基于反馈控制的软件适应性需求的识别与分析

适应性需求的识别与分析是开发自适应软件系统的基础,它将明确软件系统在运行过程中所可能面临的环境变化以及应对这些变化系统应采取的措施.但是,当前的适应性需求识别与分析方法一方面缺乏对环境的分析导致无法系统性地识别环境变化,另一方面也缺乏有效的框架来同时考虑如何应对已知的环境变化和未知的环境变化.针对这些问题,提出了基于反馈控制的适应性需求识别与分析方法.该方法将自适应软件与其作用的环境看作为一个自适应控制系统,将适应性需求的识别与分析转化为对该控制系统所应具有的反馈回路的识别与分析.通过将环境看作软件的控制对象,它不仅突出了软件的环境,同时还可以通过确定环境感知反馈回路和需求感知反馈回路来分别应对已知的环境变化和未知的环境变化.最后,用一个实例说明所提出方法的可行性.     

User-centered requirements engineering in health information systems: a study in the hemophilia field

The use of sophisticated information and communication technologies (ICTs) in the health care domain is a way to improve the quality of services. However, there are also hazards associated with the introduction of ICTs in this domain and a great number of projects have failed due to the lack of systematic consideration of human and other non-technology issues throughout the design or implementation process, particularly in the requirements engineering process. This paper presents the methodological approach followed in the design process of a web-based information system (WbIS) for managing the clinical information in hemophilia care, which integrates the values and practices of user-centered design (UCD) activities into the principles of software engineering, particularly in the phase of requirements engineering (RE). This process followed a paradigm that combines a grounded theory for data collection with an evolutionary design based on constant development and refinement of the generic domain model using three well-known methodological approaches: (a) object-oriented system analysis; (b) task analysis; and, (c) prototyping, in a triangulation work. This approach seems to be a good solution for the requirements engineering process in this particular case of the health care domain, since the inherent weaknesses of individual methods are reduced, and emergent requirements are easier to elicit. Moreover, the requirements triangulation matrix gives the opportunity to look across the results of all used methods and decide what requirements are critical for the system success.     

Progressive autonomy: a method for gradually introducing autonomy into space missions

This paper presents a method under development for introducing autonomy and agent-based software into future space- and ground-based missions while both reducing the risk of mission failures and gaining the confidence and support of mission management and principal investigators (PIs). This is being done using a mechanism to support dynamic agent-community evolution (e.g., agents adapting to community changes, agents joining a community, or agents leaving a community). This dynamic capability of agents is necessary to achieve what we call ‘‘progressive autonomy,’’ which will allow dynamic modification of satellite systems using agent migration to update and modify spacecraft capabilities on an as-needed basis, as well as allow the introduction of mission management and autonomy into existing missions. This paper will also address an application of progressive autonomy through spectral analysis automation (SAA). The long-term fully realized SAA system will be a multiagent system designed to provide automated support for two major functions: (1) the automatic remote filtering (onboard a spacecraft or robotic device) of spectral image data based on PI guidance, goals, and science agenda and (2) the packing and transmission of the selected spectral data to the PI for further processing. Additionally, the innovative multiagent-based infrastructure for the SAA can be generalized in such a way as to enable it to support the type of progressive autonomy that will be needed to support an adaptive and growing autonomous behavior for other spacecraft or robotic subsystems.     

Case-Based Reasoning adaptation of numerical representations of human organs by interpolation

Case-Based Reasoning (CBR) and interpolation tools can provide solutions to unknown problems by adapting solutions from other problems already solved. We propose a generic approach using an interpolation tool during the CBR-adaptation phase. The application EquiVox, which attempts to design three dimensional representations of human organs according to external measurements, was modelled. It follows the CBR-cycle with its adaptation tool based on Artificial Neural Networks and its performances are evaluated and discussed. The results show that this adaptation tool meets the requirements of radiation protection experts who use such prototypes and also what the limits are of such tools in CBR-adaptation. When adaptations are guided by experience grained through trial and error by experts, interpolation tools become well-suited methods for automatically and quickly providing adaptation strategies and knowledge through training phases.     

