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Systematic Knowledge Engineering: Building Bodies of Knowledge from Published Research

机译:系统知识工程:从已发表的研究中建立知识体系

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Context. Software engineering researchers conduct systematic literature reviews (SLRs) to build bodies of knowledge (BoKs). Unfortunately, relevant knowledge collected in the SLR process is not publicly available, which considerably slows down building BoKs incrementally. Objective. We present and evaluate the Systematic Knowledge Engineering (SKE) process to support efficiently building BoKs from published research. Method. SKE is based on the SLR process and on Knowledge Engineering practices to build a Knowledge Base (KB) by reusing intermediate data extraction results from SLRs. We evaluated the feasibility of applying SKE by building a Software Inspection BoK KB from published experiments and a Software Product Line BoK KB from published experience reports. We compared the effort, benefits, and risks of building BoK KBs regarding the SKE and the traditional SLR processes. Results. The application of SKE for incrementally collecting and organizing knowledge in the context of a BoK was feasible for different domains and different types of evidence. While the efforts for conducting the SKE and traditional SLR processes are comparable, SKE provides significant benefits for building BoKs. Conclusions. SKE enables researchers in a scientific community to reuse and incrementally build knowledge in a BoK. SKE is ready to be evaluated in other software engineering domains.
机译:上下文。软件工程研究人员进行系统的文献综述(SLR),以建立知识体系(BoK)。不幸的是,在SLR流程中收集的相关知识无法公开获得,这大大减慢了BoK的构建速度。目的。我们提出并评估系统知识工程(SKE)流程,以支持通过已发表的研究有效地构建BoK。方法。 SKE基于SLR流程和知识工程实践,通过重用SLR的中间数据提取结果来构建知识库(KB)。我们通过从已发布的实验中构建软件检查BoK KB,并从已发布的经验报告中构建软件产品线BoK KB,来评估应用SKE的可行性。我们比较了有关SKE和传统SLR流程构建BoK KB的工作,收益和风险。结果。将SKE用于在BoK的背景下逐步收集和组织知识的应用对于不同领域和不同类型的证据都是可行的。尽管执行SKE和传统SLR流程的工作是可比的,但SKE为构建BoK提供了显着的好处。结论。 SKE使科学界的研究人员可以在BoK中重复使用并逐步积累知识。 SKE准备在其他软件工程领域进行评估。

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