情景感知的软件需求获取与建模技术综述

情境感知的移动应用能够适应用户当前状态,因此这类应用越来越受到青睐。但由于上下文情景的维度是无数的,这使得获取和建模情境感知系统的情景需求成为一大挑战。本文通过文献综述的方法,旨在:(1)发现有哪些情境感知的需求获取与建模方法;(2)评估这些方法的技术转移成熟度。通过对所选择的61篇相关文献,进行数据抽取和分析综合,本文总结并识别出11种需求获取的方法、10种需求建模的方法以及它们的技术转移现状。结果显示:(1)最受欢迎的情景感知需求获取方法是用例和场景,而最常用的情境感知需求建模方法则是面向目标的方法;(2)在大多数相关文献中的需求获取与建模技术的技术成熟度都不高,不利于其面向工业界进行技术转移。     

ChatModeler：基于大语言模型的人机协作迭代式需求获取和建模方法

需求获取和建模是需求工程中的关键步骤,影响后续系统设计与实现.传统的需求获取和建模方法通常由需求提供者、需求分析师等多类干系人共同协作、反复迭代完成,需要耗费大量的人力.如何减轻需求提供者与需求分析师的负担、提高获取和建模的效率有着重要意义.现有工作中有的使用知识库来提供更多知识,以辅助获取或者建模,有的利用自然语言处理等技术对获取或者建模过程进行自动化,但是它们并没有减轻需求提供者的负担.利用大语言模型（large language models,LLMs）的生成能力,提供了一种人机协作的迭代式需求获取和建模框架ChatModeler.具体来说,根据真实世界中需求团队的分工及协作关系,将部分需求提供者、需求分析师等角色的工作由大语言模型承担,而需求提供者只需要进行确认.为大语言模型扮演的各种角色进行了提示词设计,该提示词会随需求的元模型而变化.ChatModeler在7个需求案例上与3种需求模型的自动建模方法进行了14组对比实验,证明了ChatModeler在降低需求提供者的负担和生成高质量需求模型2个方面上的优越性.     

物联网情景感知技术研究

与互联网不同,物联网(Internet of Things, IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了“物物互连”的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将通布人们周围的环境,情景感知技术应运而生。情景感知获得传感器采集的情景信息以后,对信息进行智能处理,自主地为用户提供服务。物联网具有海量信息的特性,传统的情景信息处理方法已不再适用。对物联网情景感知技术进行了详细的介绍,首先给出情景和情景感知的概念及其研究发展和应用。然后,结合物联网特性,以情景感知流程为主线,探讨了信息获取、建模和智能处理等内容。最后,系统结构是情景感知的关键,因此对现有的系统结构进行了分析和对比,结合物联网环境论述了当前情景感知系统的不足之处,并给出了情景感知系统的参考结构。     

智能需求获取与建模研究综述

需求获取和建模是指从需求文本或记录中获取显式和隐式的需求,并通过表格化、图形化、形式化等方法构建相应模型的过程,是软件开发过程中极为关键的一步,为后续系统设计与实现铺平道路,提高软件开发效率和质量,提升软件系统稳定性和可行性.研究者们在需求获取与建模方面获得了一系列研究成果,根据其关注阶段不同,可以将它们分为需求知识提...     

情景感知驱动的移动对象多模式轨迹预测技术综述

如何利用多源异构时空数据进行准确的轨迹预测并且反映移动对象的移动特性是轨迹预测领域的核心问题.现有的大多数轨迹预测方法是长序列轨迹模式预测模型,根据历史轨迹的特点进行预测,或将当前移动对象的轨迹位置放入时空语义场景根据历史移动对象轨迹预测位置.综述当前常用的轨迹预测模型和算法,涉及不同的研究领域.首先,阐述了多模式轨迹预测的主流工作,轨迹预测的基本模型类;其次,对不同类的预测模型进行总结,包括数学统计类、机器学习类、滤波算法,以及上述领域具有代表性的算法;再次,对情景感知技术进行了介绍,描述了不同领域的学者对情景感知的定义,阐述了情景感知技术所包含的关键技术点,诸如情景感知计算、情景获取和情景推理的不同类模型,分析了情景感知的不同分类、过滤、存储和融合以及它们的实现方法等.详细介绍了情景感知驱动的轨迹预测模型技术路线及各阶段任务的工作原理.给出了情景感知技术在真实场景中的应用,包括位置推荐,兴趣点推荐等,通过与传统算法对比,分析情景感知技术在此类应用中的优劣.详细介绍了情景感知结合LSTM (long short-term memory)技术应用于行人轨迹预测领域的新方法.最后,总结了...     