Modeling run-time adaptation at the system architecture level in dynamic service-oriented environments

Today, software systems are more and more executed in dynamic, virtualized environments. These environments host diverse applications of different parties, sharing the underlying resources. The goal of this resource sharing is to utilize resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system-specific, it is a challenge to abstract from system details, enabling the reuse of adaptation strategies. In this paper, we present S/T/A, a modeling language to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to multiple different realistic contexts (dynamic resource allocation, run-time adaptation planning, etc.). The results show how a holistic model-based approach can close the gap between complex manual adaptations and their autonomous execution.     

User-centered approach to adaptive interfaces

Both adaptive and adaptable user interfaces are intended to fit the needs of individual users and their tasks better. A problem with these interfaces is that users must have and use additional knowledge, either to understand the automatic, system-driven adaptations or to adapt the interface on their own. Beyond these two extreme approaches, an automatically self-adapting system and a user manually adapting the system, the approach introduced in this paper strives for a third option, in which users are able to tell the system how to adapt itself. Accordingly, methods include adaptive adaptation (i.e. modifying the adaptation strategies) and implicit adaptation (i.e. changing the underlying models). The applicability of these findings is illustrated in the context of an adaptive action prompting environment.     

A self-learning approach for validation of runtime adaptation in service-oriented systems

Ensuring that service-oriented systems can adapt quickly and effectively to changes in service quality, business needs and their runtime environment is an increasingly important research problem. However, while considerable research has focused on developing runtime adaptation frameworks for service-oriented systems, there has been little work on assessing how effective the adaptations are. Effective adaptation ensures the system remains relevant in a changing environment and is an accurate reflection of user expectations. One way to address the problem is through validation. Validation allows us to assess how well a recommended adaptation addresses the concerns for which the system is reconfigured and provides us with insights into the nature of problems for which different adaptations are suited. However, the dynamic nature of runtime adaptation and the changeable contexts in which service-oriented systems operate make it difficult to specify appropriate validation mechanisms in advance. This paper describes a novel consumer-centered approach that uses machine learning to continuously validate and refine runtime adaptation in service-oriented systems, through model-based clustering and deep learning.     

Towards a goal‐driven approach to action selection in self‐adaptive software

Self‐adaptive software is a closed‐loop system, since it continuously monitors its context (i.e. environment) and/or self (i.e. software entities) in order to adapt itself properly to changes. We believe that representing adaptation goals explicitly and tracing them at run‐time are helpful in decision making for adaptation. While goal‐driven models are used in requirements engineering, they have not been utilized systematically yet for run‐time adaptation. To address this research gap, this article focuses on the deciding process in self‐adaptive software, and proposes the Goal‐Action‐Attribute Model (GAAM). An action selection mechanism, based on cooperative decision making, is also proposed that uses GAAM to select the appropriate adaptation action(s). The emphasis is on building a light‐weight and scalable run‐time model which needs less design and tuning effort comparing with a typical rule‐based approach. The GAAM and action selection mechanism are evaluated using a set of experiments on a simulated multi‐tier enterprise application, and two sample ordinal and cardinal action preference lists. The evaluation is accomplished based on a systematic design of experiment and a detailed statistical analysis in order to investigate several research questions. The findings are promising, considering the obtained results, and other impacts of the approach on engineering self‐adaptive software. Although, one case study is not enough to generalize the findings, and the proposed mechanism does not always outperform a typical rule‐based approach, less effort, scalability, and flexibility of GAAM are remarkable. Copyright © 2011 John Wiley & Sons, Ltd.     

基于验证的自适应系统决策:一种模型驱动的方法

自适应系统需要根据运行时上下文和自身的变化进行其行为的调节.为实现自主调节,自适应系统必须被赋予运行时监测上下文和自身变化,分析需求满足程度的变化,以及推理得到自适应决策的能力.这种在线决策的行为在满足功能需求的同时,还需要保证系统满足特定的非功能需求,如可靠性和性能等.本文提出了一种基于验证的自适应系统优化决策方法,以保证非功能需求的满足.该方法在识别可调节目标以建模自适应机制的同时,将系统的目标模型映射为相应的行为模型,用标签转移系统表示;以可靠性需求为例,用标记目标模型规约任务的可靠性;然后将系统行为模型和可靠性规约整合为带可变状态的离散时间马尔可夫链,将候选自适应配置描述为不同可变状态间的组合;最终通过相关需求的在线验证,使系统找到关于某类上下文的最优决策配置.本文通过一个移动信息系统的案例展示了该方法的可行性和有效性.     

