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A Context-based Information Retrieval Technique for Recovering Use-Case-to-Source-Code Trace Links in Embedded Software Systems

机译:基于上下文的信息检索技术,用于在嵌入式软件系统中恢复使用的使用情况到源代码码链路

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Post-requirements trace ability is the ability to relate requirements (e.g., use cases) forward to corresponding design documents, source code and test cases by establishing trace links. This ability is becoming ever more crucial within embedded systems development, as a critical activity of testing, verification, validation and certification. However, semi-automatically or fully-automatically generating accurate trace links remains an open research challenge, especially for legacy systems. Vector Space Model (VSM), a notably known Information Retrieval (IR) technique aims to remedy this situation. However, VSM's low-accuracy level in practice is a limitation. The contribution of this paper is an improved VSM-based post-requirements trace ability recovery approach using a novel context analysis. Specifically, the analysis method can better utilize context information extracted from use cases to discover relevant source code files. Our approach is evaluated by using three different embedded applications in the domains of industrial automation, automotive and mobile. The evaluation shows that our new approach can achieve better accuracy than VSM, in terms of higher values of three main IR metrics, i.e., recall, precision, and mean average precision, when it handles embedded software applications.
机译:后需求跟踪能力是通过建立跟踪链接来对应于相应的设计文档,源代码和测试用例的要求(例如,使用情况)的能力。这种能力在嵌入式系统开发中变得更加重要,作为测试,验证,验证和认证的关键活动。但是,半自动或完全生成准确的跟踪链接仍然是开放的研究挑战,特别是对于遗留系统。传染媒介空间模型(VSM),一个明显的已知信息检索(IR)技术旨在解决这种情况。然而,VSM在实践中的低精度水平是一个限制。本文的贡献是一种改进的基于VSM的后需求跟踪能力恢复方法,使用新颖的上下文分析。具体地,分析方法可以更好地利用从用例中提取的上下文信息来发现相关源代码文件。我们的方法是通过在工业自动化,汽车和移动域中使用三种不同的嵌入式应用来评估。评估表明,根据三个主要IR度量的值,即回忆,精度,平均精度,我们的新方法可以达到比VSM更好的准确性,即召回,精度,平均精度,当它处理嵌入式软件应用程序时。

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