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首页> 外文期刊>International Journal of Emerging Technologies in Learning (iJET) >The Knowledge Repository Management System Architecture of Digital Knowledge Engineering using Machine Learning to Promote Software Engineering Competencies
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The Knowledge Repository Management System Architecture of Digital Knowledge Engineering using Machine Learning to Promote Software Engineering Competencies

机译：利用机器学习提升软件工程能力的数字知识工程知识库管理系统架构

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The knowledge repository management system architecture of digital knowledge engineering using machine learning (KRMS-SWE) to promote software engineering competencies is comprised of four parts, as follows: 1) device service, 2) application service, 3) module service of the KRMS-SWE and 4) machine learning service and storage unit. The knowledge creation, storage, testing and assessing of students’ knowledge in software engineering is carried out using a knowledge verification process with machine learning and divided into six steps, as follows: pre-processing, filtration, stemming, indexing, data mining and interpretation and evaluation. The overall result regarding the suitability of the KRMS-SWE is assessed by five experts who have high levels of experience in related fields. The findings reveal that this research approach can be applied to the future development of the KRMS-SWE.
机译：利用机器学习（KRMS-SWE）来提升软件工程能力的数字知识工程的知识库管理系统体系结构包括四个部分，分别是：1）设备服务，2）应用程序服务，3）KRMS-的模块服务。 SWE和4）机器学习服务和存储单元。软件工程中学生知识的创造，存储，测试和评估是通过机器学习的知识验证过程来进行的，分为六个步骤，如下所示：预处理，过滤，提取，索引，数据挖掘和解释和评估。有关KRMS-SWE适用性的总体结果由五位在相关领域具有丰富经验的专家评估。研究结果表明，该研究方法可以应用于KRMS-SWE的未来发展。

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