Toward combining autonomy and interactivity for social robots

The success of social robots in achieving natural coexistence with humans depends on both their level of autonomy and their interactive abilities. Although a lot of robotic architectures have been suggested and many researchers have focused on human–robot interaction, a robotic architecture that can effectively combine interactivity and autonomy is still unavailable. This paper contributes to the research efforts toward this architecture in the following ways. First a theoretical analysis is provided that leads to the notion of co-evolution between the agent and its environment and with other agents as the condition needed to combine both autonomy and interactivity. The analysis also shows that the basic competencies needed to achieve the required level of autonomy and the envisioned level of interactivity are similar but not the same. Secondly nine specific requirements are then formalized that should be achieved by the architecture. Thirdly a robotic architecture that tries to achieve those requirements by utilizing two main theoretical hypothesis and several insights from social science, developmental psychology and neuroscience is detailed. Lastly two experiments with a humanoid robot and a simulated agent are reported to show the potential of the proposed architecture.     

Rainbow: architecture-based self-adaptation with reusable infrastructure

While attractive in principle, architecture-based self-adaptation raises a number of research and engineering challenges. First, the ability to handle a wide variety of systems must be addressed. Second, the need to reduce costs in adding external control to a system must be addressed. Our rainbow framework attempts to address both problems. By adopting an architecture-based approach, it provides reusable infrastructure together with mechanisms for specializing that infrastructure to the needs of specific systems. The specialization mechanisms let the developer of self-adaptation capabilities choose what aspects of the system to model and monitor, what conditions should trigger adaptation, and how to adapt the system.     

Designing an adaptive computer‐aided ambulance dispatch system with Zanshin: an experience report

We have been witnessing growing interest in systems that can adapt their behavior to deal with deviations between their performance and their requirements at run‐time. Such adaptive systems usually need to support some form of a feedback loop that monitors the system's output for problems and carries out adaptation actions when necessary. Being an important feature, adaptivity needs to be considered in early stages of development. Therefore, adopting a requirements engineering perspective, we have proposed an approach and a framework (both called Zanshin) for the engineering of adaptive systems based on a feedback loop architecture. As part of our framework's evaluation, we have applied the Zanshin approach to the design of an adaptive computer‐aided ambulance dispatch system, whose requirements were based on a well‐known case study from the literature. In this paper, we report on the application of Zanshin for the design of an adaptive computer‐aided ambulance dispatch system, presenting elements of the design, as well as the results from simulations of run‐time scenarios. Copyright © 2013 John Wiley & Sons, Ltd.     

面向服务的织女星网格系统软件设计与评测

网格是开放的分布式系统,具有自治、动态和可扩展特性．在Internet环境下实现网格系统必须解决大规模资源共享与协同问题,也就是如何将Internet上无序的资源有序组织的问题．织女星网格系统软件针对现有网格平台软件存在的不足,借鉴传统计算机系统结构的方法来研究网格体系结构及其系统软件．面向网格应用的共性需求,提出了面向服务的层次化软件结构;引入网格资源空间的概念,实现了网格资源的虚拟化,屏蔽使用网格资源的技术细节．文章通过对网格关键问题的讨论,阐述了织女星网格系统软件的关键技术和设计思路,并通过对传统批作业的测试,分析了织女星网格系统软件的性能．     

Work domain analysis and sensors I: principles and simple example

In this paper we establish a foundation for understanding the instrumentation needs of complex dynamic systems if ecological interface design (EID)-based interfaces are to be robust in the face of instrumentation failures. EID-based interfaces often include configural displays which reveal the higher-order properties of complex systems. However, concerns have been expressed that such displays might be misleading when instrumentation is unreliable or unavailable. Rasmussen's abstraction hierarchy (AH) formalism can be extended to include representations of sensors near the functions or properties about which they provide information, resulting in what we call a “sensor-annotated abstraction hierarchy”. Sensor-annotated AHs help the analyst determine the impact of different instrumentation engineering policies on higher-order system information by showing how the data provided from individual sensors propagates within and across levels of abstraction in the AH. The use of sensor-annotated AHs with a configural display is illustrated with a simple water reservoir example. We argue that if EID is to be effectively employed in the design of interfaces for complex systems, then the information needs of the human operator need to be considered at the earliest stages of system development while instrumentation requirements are being formulated. In this way, Rasmussen's AH promotes a formative approach to instrumentation engineering.     

