Models are used in both the Software Engineering (SE) and the Artificial Intelligence (AI) communities. In the former case, models of software, which may specify the software system architecture on different levels of abstraction could be used in various stages of the Software Development Life-Cycle (SDLC), from early conceptualization and design, to verification, implementation, testing and evolution. However, in the latter case, i.e., AI, models may provide smart capabilities, such as prediction and decision making support. For instance, in Machine Learning (ML), which is the most popular sub-discipline of AI at the present time, mathematical models may learn useful patterns in the observed data instances and can become capable of making better predictions or recommendations in the future. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach. We illustrate how software models can become capable of producing or dealing with data analytics and ML models. The main focus is on the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) use cases, where both ML and model-driven (model-based) SE play a key role. In particular, we implement the proposed approach in an open source prototype and validate it using two use cases from the IoT/CPS domain.
翻译:软件工程(SE)和人工智能(AI)社区都使用模型,在前一种情况下,软件模型可以用于软件开发生命-循环(SDLC)的各个阶段,从早期概念化和设计到核查、实施、测试和演变,但后一种情况下,即AI,模型可以提供智能能力,例如预测和决策支持。例如,机器学习(ML),这是目前大赦国际最受欢迎的次纪律,数学模型可以在观测到的数据实例中学习有用的模式,并能够在未来作出更好的预测或建议。这项工作的目标是通过将模型纳入上述社区,形成协同效应,并提出综合办法。在后一种情况下,例如AI,模型可以提供智能能力,例如预测和决策支持。例如,机器学习(MML),这是目前大赦国际最受欢迎的次纪律,数学模型模型模型模型模型模型模型可以在所观察到的数据实例中学习有用的模式或网络-物理系统(CPS),使用ML和模型原型系统,在SE-C领域中,我们使用一种主要案例,使用SE-CP,我们所拟议的一种特殊的原型系统。