Self-adaptive software

计算机科学 故障排除 适应(眼睛) 软件系统 自动化 风险分析(工程) 稳健性(进化) 软件质量 软件 软件开发 软件工程 可靠性工程 化学 光学 操作系统 生物化学 程序设计语言 医学 工程类 物理 基因 机械工程
作者
Mazeiar Salehie,Ladan Tahvildari
出处
期刊:ACM Transactions on Autonomous and Adaptive Systems [Association for Computing Machinery]
卷期号:4 (2): 1-42 被引量:1219
标识
DOI:10.1145/1516533.1516538
摘要

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-* properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how , what , when and where , towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.
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