微量矩阵分析仪(TMA)

Wenbin Li, J. Hayes, Fan Yang, Ken Imai, Jesse Yannelli, C. Carnes, M. Doyle
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引用次数: 3

摘要

跟踪矩阵(TM)表示软件工程工件之间的关系,并且是许多软件保证技术(如关键性分析)的基础。在一个大型项目中,一个TM可能表示成千上万个元素或几十个工件之间的关系(例如,在设计元素和代码元素之间,在需求和测试用例之间)。在任务和安全关键型系统中,第三方代理可能被赋予评估开发人员准备的TM的工作。由于任务的规模和复杂性,需要自动化技术。我们已经开发了一种分析TM的技术,称为痕量矩阵分析仪(TMA),因此第三方代理可以更快更有效地执行他们的工作。为了验证,我们将TMA应用于两个具有已知问题和黄金答案集的TMs: MoonLander和MODIS。我们还要求有经验的软件工程师手动检查TM。我们发现TMA正确地识别了TM问题,并且比人工审查快得多,但也错误地识别了一个数据集的问题。这项工作涉及可信大挑战,研究项目3、5和6。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trace Matrix Analyzer (TMA)
A Trace Matrix (TM) represents the relationship between software engineering artifacts and is foundational for many software assurance techniques such as criticality analysis. In a large project, a TM might represent the relationships between thousands of elements of dozens of artifacts (for example, between design elements and code elements, between requirements and test cases). In mission- and safety-critical systems, a third party agent may be given the job to assess a TM prepared by the developer. Due to the size and complexity of the task, automated techniques are needed. We have developed a technique for analyzing a TM, called Trace Matrix Analyzer (TMA), so that third party agents can perform their work faster and more effectively. To validate, we applied TMA to two TMs with known problems and golden answersets: MoonLander and MODIS. We also asked an experienced software engineer to manually review the TM. We found that TMA properly identified TM issues and was much faster than manual review, but also falsely identified issues for one dataset. This work addresses the Trusted Grand Challenge, research projects 3, 5, and 6.
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