摘要:软件系统自适应提供了应对动态变化的环境和不确定的需求的技术方案.在已有的软件系统自适应性的相关研究中，有一类工作将软件系统自适应性转换为回归、分类、聚类、决策等问题，并利用强化学习、神经网络/深度学习、贝叶斯决策理论和概率图模型、规则学习等机器学习算法进行问题建模与求解，并以此构造软件系统自适应机制.将其称为机器学习赋能的软件自适应性.通过系统化的文献调研，综述了该研究方向的前沿工作：首先介绍基本概念，然后分别从机器学习、软件自适应的视角对当前工作进行分类；按机器学习算法、软件对外交互、软件对内控制、自适应过程、自适应任务和学习能力的对应关系等方面进行分析；最后对未来的研究进行展望.



Abstract:Software self-adaptation (SSA) provides a way of dealing with dynamic environment and uncertain requirement. There are existing works that transform the dynamic and uncertainty concerned by SSA into regression, classification, cluster, or decision problems; and apply machine learning algorithms, including reinforcement learning, neural network/deep learning, Bayesian decision theory and probabilistic graphical model, rule learning, to problem formulation and solving. These kinds of work are called as “machine learning enabled SSA” in this study. The survey is conducted on the state-of-the-art research about machine learning enabled SSA by firstly explaining the related concepts of SSA and machine learning; and then proposing a taxonomy based on current work from SSA perspective and machine learning perspective respectively; analyzing the machine learning algorithms, software external interaction, software internal control, adaptation process, the relationship between SSA task and learning ability under this taxonomy; as well as identifying finally deficiency of current work and highlighting future research trends.



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