多视点元模型间需求追踪性方法

以全面而统筹的眼光解决问题,则必须从局部分析不同的侧面,面向多视点的需求工程即为该理论的一个应用.多视点需求工程越来越受到重视,但是各视点间却缺少统一,这不仅不利于获取完整的系统需求,还导致需求变更困难等问题.为了解决以上问题,首先提出一个新的多视点建模过程框架,合理的建模框架有利于获取完整的系统需求.其次在多视点建模过程框架下建立追踪元模型,阐明系统需求在多视点元模型间的平稳过渡.最后结合需求追踪矩阵方法在追踪元模型间建立起追踪关系,进而通过计算变化追踪矩阵在需求变更时可以追踪到相关元素并进行更改,解决需求变更困难的问题.     

基于情景的主动知识需求建模研究

目前企业知识管理系统中,初始化需求建模耗费大量手工人力以及知识推送触发条件过于单一,针对这两个问题,论文提出了一种将知识融合情景的方式,将知识需求与特定情景相结合.首先分析了情景维度、情景空间、情景树和情景节点模型等概念,然后给出了本体知识源提取和情景需求初始化算法.最后以某航空研究所的实例验证了该方式的可行性.     

一种支持多视点方法的自动需求建模

需求建模是需求工程的关键一环,以基于本体的需求获取和分析为背景,尝试将多视点方法应用到基于本体的需求建模过程中,从多个视点出发在软件工具的协助下对需求描述进行自动需求建模,从而提高需求模型的完整性.同时给出一种视点描述方法,用于对自动建模过程中使用的视点进行形式化定义.     

基于多维度服务情景的人的舒适需求建模

服务机器人需要针对不同服务情景下的任务需求切换对应的服务行为.为了提供一种能够体系化地涵盖服务机器人的全部服务情景的共性研究方法,提出了多维度服务情景的概念,通过机器人所处工作环境的大小、环境中的参与者的多少和参与者的动态情况等多维度因素来量化与理解服务机器人所面对的全部服务情景.基于多维度服务情景的量化与理解,设计了一种基于紧张空间的人的舒适需求建模方法.该模型描述了每种服务情景下人的紧张空间在工作环境的大小、环境中的参与者的数量和参与者的动态情况的影响下产生的不规则形变.最后,通过设计的仿真系统研究了多维度服务情景对应的人的紧张空间.多维度服务情景不仅可以覆盖全部服务情景,而且可以根据研究的需要调整所选取的因素.相较于枚举式的研究方法,多维度服务情景的概念更适用于服务机器人的研究.     

情景感知在移动计算中的应用研究

随着移动商务的发展,情景感知在交互移动计算系统中的应用越来越广泛。为了把这一概念应用到设计的动态环境中,有必要了解什么是情景感知、情景感知的分类等概念。学者们针对情景感知概念的讨论和验证从未停止过,分析情景感知在移动计算中的应用是当前一个重要的研究方向。本文对情景感知概念及移动计算数据获取的方法提出了一些建议。     

一种面向主动服务的情境觉察计算方法

We focus on modeling and computing of aware context with uncertainty for making dynamic decision during seamless mobile service. We re-examine formalism of random set, which is not finite-set statistics (FISST), argue the limitations of the direct numerical approaches, give new modeling mode based on random sets theory (RST) for aware context, and propose our computing approach of modeled aware context. In addition, we extend classic D-S evidence theory after considering reliability, time efficiency, relativity of context, and compare these two kinds of relative computing methods for uncertain context. By comparing, the validity of new context-aware computing approach based on improved random set theory (IRST) or extended D-S evidence theory (EDS) for proactive service has been tested.     

一种面向主动服务的情境觉察计算方法

We focus on modeling and computing of aware con- text with uncertainty for making dynamic decision during seam- less mobile service.We re-examine formalism of random set, which is not finite-set statistics (FISST),argue the limitations of the direct numerical approaches,give new modeling mode based on random sets theory (RST) for aware context,and propose our computing approach of modeled aware context.In addition, we extend classic D-S evidence theory after considering relia- bility,time efficiency,relativity of context,and compare these two kinds of relative computing methods for uncertain context. By comparing,the validity of new context-aware computing ap- proach based on improved random set theory (IRST) or extended D-S evidence theory (EDS) for proactive service has been tested.     

