基于全知识决策的自动化需求获取方法

Lin Liu, Lei Lin
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引用次数: 8

摘要

需求提取作为软件生命周期的第一阶段,其重要性越来越突出。如何快速准确地引出用户需求,是人们长期以来想要解决的问题。本文提出了一种基于用户需求的领域知识自动抽取系统。通过对原始需求描述的逐步完善和细化,我们可以有效地获取需求,最终提高需求的质量和激发过程的效率。自动化需求提取的基于决策的方法,ARED-CK,假设有一个相当完整的领域知识库,在此基础上,用于提取需求的查询周期被制定为决策问题。需求决策过程通过与用户的持续交互,利用领域知识库中的需求模型与需求模型的具体实例相匹配,最终识别出适合目标用户特定用途的需求模型。在此基础上,提出了优化决策过程,从而在最少的用户交互步骤内识别出最优的精确需求模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ARED-CK: an Automated Requirements Elicitation Approach Based on Decision-making with Complete Knowledge
Requirements elicitation, as the first phase of the software life cycle, its importance is becoming more and more prominent. How fast and accurate user requirements can be elicited, is a problem that people wants to solve for a long time. This paper proposes to establish an automated elicitation system based on domain knowledge on user requirements. By gradually perfecting and refining the original requirements description, we can acquire requirements effectively and ultimately improve the quality of requirements and the efficiency of the elicitation process. The decision-making based approach for automated requirements elicitation, ARED-CK, assumes that a fairly complete domain knowledge base is in place, based on which inquiry cycles for eliciting requirements is formulated into a decision-making problem. Through continuous interactions with the user, the requirements decision-making process use requirements models in the domain knowledge base matching with specific instances of requirements model, and ultimately identify a requirements model fit the specific purpose of the target user. Moreover, the paper put forward the proposed decision process for optimization, so that within least steps of user interaction an optimal precise requirements model can be identified.
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