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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:一种基于上下文的信息检索技术,用于恢复嵌入式软件系统中的用例到源代码的跟踪链接

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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:需求后跟踪能力是通过建立跟踪链接将需求(例如用例)转发到相应的设计文档,源代码和测试用例的能力。作为测试,验证,确认和认证的关键活动,该功能在嵌入式系统开发中变得越来越重要。但是,半自动或全自动生成精确的跟踪链接仍然是一个开放的研究挑战,特别是对于旧系统而言。向量空间模型(VSM)是一种众所周知的信息检索(IR)技术,旨在解决这种情况。但是,VSM在实践中的低精度级别是一个限制。本文的贡献是使用新型上下文分析改进了基于VSM的需求后跟踪能力恢复方法。具体而言,该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的源代码文件。我们的方法是通过在工业自动化,汽车和移动领域中使用三种不同的嵌入式应用程序进行评估的。评估显示,在处理嵌入式软件应用程序时,我们的新方法在三个主要IR指标(即召回率,精度和平均平均精度)方面具有更高的值,因此比VSM可以实现更高的准确性。

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