个性化领域知识支持的用户主导需求获取方法

应用软件的需求获取与应用领域的特征密切相关,用户的参与日益受到重视,然而用户对系统的认识通常模糊且不完整,且对于多用户系统,需要拥有局部需求的多用户进行协同,才能得到完整、一致的需求.提出一种用户主导的需求获取方法,根据用户特征及其上下文环境,为用户进行需求定义提供个性化的领域知识支持,包括领域需求资产推荐和对多用户协同需求获取的建议.应用实例说明了该方法的可行性,且对于提高用户参与程度、改进领域知识重用效果,从而最终提高需求获取的质量具有积极意义.     

Improving requirements elicitation: an empirical investigation of procedural prompts

Abstract.  Information system success is dependent upon the effectiveness of requirements elicitation. Requirements elicitation is subject to numerous challenges, however, including cognitive limitations of analysts. In the present study, we test prompts designed to overcome the cognitive limitations that cause analysts to gather inadequate and inaccurate requirements. The study's results indicate that providing analysts with procedural prompts to aid their requirements elicitation can result in the acquisition of additional meaningful requirements. In addition, the procedural prompts designed to mitigate cognitive challenges were significantly more effective than simple interrogatory prompts. Theoretical implications for the elicitation of requirements and practical implications for the training and practice of systems analysts are discussed.     

Ubiquitous enterprise service adaptations based on contextual user behavior

Recent advances in mobile technologies and infrastructures have created the demand for ubiquitous access to enterprise services from mobile handheld devices. Further, with the invention of new interaction devices, the context in which the services are being used becomes an integral part of the activity carried out with the system. Traditional human–computer interface (HCI) theories are now inadequate for developing these context-aware applications, as we believe that the notion of context should be extended to different categories: computing contexts, user contexts, and physical contexts for ubiquitous computing. This demands a new paradigm for system requirements elicitation and design in order to make good use of such extended context information captured from mobile user behavior. Instead of redesigning or adapting existing enterprise services in an ad hoc manner, we introduce a methodology for the elicitation of context-aware adaptation requirements and the matching of context-awareness features to the target context by capability matching. For the implementation of such adaptations, we propose the use of three tiers of views: user interface views, data views, and process views. This approach centers on a novel notion of process views to ubiquitous service adaptation, where mobile users may execute a more concise version or modified procedure of the original process according to their behavior under different contexts. The process view also serves as the key mechanism for integrating user interface views and data views. Based on this model, we analyze the design and implementation issues of some common ubiquitous access situations and show how to adapt them systematically into a context-aware application by considering the requirements of a ubiquitous enterprise information system.

Incorporating context into recommender systems: an empirical comparison of context-based approaches

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.     

Acquiring Customers' Requirementsin Electronic Commerce

A key role for Artificial Intelligencetechnology in electronic commerce is in findingproducts and services that meet a user'srequirements. This may be implemented as athree-stage process of requirementselicitation, product search, and finallyproduct presentation. Alternatively the searchof the product space may happen in tandem withthe requirements elicitation process. It isalso possible to use product presentation as amechanism to focus and give context torequirements elicitation. A variety ofdifferent approaches to this issue of matchingproducts to requirements have been explored inAI research. Thus, while the focus in thispaper is on different approaches torequirements elicitation, these related issuesof product search and product presentation arealso discussed in detail. The paper concludesby placing the other papers in this specialissue in the context of this structuredoverview.     

A JESS-enabled context elicitation system for providing context-aware Web services

Providing context-aware Web services is an adaptive process of delivering contextually matched Web services to meet service requesters’ needs. We define the term “context” from two perspectives: one from service requesters; and the other from Web services. From the former perspective, context is defined as the surrounding environment affecting requesters’ services discovery and access, such as requesters’ preferences, locations, activities, and accessible network and devices. From the latter perspective, context is defined as the surrounding environment affecting Web services delivery and execution, such as networks and protocols for service binding, devices and platforms for service execution, and so on. This paper presents a Java Expert System Shell (JESS)-enabled context elicitation system featuring an ontology-based context model that formally describes and acquires contextual information pertaining to service requesters and Web services. Based on the context elicitation system, we present a context-aware services-oriented architecture for providing context-aware Web service request, publication, and discovery. Implementation details of the context elicitation system and the evaluation results of context-aware service provision are also reported.