Trusted autonomy

We describe how agents are the right building blocks for constructing trustworthy systems. Robust software and trusted autonomy represent the future for agent technology and software engineering     

Agile requirements: opportunity or oxymoron?

We show that, in today's world, a requirement without agility is just as bad as agility without requirements. We have discovered a consensus that systems analysis and requirements definition, in particular, have fallen on hard times. Ten or 15 years ago we taught people structured analysis, information engineering, data modeling, and working with computer-aided software engineering tools to turn those requirements into running programs. Rather than understanding the user's business and information needs, some agile approaches attempt to give users what they say they want as quickly as possible. Every system of consequence needs good requirements. Without good requirements, the project risks increase dramatically. The better the requirements, the better people understand what they're trying to build (or acquire).     

Work domain analysis and sensors II: Pasteurizer II case study

In this paper we establish a foundation for understanding the instrumentation needs of complex dynamic systems if ecological interface design (EID)-based interfaces are to be robust in the face of instrumentation failures. EID-based interfaces often include configural displays which reveal the higher-order properties of complex systems. However, concerns have been expressed that such displays might be misleading when instrumentation is unreliable or unavailable. Rasmussen's abstraction hierarchy (AH) formalism can be extended to include representations of sensors near the functions or properties about which they provide information, resulting in what we call a “sensor-annotated abstraction hierarchy”. Sensor-annotated AHs help the analyst determine the impact of different instrumentation engineering policies on higher-order system information by showing how the data provided from individual sensors propagates within and across levels of abstraction in the AH. The use of sensor-annotated AHs with a configural display is illustrated with a simple water reservoir example. We argue that if EID is to be effectively employed in the design of interfaces for complex systems, then the information needs of the human operator need to be considered at the earliest stages of system development while instrumentation requirements are being formulated. In this way, Rasmussen's AH promotes a formative approach to instrumentation engineering.     

Multiagent robotics: toward energy autonomy

In this paper, we propose a novel trend in multiagent robotics: energy autonomy. A definition of energy autonomy is developed from an original concept, “potential energy,” that is under the constraints of remaining energy capacity and the relative distance among robotic agents. Toward energy autonomy, we initially present a simulation of a multiagent robotic system in which each robot is capable of exchanging energy cells with other robots. Our simulation points out that: (1) each robot is able not only to act as an autonomous agent, but also to interact with others to be beyond the individual capabilities; (2) in order to adapt to changes in the environment, each robot is situated as an adaptive agent in a network of neighboring robots, which leads to a state of energy autonomy. Finally, based on the results of the simulation, we adjust the rules for our real multirobot system. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Disciplined autonomy: How business analytics complements customer involvement for digital innovation

The rise of big data and the fluid boundaries of digital products are driving companies to use business analytics (BA) to power their customer involvement. The complementarity view offers unique competence to generate value from BA because capability complementarity is less likely to be replicated or imitated. Unlike prior studies on BA-enabled value realization, our research investigates the interactions of BA and customer involvement capabilities using the complementarity view. We tested our model using data collected from 317 IT companies in China. Our results suggest that BA value realization requires both a top-down mechanism in which BA skills provide global guidance for alignment with a company’s goals and a bottom-up mechanism in which BA culture empowers local autonomy for adaptation to ever-changing needs. Our BA-complemented mechanisms provide research and practice with a way to concurrently use BA and customer involvement capabilities to address the duality of digital innovation. We further suggest that BA skills are necessary but insufficient for digital innovation because BA culture demonstrates a stronger effect in complementing organizations’ existing capabilities than BA skills do.