Context:

Modern systems require programmers to develop code that dynamically adapts to different contexts, leading to the evolution of new context-oriented programming languages. These languages introduce new software-engineering challenges, such as: how to maintain the separation of concerns of the codebase? how to model the changing behaviors? how to verify the system behavior? and more.

Objective:

This paper introduces Context-Oriented Behavioral Programming (COBP) — a novel paradigm for developing context-aware systems, centered on natural and incremental specification of context-dependent behaviors. As the name suggests, we combine behavioral-programming (BP) — a scenario-based modeling paradigm — with context idioms that explicitly specify when scenarios are relevant and what information they need. The core idea is to connect the behavioral model with a data model that represents the context, allowing an intuitive connection between the models via update and select queries. Combining behavioral-programming with context-oriented programming brings the best of the two worlds, solving issues that arise when using each of the approaches in separation.

Methods:

We begin with providing abstract semantics for COBP and two implementations for the semantics, laying the foundations for applying reasoning algorithms to context-aware behavioral programs. Next, we exemplify the semantics with formal specifications of systems, including a variant of Conway’s Game of Life. Then, we provide two case studies of real-life context-aware systems (one in robotics and another in IoT) that were developed using this tool. Throughout the examples and case studies, we provide design patterns and a methodology for coping with the above challenges.

Results:

The case studies show that the proposed approach is applicable for developing real-life systems, and presents measurable advantages over the alternatives — behavioral programming alone and context-oriented programming alone.

Conclusion:

We present a paradigm allowing programmers and system engineers to capture complex context-dependent requirements and align their code with such requirements.

中文翻译：

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面向上下文的行为编程

内容：

现代系统要求程序员开发能动态适应不同上下文的代码，从而导致新的面向上下文的编程语言的发展。这些语言带来了新的软件工程挑战，例如：如何保持代码库关注点的分离？如何为变化的行为建模？如何验证系统行为？和更多。

目的：

本文介绍了面向上下文的行为编程（Context-Oriented Behavioral Programming，COBP）—一种用于开发上下文感知系统的新颖范例，其重点是上下文相关行为的自然和增量规范。顾名思义，我们将行为编程（BP）（一种基于场景的建模范例）与上下文习语相结合，后者明确指定了场景何时相关以及它们需要什么信息。核心思想是将行为模型与表示上下文的数据模型连接起来，从而允许通过更新和选择查询在模型之间进行直观的连接。将行为编程与面向上下文的编程相结合带来了两个方面的优点，解决了将每种方法分开使用时出现的问题。

方法：

我们从为COBP提供抽象语义以及为语义提供两种实现开始，为将推理算法应用于上下文感知行为程序奠定了基础。接下来，我们以系统的形式规范来举例说明语义，包括Conway的《生命游戏》的变体。然后，我们提供了使用此工具开发的两个实际情景感知系统的案例研究（一个是机器人技术，另一个是物联网）。在整个示例和案例研究中，我们提供了应对上述挑战的设计模式和方法。

结果：

案例研究表明，所提出的方法适用于开发现实生活中的系统，并且相对于其他选择（仅行为编程和单独的面向上下文的编程）具有明显的优势。

结论：

我们提供了一种范例，允许程序员和系统工程师捕获复杂的上下文相关需求，并使他们的代码与此类需求保持一致。