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A situation-centric, knowledge-driven requirements elicitation approach

机译：以情境为中心，知识驱动的需求启发方法

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摘要

Human factors have been increasingly recognized as one of the major driving forces of requirement changes. We believe that the requirements elicitation (RE) process should largely embrace human-centered perspectives, and this work focuses on changing human intentions and desires over time. To support software evolution due to requirement changes, Situ framework has been proposed to model and detect human intentions by inferring their desires through monitoring environmental contexts and human behavioral contexts prior to or after system deployment. Earlier work on Situ reported that the technique is able to infer users' desires with a certain degree of accuracy using the Conditional Random Fields method. However, new intention identification and new requirements elicitation still primarily depends on manual analysis.;This work attempts to find a computable way to identify users' new intentions with limited help from human oracle. We discuss the feasibility of implementing the concept of Data-Information-Knowledge-Wisdom (DIKW) to bridge the gap between requirements and data pertaining to user behaviors and environmental contexts, and propose a situation-centric, knowledge-driven requirements elicitation approach using the Multi-strategy, Task-adaptive Learning (MTL) method and the Strategic Rationale (SR) model. A case study shows that the proposed approach is able to identify users' new intentions, and is especially effective to capture alternatives of low-level tasks. We also demonstrate how these newly identified intentions can be fused to the existing domain knowledge network using the SR model, and harvest high-level wisdom, in terms of new requirements and design insights.
机译：人为因素已日益被认为是需求变化的主要驱动力之一。我们认为，需求引发（RE）流程应在很大程度上包含以人为中心的观点，并且这项工作着眼于随着时间的推移改变人类的意图和欲望。为了支持由于需求变化而引起的软件演化，已经提出了Situ框架来建模和检测人的意图，方法是通过在系统部署之前或之后监视环境上下文和人类行为上下文来推断人类的意图，从而推断人类的意图。 Situ上的早期工作报道说，该技术能够使用条件随机场方法以一定的准确性来推断用户的需求。但是，新意图的识别和新需求的启发仍然主要取决于人工分析。这项工作试图找到一种可计算的方法来识别用户的新意图，而这需要人工的有限帮助。我们讨论了实施数据-信息-知识-智慧（DIKW）概念的可行性，以弥合需求和与用户行为和环境相关的数据之间的差距，并提出了一种以情境为中心，知识驱动的需求启发方法。多策略，任务自适应学习（MTL）方法和战略依据（SR）模型。案例研究表明，所提出的方法能够识别用户的新意图，并且对于捕获低级任务的替代方案特别有效。我们还演示了如何使用SR模型将这些新发现的意图与现有领域知识网络融合，并在新的需求和设计见解方面收获高级智慧。

著录项

  • 作者

    Yang, Jingwei.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 71 p.
  • 总页数 71
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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