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| 檀超,张静宣,王铁鑫,岳涛.复杂软件系统的不确定性.软件学报,2021,32(7):22-0 |
| 复杂软件系统的不确定性 |
| Uncertainty-wise Software Engineering of Complex Systems:A Systematic Mapping Study |
| 投稿时间：2020-09-15 修订日期：2020-10-26 |
| DOI：10.13328/j.cnki.jos.006267 |
| 中文关键词: 不确定性 系统研究 信息物理系统 物联网 |
| 英文关键词:uncertainty systematic mapping study cyber-physical systems internet of things |
| 基金项目:国家自然科学基金（61872182） |
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| 摘要点击次数: 265 |
| 全文下载次数: 145 |
| 中文摘要: |
| 复杂软件系统（如信息物理系统CPS、物联网IoT以及自适应软件系统等）在其开发和运行过程中会遇到各种类型的不确定性问题.针对这些不确定性问题，研究人员开展了大量的研究工作，提出了一系列的方法，取得了诸多成果.然而，由于此类系统本身固有的复杂性和其内在与外在不确定性的共同作用，截止目前研究人员针对该研究领域仍然缺乏系统性和整体性的理解和分析.为了深入探究该领域的研究现状，本文采用系统研究的方法（Systematic Mapping Study）识别出142篇相关文献，并基于这些文献研究信息物理系统和物联网等系统生命周期中各个阶段和系统开发过程中产生的各种制品的不确定性及其处理方法.通过对相关文献进行分析，我们发现针对复杂系统的不确定性研究主要集中在其生命周期的设计定义、系统分析和运行等阶段.此外，本文首先将文献划分为三种不确定性类型，包括外部不确定性、内部不确定性和传感器不确定性，并将142篇相关论文关注的不确定性进行了分类.为了深入探究不确定性，我们将外部不确定性细分为环境不确定性、基础设施不确定性、用户行为不确定性以及经济属性不确定性，将内部不确定性细分为系统结构不确定性、内部交互不确定性、支持系统运行的技术不确定性以及处理系统运行技术的不确定性.针对复杂系统中的开发制品，我们提出了对应的不确定性类型，包括模型的不确定性、数据的不确定性和参数的不确定性等8类.针对复杂系统的不确定性问题，研究人员主要采用不确定性下的决策、不确定性推理和不确定性规约/建模等方法进行不确定性分析和处理.基于文献分析结果，本文进一步探讨和展望了该领域未来的研究趋势. |
| 英文摘要: |
| Complex software systems (e.g., Cyber-Physical Systems, Internet of Things, and adaptive software system) encounter various types of uncertainties in their different phases of development and operation. To handle these uncertainties, researchers have carried out a lot of research work, proposed a series of methods, and achieved considerable results. However, there is still a lack of systematic understanding of the current state of the art. Motivated by this observation, in this paper, we report a systematic mapping study of 142 primary studies collected by following a rigorous literature review methodology. The scope of the study is about investigating on how the literature deals with uncertainties appearing in various phases or artifacts produced during a development lifecycle of Cyber-Physical Systems and Internet of Things. Results show that uncertainties mainly appear in the phases of design definition, system analysis, and operation. Based on the 142 primary studies, we first defined and classified uncertainties into external uncertainty, internal uncertainty, and sensor uncertainty, and reported descriptive statistics in terms of this classification. In order to explore the uncertainty in depth, we subdivide external uncertainty into environmental uncertainty, infrastructure uncertainty, user behavior uncertainty, and economic attribute uncertainty, and internal uncertainty into uncertainty in system structure, internal interaction uncertainty, uncertainty in the technology supporting system operation, and uncertainty in the technology dealing with system operation. Furthermore, we presented another classification and descriptive statistics for those primary studies where uncertainties in eight different types of artifacts were discussed, including model uncertainty, data uncertainty, and parametric uncertainty. Results also show that researchers mainly focused on decision-making under uncertainty, uncertainty reasoning, and uncertainty specification/modeling when dealing with uncertainties. Based on the results, we commented on the future research trend in this area. |
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