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首页> 外文会议>Information Technology and Applications, 2005. ICITA 2005. Third International Conference on >Applying machine learning using case-based reasoning (CBR) and rule-based reasoning (RBR) approaches to object-oriented application framework documentation
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Applying machine learning using case-based reasoning (CBR) and rule-based reasoning (RBR) approaches to object-oriented application framework documentation

机译：使用基于案例的推理（CBR）和基于规则的推理（RBR）方法将机器学习应用于面向对象的应用程序框架文档

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摘要

Several challenges and problems of developing, using and maintaining object-oriented application frameworks have been identified. It was discovered that companies attempting to build or use large-scale reusable framework often fail unless they recognize and resolve challenges such as development effort, learning curve, integratability, maintainability, validation, defect removal, efficiency, and lack of standards. Framework documentation plays a major role in facing the above challenges. It directly affects the learning curve, maintainability, and defect removal aspects of the application frameworks. We have studied various documenting approaches and concluded that the current approaches are not very effective in overcoming the above challenges, especially on the efficiency problem. So, in this paper, we are going to apply machine learning using case-based reasoning (CBR) and rule-based reasoning (RBR) to framework documentation. We come up with a documentation architecture that combines both techniques in order to come up with improved framework documentation.
机译：已经确定了开发，使用和维护面向对象的应用程序框架的若干挑战和问题。已经发现，尝试建立或使用大规模可重用框架的公司通常会失败，除非它们认识并解决了诸如开发工作，学习曲线，可集成性，可维护性，验证，缺陷消除，效率和缺乏标准之类的挑战。框架文档在面对上述挑战中扮演着重要角色。它直接影响应用程序框架的学习曲线，可维护性和缺陷消除方面。我们研究了各种记录方法，并得出结论认为，当前的方法在克服上述挑战方面不是很有效，特别是在效率问题上。因此，在本文中，我们将使用基于案例的推理（CBR）和基于规则的推理（RBR）的机器学习应用于框架文档。我们提出了一种将两种技术结合在一起的文档体系结构，以提供改进的框架文档。

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