Using Automated Use Case Generation for Early Design Stage Functional Failure and Human Error Analysis

Lukman Irshad, H. Demirel, I. Tumer
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引用次数: 1

Abstract

Human errors and poor ergonomics are attributed to a majority of large-scale accidents and malfunctions in complex engineered systems. Human Error and Functional Failure Reasoning (HEFFR) is a framework developed to assess potential functional failures, human errors, and their propagation paths during early design stages so that more reliable systems with improved performance and safety can be designed. In order to perform a comprehensive analysis using this framework, a wide array of potential failure scenarios need to be tested. Coming up with such use cases that can cover a majority of faults can be challenging or even impossible for a single engineer or a team of engineers. In the field of software engineering, automated test case generation techniques have been widely used for software testing. This research explores these methods to create a use case generation technique that covers both component-related and human-related fault scenarios. The proposed technique is a time based simulation that employs a modified Depth First Search (DFS) algorithm to simulate events as the event propagation is analyzed using HEFFR at each timestep. This approach is applied to a hold-up tank design problem and the results are analyzed to explore the capabilities and limitations.
在早期设计阶段使用自动化用例生成功能故障和人为错误分析
在复杂的工程系统中,人为错误和不良的人体工程学是造成大多数大规模事故和故障的原因。人为错误和功能故障推理(HEFFR)是一个框架,用于在早期设计阶段评估潜在的功能故障、人为错误及其传播路径,以便设计出性能和安全性更高的更可靠的系统。为了使用该框架执行全面的分析,需要测试一系列潜在的故障场景。对于单个工程师或工程师团队来说,提出能够覆盖大多数错误的用例是具有挑战性的,甚至是不可能的。在软件工程领域,自动化测试用例生成技术已被广泛应用于软件测试。本研究探索了这些方法,以创建涵盖组件相关和人类相关故障场景的用例生成技术。所提出的技术是一种基于时间的模拟,它采用改进的深度优先搜索(DFS)算法来模拟事件,因为在每个时间步长使用HEFFR分析事件传播。将该方法应用于储罐设计问题,并对结果进行了分析,以探讨其能力和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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