.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Requirements-based Automated Test Generation for Safety Critical Software\",\"authors\":\"Meng Li, B. Meng, Han Yu, Kit Siu, Michael Durling, Daniel Russell, Craig McMillan, Matthew Smith, M. Stephens, Scott Thomson\",\"doi\":\"10.1109/DASC43569.2019.9081726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the growing size and complexity of safety critical software in industrial domains such as aviation, automotive and medical devices, developing tests for such software to achieve corresponding standards such as DO-178C and ISO-26262 has become a challenge. Existing test generation tools are either not generating a complete set of tests to satisfy the standards or requires considerable human interventions. General Electric developed a toolchain called ASSERT™ (Analysis of Semantic Specifications and Efficient generation of Requirements based Tests) to address the challenges and limitations of existing tools by formally capturing requirements and automatically generating a complete set of requirements-based tests to satisfy certain industry standards. This paper describes our approach to automatically generate test objectives, test cases, and test procedures from requirements to satisfy DO-178C. We demonstrate ASSERT™'s requirements-based automated test generation (ATG) tool on an avionics system.\",\"PeriodicalId\":129864,\"journal\":{\"name\":\"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DASC43569.2019.9081726\",\"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/AIAA 38th Digital Avionics Systems Conference (DASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DASC43569.2019.9081726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

随着航空、汽车和医疗设备等工业领域安全关键软件的规模和复杂性不断增长,为这些软件开发测试以达到相应的标准,如DO-178C和ISO-26262已成为一项挑战。现有的测试生成工具要么不能生成一套完整的测试来满足标准,要么需要大量的人工干预。通用电气开发了一个名为ASSERT™(语义规范分析和基于测试的有效生成需求)的工具链,通过正式捕获需求并自动生成一套完整的基于需求的测试来满足某些行业标准,从而解决现有工具的挑战和局限性。本文描述了我们从满足DO-178C的需求中自动生成测试目标、测试用例和测试过程的方法。我们在航空电子系统上演示了ASSERT™基于需求的自动测试生成(ATG)工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Requirements-based Automated Test Generation for Safety Critical Software
With the growing size and complexity of safety critical software in industrial domains such as aviation, automotive and medical devices, developing tests for such software to achieve corresponding standards such as DO-178C and ISO-26262 has become a challenge. Existing test generation tools are either not generating a complete set of tests to satisfy the standards or requires considerable human interventions. General Electric developed a toolchain called ASSERT™ (Analysis of Semantic Specifications and Efficient generation of Requirements based Tests) to address the challenges and limitations of existing tools by formally capturing requirements and automatically generating a complete set of requirements-based tests to satisfy certain industry standards. This paper describes our approach to automatically generate test objectives, test cases, and test procedures from requirements to satisfy DO-178C. We demonstrate ASSERT™'s requirements-based automated test generation (ATG) tool on an avionics system.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信