面向特征感知的精化轨迹检索

P. Rempel, Patrick Mäder, Tobias Kuschke
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引用次数: 11

摘要

需求可追溯性支持从业者达到更高的项目成熟度和更好的产品质量。为了获得这种支持,需要在软件开发过程的各种工件之间进行跟踪。根据现有工件的数量,建立跟踪可能是一项耗时且容易出错的任务。另外,手工创建跟踪经常会中断软件开发过程。为了克服这些问题,从业者正在寻求支持创建轨迹的技术(参见大挑战:无处不在(GC-U))。在本文中,我们提出使用图聚类算法来支持改进轨迹的检索。细化跟踪是在开发项目的不同阶段创建的工件之间存在的跟踪,例如,在特性和用例之间。我们在几个TraceLab实验中评估了我们的方法的有效性。这些实验采用了包含不同类型的细化轨迹的三个标准数据集。结果表明,图聚类可以改善改进轨迹的检索,是实现泛在可追溯性总体目标的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards feature-aware retrieval of refinement traces
Requirements traceability supports practitioners in reaching higher project maturity and better product quality. To gain this support, traces between various artifacts of the software development process are required. Depending on the number of existing artifacts, establishing traces can be a time-consuming and error-prone task. Additionally, the manual creation of traces frequently interrupts the software development process. In order to overcome those problems, practitioners are asking for techniques that support the creation of traces (see Grand Challenge: Ubiquitous (GC-U)). In this paper, we propose the usage of a graph clustering algorithm to support the retrieval of refinement traces. Refinement traces are traces that exist between artifacts created in different phases of a development project, e.g., between features and use cases. We assessed the effectiveness of our approach in several TraceLab experiments. These experiments employ three standard datasets containing differing types of refinement traces. Results show that graph clustering can improve the retrieval of refinement traces and is a step towards the overall goal of ubiquitous traceability.
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